Information processing device, information processing method, and program

Abstract

[Problem] It has not been possible to simulate the mechanism of memory in the brain. [Solution] The mechanism of memory in the brain can be simulated by an information processing device 1 comprising: a hippocampus unit 114 that stores one or more firing patterns; a transmission unit 131 that is connected to nodes identified by one or more node identifiers of one or more firing nodes corresponding respectively to the one or more firing patterns in the hippocampus unit 114, and determines which node to fire; a positive amygdala unit 133 that obtains a degree of positivity using the number of firing nodes of the positive amygdala; a negative amygdala unit 134 that obtains a degree of negativity using the number of firing nodes of the negative amygdala; and a corpus callosum unit 132 that obtains a firing pattern in which the firing state satisfies a steady-state condition, obtains a signal intensity using the degree of positivity and the degree of negativity, and accumulates long-term memory information having the firing pattern and the signal intensity in a cortex unit 113.

Description

Information processing device, information processing method, and program

[0001] The present invention relates to an information processing device or the like that simulates the mechanism of memory in the brain.

[0002] Conventionally, there has been a visual simulation device that is capable of simulating how things appear (see Patent Document 1).

[0003] JP 2019-88383 A

[0004] However, conventional techniques have not been able to simulate the mechanism of memory in the brain, and in particular, have not been able to simulate the mechanism of long-term memory in the brain.

[0005] The information processing device of the first invention includes an NN storage unit that stores neural network information having two or more pieces of node information each having a node identifier and one or more pieces of edge information each having an edge identifier and specifying connections between the nodes, the neural network information corresponding to an area identifier that identifies one of two or more areas including positive and negative areas, a hippocampus unit that stores one or more firing patterns that are information about one or more firing nodes, and a cortex unit that stores one or more pieces of long-term memory information each having a firing pattern and a signal strength, and acquires one or more firing patterns from the one or more firing patterns stored in the hippocampus unit, acquires node identifiers for one or more firing nodes included in each of the one or more firing patterns, and determines node identifiers for nodes that are connected by edges to the nodes identified by the one or more node identifiers and that will fire based on a firing condition. a positive amygdala unit that acquires the number of fired nodes among one or more nodes corresponding to an area identifier indicating a positive amygdala and acquires a positivity level using the number of nodes; a negative amygdala unit that acquires the number of fired nodes among one or more nodes corresponding to an area identifier indicating a negative amygdala and acquires a negativity level using the number of nodes; and a corpus callosum unit that determines whether the firing state in the neural network satisfies a steady-state condition, acquires a firing pattern when it is determined that the steady-state condition is satisfied, acquires the positivity level acquired by the positive amygdala unit and the negativity level acquired by the negativity unit, acquires signal strength using information on one or more of the positivity and negativity levels, and stores long-term memory information having the firing pattern and signal strength in a cortex unit.

[0006] This configuration makes it possible to simulate the mechanism of memory in the brain.

[0007] In addition, the information processing device of the second invention is an information processing device in which, compared to the first invention, the corpus call osm unit determines whether the signal strength satisfies the storage condition, and if it determines that the storage condition is met, stores the long-term memory information in the cortex unit.

[0008] This configuration makes it possible to simulate the mechanism of memory in the brain.

[0009] Furthermore, in the information processing device of the third invention, compared to the first or second invention, the steady-state condition is that variation information regarding the variation in the number of firings over time is equal to or less than a threshold, the corpus callOSM unit obtains the number of firing nodes at each of two or more time points in the neural network, obtains variation information using the number of firing nodes, determines whether the variation information satisfies the steady-state condition, obtains a firing pattern when it is determined that the steady-state condition is satisfied, and stores long-term memory information having the firing pattern and signal intensity in the cortex unit.

[0010] This configuration makes it possible to simulate the mechanism of memory in the brain.

[0011] Furthermore, in the information processing device of the fourth invention, compared to the first or second invention, the steady-state condition is that a predetermined time or more has elapsed since the transmission unit started the firing transmission process using one or more firing patterns of the hippocampus unit, and the corpus callosum unit determines whether the steady-state condition is met by the fact that a predetermined time or more has elapsed since the transmission unit started the firing transmission process using one or more firing patterns of the hippocampus unit, acquires the firing pattern when it is determined that the steady-state condition is met, and stores long-term memory information having the firing pattern and signal intensity in the cortex unit.

[0012] This configuration makes it possible to simulate the mechanism of memory in the brain.

[0013] In addition, the information processing device of the fifth invention is an information processing device in which, compared to any one of the first to fourth inventions, the cortex unit stores two or more pieces of long-term memory information, and the corpus call OSAM unit sorts the two or more pieces of long-term memory information in the cortex unit using signal strength as a key.

[0014] This configuration makes it possible to simulate the mechanism of memory in the brain.

[0015] In addition, the information processing device of the sixth invention is an information processing device in accordance with any one of the first to fifth inventions, further comprising a dreaming unit that acquires an image corresponding to the firing pattern accumulated by the corpus call analysis unit and outputs the image.

[0016] This configuration allows for the simulation of dreams in the brain.

[0017] Furthermore, the information processing device of the seventh invention is an information processing device that, compared to any one of the first to sixth inventions, further includes a reception unit that receives a start instruction, and when the reception unit receives the start instruction, the transmission unit performs a firing transmission process using partial neural network information having two or more node information and one or more edge information of areas corresponding to area identifiers that identify one or more areas that meet the sleep conditions.

[0018] This configuration makes it possible to simulate the mechanism of memory in the brain.

[0019] The information processing device according to the present invention can simulate the mechanism of memory in the brain.

[0020] A block diagram of the information processing device 1 according to the first embodiment. A flowchart illustrating an example of the operation of the information processing device 1. A flowchart illustrating an example of the simultaneous transmission process. A flowchart illustrating an example of the firing determination process. A flowchart illustrating an example of the firing process. A flowchart illustrating an example of the long-term storage process. A flowchart illustrating an example of the image acquisition process. An overview of the computer system. A block diagram of the computer system.

[0021] Hereinafter, embodiments of an information processing device and the like will be described with reference to the drawings. Note that components with the same reference numerals in the embodiments perform similar operations, and therefore repeated description may be omitted.

[0022] (Embodiment 1) In this embodiment, an information processing device that performs long-term memory processing will be described. That is, in this embodiment, one or more firing patterns are stored, and firings are propagated in a neural network (hereinafter referred to as "NN") from one or more firing nodes included in the firing patterns acquired from the one or more firing patterns. When Corpus Callosum determines that a steady-state condition is satisfied, signal strength based on the number of firings of Amygdala is acquired, and long-term memory information including the signal strength and the firing pattern when the steady-state condition is satisfied is stored in a cortex. Note that signal strength is information indicating the strength of a signal. A firing pattern is information that identifies one or more firing nodes. A firing pattern typically includes one or more node identifiers. A firing node is a node that is firing. The node identifier included in the firing pattern is the node identifier of the firing node.

[0023] In this embodiment, an information processing device will be described that stores long-term memory information in the cortex only when the signal strength satisfies the storage condition.

[0024] Furthermore, in this embodiment, an information processing device that performs dream processing will be described. That is, in this embodiment, an information processing device that performs processing to acquire and output an image corresponding to an ignition pattern to be stored will be described.

[0025] In this specification, information X being associated with information Y means that information Y can be obtained from information X, or information X can be obtained from information Y, and the method of association is not important. Information X and information Y may be linked, may exist in the same buffer, information X may be included in information Y, or information Y may be included in information X, etc.

[0026] Furthermore, in this specification, selecting or determining information Z means obtaining information Z, obtaining a pointer to information Z, obtaining the ID of information Z, setting a flag on information Z, etc., and it is sufficient if information Z can be accessed.

[0027] 1 is a block diagram of an information processing device 1 according to the present embodiment. The information processing device 1 includes a storage unit 11, a reception unit 12, and a processing unit 13. The storage unit 11 includes an NN storage unit 111, an ignition condition storage unit 112, a cortex unit 113, a hypocampus unit 114, and an image storage unit 115. The processing unit 13 includes a transmission unit 131, a corpus callosum unit 132, a positive amygdala unit 133, a negative amygdala unit 134, and a daydream unit 135.

[0028] The information processing device 1 is, for example, a terminal, but may also be a server. When the information processing device 1 is a terminal, the information processing device 1 is, for example, a personal computer, a tablet terminal, or a smartphone, but may also be a robot, etc. When the information processing device 1 is a robot, the robot may be a human-shaped robot. When the information processing device 1 is a server, the information processing device 1 is, for example, a cloud server or an ASP server, but this does not matter.

[0029] Various types of information are stored in the storage unit 11 that constitutes the information processing device 1. The various types of information include, for example, neural network information (hereinafter referred to as "NN information"), firing information, various conditions (described later), and one or more images. The various conditions include, for example, a steady state condition, an accumulation condition, a sleep condition, and a firing condition. It goes without saying that the various conditions may be embedded in the program.

[0030] The firing information is information about the result of firing. The firing information is information about the node that fired. Preferably, the firing information is information about the node that is firing. The firing information has a node identifier that identifies the node that fired. The firing information may have timer information that indicates the time of firing. The timer information may be information that indicates relative time or time information that indicates absolute time. The timer information may be considered to be a node attribute value. The firing information may have a node identifier of the node from which the firing is transmitted. The firing information may have an edge identifier of the edge from which the firing is transmitted. It is preferable that the firing information in the storage unit 11 is automatically deleted by the processing unit 13 after a certain period of time has elapsed since storage. It is preferable that the firing information has transmission information. The transmission information is information transmitted between nodes via edges. The transmission information is one or more pieces of characteristic information, signal strength, or one or more pieces of characteristic information and signal strength.

[0031] The NN storage unit 111 stores NN information. The NN information is information that simulates a neural network in the brain. The NN information is information that specifies a NN having two or more nodes and one or more edges connecting the nodes. The NN information has two or more node information and one or more edge information. It is preferable that each node of the NN information is connected to one or more other nodes by an edge. In other words, it is preferable that the NN here does not have an input layer or an output layer.

[0032] Node information is information about the nodes that make up the NN. Nodes are usually connected by two or more edges. A node is connected to an edge, and there may be two or more other nodes that transmit information to that node. A node is connected to an edge, and there may be two or more other nodes to which that node transmits information. Node information has a node identifier. Node information has, for example, one or two or more node attribute values. Each of the two or more pieces of node information that make up the NN information corresponds to an area identifier. The area identifier is information that identifies an area in the brain. The area identifier is, for example, an area name or an area ID. The area identifier is, for example, "positivity," "negativity," or "image processing area." It is preferable that the area identifier be information that identifies one of two or more areas including "positivity" or "negativity." One or more pieces of node information among two or more pieces of node information may be associated with two area identifiers: "positive" and "negative." The "positive" area is the area of ​​the node that fires in positive cases. The "negative" area is the area of ​​the node that fires in negative cases. Note that the NN information may have node information that does not correspond to an area identifier. The node information of a node may have the node identifier of another node that is connected to the node by an edge and transmits information to the node. The node information of a node may have the node identifier of another node that is connected to the node by an edge and transmits information from the node. The "image processing area" is the area of ​​the node that processes information that enters through the eyes.

[0033] A node identifier is information that identifies a node that constitutes a network. A node identifier is, for example, a node ID or node name. A node may also be called a soma. A node identifier may also be called a soma identifier.

[0034] The node attribute values ​​include, for example, node position information, firing conditions, firing probability information, number of times information, and wait time. The area identifier may be one of the node attribute values.

[0035] The node position information is information about the position of a node, and is, for example, three-dimensional coordinate values ​​(x, y, z), two-dimensional coordinate values ​​(x, y), or four-dimensional quaternion (x, y, x, w).

[0036] The firing condition is a condition under which a node fires. The firing condition may be considered to be the firing condition stored in the firing condition storage unit 112. The firing condition will be described in detail later.

[0037] The firing probability information is information about the probability of firing. The firing probability information may be the firing probability itself, or a value obtained by converting the firing probability using a function or the like. It is preferable that the firing probability information is referenced, and the node may or may not fire at the probability specified by the firing probability information, even if the transmission information is the same.

[0038] The number of times information is information based on the number of times a node has fired. The number of times information is, for example, the number of times a node fired or the firing rate. The number of times a node fired is, for example, the number of times a node fired in a specific period of time, or the cumulative number of times a node fired. The firing rate is, for example, the rate of a node fired in a specific period of time, or the rate of a node fired over the entire period of time. The firing rate of a node is the "number of times a node fired / number of times a signal was transmitted to the node."

[0039] The wait time is the waiting time from when a node receives transmission information until it transmits the information to the next node. The wait time may be common to two or more nodes. The wait time may also be common to all nodes.

[0040] Edge information is information about the edges that make up a neural network. Edge information is information that identifies edges that connect nodes. Edge information typically includes an edge identifier. Edge identifiers are information that identifies edges. The edge identifier is, for example, the edge ID or the edge name. Edge information is, for example, the node identifier of each of the two nodes connected by the edge. Edge information is, for example, the node identifier of one connecting node. When edge information includes only one node identifier, the edge is in the growth process before connecting the two nodes. Edge information typically includes one or more edge attribute values. Edge attribute values ​​include, for example, weight and edge position information. Weight can be considered information based on the thickness of the edge. The larger the weight, the more easily information is transmitted via the edge. The larger the edge weight, the more likely the node beyond the edge will typically fire. Edge position information is edge position information. Edge position information is information that identifies the position of the endpoint of the edge. When edge information includes two node identifiers, for example, edge position information is not included. When edge information has two node identifiers, the edge specified by the edge information connects two nodes.

[0041] The edge information includes, for example, dendrites information and AXON information. In this case, the edge includes dendrites and AXON. Note that the edge may be considered to be a single line or two or more branched lines. The edge may also be called a synapse.

[0042] Dendrites information is information about DENDRITES. DENDRITES are also called dendrites and are parts of nerve cells. They are multiple projections that branch out from the cell body like the branches of a tree in order for nerve cells to receive external stimuli and information sent from the axons (AXONs) of other nerve cells. DENDRITES are elements that make up edges in this case. DENDRITES information includes a DENDRITES identifier and DENDRITES position information.

[0043] The DENDRITES identifier is information for identifying DENDRITES, such as the ID or name of DENDRITES.

[0044] DENDRITES position information is information about the position of DENDRITES. DENDRITES position information is information that specifies the position of DENDRITES, and is, for example, one or more three-dimensional coordinate values ​​(x, y, z) or one or more two-dimensional coordinate values ​​(x, y). When the DENDRITES position information has two or more coordinate values, DENDRITES is a line connecting the points of the two or more coordinate values.

[0045] It is also preferable that the dendrites information includes information on the amount of energy held by the dendrites, which indicates the amount of energy held by the dendrites.It is also preferable that the dendrites information includes information on the amount of energy required to transmit information using the dendrites.

[0046] The axon information is information about an axon. An axon, also known as an axon, is a protruding structure extending from a cell body and responsible for signal output in a nerve cell. Here, an axon is an element that constitutes an edge. The axon information includes an axon identifier and axon position information.

[0047] The AXON identifier is information for identifying the AXON, such as the ID or name of the AXON.

[0048] AXON position information is information about the position of the AXON. The AXON position information is information that specifies the position of the AXON, and is, for example, one or more three-dimensional coordinate values ​​(x, y, z) or one or more two-dimensional coordinate values ​​(x, y). When the AXON position information has two or more coordinate values, the AXON is a line connecting the points of the two or more coordinate values.

[0049] Preferably, the AXON information includes information on the amount of energy stored in the AXON, and information on the amount of energy required to transmit information using the AXON.

[0050] Note that dendrites and AXON may be branched. When dendrites and AXON are branched, the position information of each may be expressed by three or more coordinate values. However, the method of expressing dendrite position information and AXON position information does not matter.

[0051] The firing condition storage unit 112 stores one or more firing conditions. The firing condition corresponds to the node information. The firing condition may be included in the node information. The firing condition is a condition for a node to fire. The firing condition is information related to transmission information or frequency information.

[0052] The firing condition storage unit 112 stores firing conditions corresponding to one or more pieces of node information among the two or more pieces of node information. Note that node information not corresponding to a firing condition may or may not exist. Node information not corresponding to a firing condition is information about a node that will always fire when a signal is transmitted.

[0053] The firing condition corresponding to a node may be common to all nodes. Furthermore, the firing condition may be "empty". If the firing condition of a node connected by an edge is "empty", the node connected by the edge may always fire. It is not necessary for all nodes to have a corresponding firing condition. In such a case, the firing condition storage unit 112 is not necessary.

[0054] The firing condition is, for example, a condition related to signal strength. In this case, the firing condition is called an intensity condition. The firing condition is, for example, that the signal strength is equal to or greater than a threshold. The signal strength here is the signal strength associated with the candidate node to be fired. The signal strength is usually the transmitted signal strength, but it may also be the signal strength reduced by the transmission.

[0055] The firing condition is, for example, a condition related to one or more feature information of the sound information. In such a case, the firing condition is called a sound feature condition. The sound feature condition is, for example, a condition related to a specific frequency of the sound information. In such a case, the firing condition is called a frequency condition. The frequency condition is, for example, information specifying a frequency range (e.g., "first threshold <= frequency <= second threshold"), information specifying an upper frequency limit (e.g., "frequency <= second threshold"), or information specifying a lower frequency limit (e.g., "first threshold <= frequency"). Examples of frequency conditions are "<information identifier> frequency = Fa <condition> information amount > = 100", "<information identifier> Fa <= frequency <Fb <condition> information amount > = 100", or "<information identifier> R <condition> information amount > = 150 & <information identifier> frequency = Fa <condition> information amount > = 180". Note that the frequencies Fa and Fb are values ​​indicating specific frequencies. Also, "<information identifier> frequency=Fa <condition> information amount>=100" means that the information amount (for example, volume) of the frequency "Fa" is 100 or more.

[0056] The firing condition is, for example, a condition related to one or more feature information of the image information. Such a firing condition is called an image feature condition. An example of the image feature condition is "<information identifier>R <condition> information amount >= 150". An example of the initial firing condition is "information amount of 'R' >= 150", "(information amount of 'R' >= 150) & (information amount of 'G' >= 80)", or "(information amount of 'R' >= 150) & (information amount of 'G' >= 80) & (information amount of 'B' >= 120)".

[0057] The firing condition is, for example, a condition related to the frequency at which a signal is transmitted to a node. Such a firing condition is called a frequency condition. The frequency condition is a condition related to frequency information. Frequency information is information related to the frequency at which information is transmitted to a node. Frequency information is, for example, the time resolution at which a signal is transmitted and the number of times a signal is transmitted within a predetermined time. Note that this information typically includes one or more characteristic information of the received information or signal strength. The information here may also be referred to as a signal. Frequency conditions are, for example, a condition for a specific time resolution, a condition for a specific time resolution range, a condition for specific rhythm information, a condition for a range of similarity with a reference specific rhythm information, etc. The time resolution is the number of times a signal fires per unit time. The rhythm information is a vector whose elements are the values ​​of two or more intervals (times) during which a signal is transmitted. Note that transmission of a signal to a node means, for example, that another node connected to the node by an edge fires. When a signal is transmitted to a node, the node in question is, for example, a node connected to the edge determined to transmit the signal.

[0058] The firing condition may be, for example, a combination of two or more conditions selected from an intensity condition, a sound feature condition, an image feature condition, and a frequency condition.

[0059] The cortex section 113 stores one or more pieces of long-term memory information. Long-term memory information is information that is stored over a long period of time. The long-term memory information includes an ignition pattern and a signal strength. The long-term memory information may also include one or more pieces of feature information. The cortex section 113 may also be referred to as "Cortex 113."

[0060] The hippocampus section 114 stores one or more firing patterns. The firing patterns stored in the hippocampus section 114 may be associated with signal strength. The firing patterns may be associated with one or more pieces of feature information. The firing patterns, etc. of the hippocampus section 114 may be referred to as short-term memory information. The firing patterns, etc. are one or more pieces of information selected from the firing pattern, or the firing pattern and signal strength, and one or more pieces of feature information. The storage of the firing patterns, etc. in the hippocampus section 114 may be temporary storage. The hippocampus section 114 may be referred to as "hippocampus114" or "HIP114."

[0061] One or more images are stored in the image storage unit 115. Each image here is associated with, for example, an ignition pattern. The image may be a still image or a moving image.

[0062] The reception unit 12 receives various commands or instructions. The various commands or instructions are, for example, a start instruction or an end instruction. A start instruction is an instruction to start processing by the information processing device 1. A start instruction is, for example, information indicating that the eyes have been closed or information indicating that the subject has fallen asleep. In other words, for example, when the subject closes their eyes or falls asleep, the processing by the information processing device 1 is started. An end instruction is an instruction to end processing by the information processing device 1. An end instruction is, for example, information indicating that the eyes have been opened or information indicating that the subject has woken up. In other words, for example, when the subject opens their eyes or wakes up, the processing by the information processing device 1 is ended.

[0063] The subject may be, for example, a robot equipped with the information processing device 1 or an avatar having the functions of the information processing device 1.

[0064] Here, acceptance may be a concept that includes, for example, receiving information transmitted via a wired or wireless communication line, accepting information input from an input device such as a keyboard, mouse, or touch panel, and accepting information read from a recording medium such as an optical disk, magnetic disk, or semiconductor memory.

[0065] The processing unit 13 performs various types of processing, such as processing performed by a transmission unit 131, a corpus call analysis unit 132, a positive and negative analysis unit 133, a negative and positive analysis unit 134, or a daydream unit 135.

[0066] The processing unit 13 deletes the ignition information stored in the storage unit 11, for example, when the deletion condition is met. The deletion condition is that a time equal to or longer than a threshold has elapsed since the ignition information was accumulated. Deleting the ignition information may involve actually deleting the ignition information, or may involve processing such as adding a deletion flag to the ignition information. Deleting the ignition information means putting the ignition information into an unused state.

[0067] The transmission unit 131 performs a firing transmission process. The firing transmission process is a process in which firing is transmitted between nodes. The process in which firing is transmitted between nodes is usually a process in which transmission information is transmitted between nodes.

[0068] The transmission unit 131 acquires one or more firing patterns from the one or more firing patterns stored in the hippocampus unit 114. Next, the transmission unit 131 acquires the node identifiers of one or more firing nodes corresponding to each of the acquired one or more firing patterns. Next, the transmission unit 131 determines the node identifiers of nodes that are connected by edges to the nodes identified by the acquired one or more node identifiers and that will fire based on the firing conditions corresponding to the nodes. This series of processes is the firing transmission process.

[0069] The transmission unit 131 may randomly select one or more firing patterns from one or more firing patterns stored in the hippocampus unit 114, or may acquire one or more firing patterns in sequence. The conditions, algorithms, etc., under which the transmission unit 131 acquires one or more firing patterns are not important. The number of firing patterns acquired by the transmission unit 131 from one or more firing patterns stored in the hippocampus unit 114 may be fixed or may vary. When the number of firing patterns acquired from one or more firing patterns stored in the hippocampus unit 114 is fixed, the number is typically "1," but is not important. When the number of firing patterns acquired from one or more firing patterns stored in the hippocampus unit 114 varies, the transmission unit 131 may, for example, randomly select a number from a predetermined range of numbers. The transmission unit 131 may acquire all firing patterns stored in the hippocampus unit 114.

[0070] For example, when the reception unit 12 receives a start instruction, the transmission unit 131 performs firing transmission processing using partial neural network information having two or more node information and one or more edge information of areas corresponding to one or more area identifiers that match the sleep conditions.

[0071] A sleep condition is a condition that specifies one or more areas used during sleep. The sleep condition has, for example, one or more area identifiers that identify areas used in the ignition transmission process in the information processing device 1. The sleep condition has, for example, one or more area identifiers that identify areas not used in the ignition transmission process. For example, the sleep condition has an area identifier of "positive or negative or image processing area."

[0072] For example, the transmission unit 131 may receive transmission information from two or more edges for one node. Receiving transmission information is the transmission of a signal.

[0073] For example, when transmitting information from one node to another node connected by an edge, the transmitter 131 may reduce the signal strength corresponding to the one node before transmitting the information to the other node. Note that transmitting information such as signal strength corresponds to associating the transmission information with the other node.

[0074] The corpus call OSAM unit 132 determines whether the firing state in the neural network satisfies a steady-state condition and acquires a firing pattern when it is determined that the steady-state condition is satisfied. Such a firing pattern may be a firing pattern acquired before it is determined that the steady-state condition is satisfied, or a firing pattern acquired after it is determined that the steady-state condition is satisfied.

[0075] The corpus call summation unit 132 also acquires one or more pieces of information: the positivity acquired by the positivity analysis unit 133 and the negativity acquired by the negativity analysis unit 134. The corpus call summation unit 132 also acquires signal strength using one or more pieces of information: the positivity or the negativity. Next, the corpus call summation unit 132 accumulates long-term memory information containing the acquired firing pattern and the acquired signal strength in the cortex unit 113. The signal strength is, for example, "positivity + negativity" or "the larger of the positivity and negativity." The signal strength typically increases as the positivity increases. The signal strength typically increases as the negativity increases. The corpus call summation unit 132 may also be referred to as "Corpus Callosum 132." It is preferable that the long-term memory information include one or more pieces of feature information.

[0076] The steady-state condition is a condition for determining that the firing state in the NN is steady. For example, the steady-state condition is that the variation information is equal to or less than a threshold value. For example, the steady-state condition is that a predetermined time or more has elapsed since the transmission unit 131 started the firing transmission process using one or more firing patterns of the hippocampus unit 114. The predetermined time is usually a constant, and its value is not important. The firing state in the NN is usually determined using the firing patterns. However, the steady-state condition is not important.

[0077] The variation information is information about the variation in the number of fired nodes over time, such as the variance of the number of fired nodes at two or more time points, the reciprocal of the difference between the numbers of fired nodes at two time points, or the reciprocal of the sum of the differences between the numbers of fired nodes at two consecutive time points among three or more consecutive time points.

[0078] The corpus call osm unit 132 determines, for example, whether the signal strength satisfies a storage condition, and stores the long-term memory information in the cortex unit 113 if it determines that the storage condition is satisfied.

[0079] The storage condition is a condition for storing long-term memory information. The storage condition is a condition related to signal strength. For example, the storage condition is that the signal strength is equal to or greater than a threshold value.

[0080] The corpus call OSAM unit 132, for example, acquires the number of firing nodes at each of two or more time points in the NN, acquires variation information using the two or more numbers of firing nodes, determines whether the variation information satisfies a steady-state condition, acquires a firing pattern when it is determined that the steady-state condition is satisfied, and stores long-term memory information having the firing pattern and the acquired signal strength in the cortex unit 113. The corpus call OSAM unit 132 may also store long-term memory information having the firing pattern, signal strength, and one or more pieces of signature information in the cortex unit 113.

[0081] The corpus callosum unit 132, for example, uses one or more firing patterns of the hippocampus unit 114 to determine whether a steady-state condition is met because a predetermined time or more has passed since the transmission unit 131 started the firing transmission process, acquires the firing pattern when it is determined that the steady-state condition is met, and stores long-term memory information having the firing pattern and signal strength in the cortex unit 113.

[0082] It is preferable that the corpus call analysis unit 132 stores long-term memory information in a storage area that is easier to access as the signal strength increases.

[0083] For example, the corpus call OSAM unit 132 sorts two or more pieces of long-term memory information stored in the cortex unit 113 using signal strength as a key. Sorting two or more pieces of long-term memory information stored in the cortex unit 113 using signal strength as a key means, for example, sorting the two or more pieces of long-term memory information stored in the cortex unit 113 in descending order using signal strength as a key. The timing at which the corpus call OSAM unit 132 sorts the two or more pieces of long-term memory information is not important. Sorting two or more pieces of long-term memory information using signal strength as a key means, for example, the cortex unit 113 determines an appropriate position in a set of long-term memory information sorted in order of signal strength using the signal strength of the long-term memory information to be stored, and stores the long-term memory information in that position. The appropriate position means inserting the long-term memory information to be stored between a piece of long-term memory information having a signal strength greater than the signal strength of the long-term memory information to be stored and a piece of long-term memory information having a signal strength less than the signal strength of the long-term memory information to be stored.

[0084] The positivity unit 133 obtains the number of fired nodes among one or more nodes corresponding to the area identifier indicating the positivity, and obtains the positivity using the number of fired nodes. The positivity is, for example, the number of nodes. The positivity is, for example, a value obtained by an increasing function using the number of nodes as a parameter. The positivity unit 133 may also be called "Posi amygdala 133."

[0085] The nega amygdala unit 134 obtains the number of fired nodes among one or more nodes corresponding to the area identifier indicating the nega amygdala, and obtains the negativity using the number of nodes. The negativity is, for example, the number of nodes. The negativity is, for example, a value obtained by an increasing function with the number of nodes as a parameter. The nega amygdala unit 134 may also be called "Nega amygdala 134."

[0086] "Posi amygdala" and "Nega amygdala" can be combined to say "amygdala."

[0087] The daydreaming unit 135 performs daydreaming processing, which is a simulation of dreaming.

[0088] The fantasy unit 135 acquires an image corresponding to the firing pattern accumulated by the corpus call OSAM unit 132 from the image storage unit 115 and outputs the image. For example, the fantasy unit 135 acquires an image paired with an firing pattern that satisfies a similarity condition to the firing pattern accumulated by the corpus call OSAM unit 132 from the image storage unit 115 and outputs the image.

[0089] The similarity condition is, for example, that the similarity is equal to or greater than a threshold, that the similarity rank is equal to or greater than N (N is a natural number equal to or greater than 1), or that the similarity is equal to or greater than a threshold and the similarity rank is equal to or greater than N. When the firing patterns are vectors, the similarity is, for example, the similarity between two vectors (usually cosine similarity). The similarity is information based on, for example, the proportion of node identifiers that the two firing patterns have in common.

[0090] Here, it is preferable that output is a concept that includes display on a display, projection using a projector, printing on a printer, sound output, transmission to an external device, storage on a recording medium, and delivery of processing results to other processing devices or other programs.

[0091] The storage unit 11, NN storage unit 111, firing condition storage unit 112, cortex unit 113, hypocampus unit 114, and image storage unit 115 are preferably non-volatile recording media, but may also be realized as volatile recording media.

[0092] There is no restriction on the process by which information is stored in the storage unit 11 etc. For example, information may be stored in the storage unit 11 etc. via a recording medium, information transmitted via a communication line etc. may be stored in the storage unit 11 etc., or information input via an input device may be stored in the storage unit 11 etc.

[0093] The reception unit 12 can be realized by a wireless or wired communication means, a means for receiving broadcasts, a device driver for an input means such as a touch panel or keyboard, or control software for a menu screen.

[0094] The processing unit 13, the transmission unit 131, the corpus call analysis unit 132, the positive and negative analysis unit 133, the negative and positive analysis unit 134, and the fantasy unit 135 can typically be realized by a processor, memory, or the like. The processing procedures of the processing unit 13, etc., are typically realized by software, and the software is recorded on a recording medium such as a ROM. However, they may also be realized by hardware (dedicated circuits). The processor may be a CPU, MPU, GPU, or the like, and the type does not matter.

[0095] The dreamer 135 may or may not be considered to include an output device such as a display, a projector, etc. The dreamer 135 may be realized by driver software for the output device, or by a combination of driver software for the output device and the output device, etc.

[0096] Next, an example of the operation of the information processing device 1 will be described with reference to the flowchart of FIG.

[0097] (Step S201) The reception unit 12 determines whether or not a start instruction has been received. If a start instruction has been received, the process proceeds to steps S202 and S208, and if not, the process returns to step S201. The reception unit 12 receives the start instruction from, for example, an external device (not shown).

[0098] (Step S202) The transmission unit 131 acquires one or more firing patterns from the one or more firing patterns stored in the hippocampus unit 114.

[0099] (Step S203) The transmission unit 131 assigns 1 to the counter i. The process proceeds to steps S204 and S209.

[0100] (Step S204) The daydreaming unit 135 performs an image acquisition process. An example of the image acquisition process will be described with reference to the flowchart in FIG.

[0101] (Step S205) The daydreaming unit 135 outputs one or more images acquired in step S203. When the daydreaming unit 135 outputs two or more images, it is preferable to output two or more images sequentially, but two or more images may also be output simultaneously.

[0102] (Step S206) The transmission unit 131 determines whether or not the i-th ignition node exists among the ignition nodes identified by the one or more ignition patterns acquired in step S202. If the i-th ignition node exists, the process proceeds to step S207; if not, the process proceeds to step S210.

[0103] (Step S207) The transmission unit 131 performs next transmission processing. An example of the next transmission processing will be described using the flowchart in FIG. 3. The next transmission processing is processing that imitates the transmission of firing from one node to a next node connected to the first node by an edge. The next transmission processing is processing that determines the node that will fire next, which is a node to which transmission information is passed from the firing node of interest.

[0104] (Step S208) The transmission unit 131 increments the counter i by 1. The process returns to step S206.

[0105] (Step S209) The corpus call OSAM unit 132 and the like perform long-term memory processing. Note that the long-term memory processing includes processing for accumulating long-term memory information. Here, the long-term memory processing includes dream processing. An example of the long-term memory processing will be described using the flowchart in FIG. 6.

[0106] (Step S210) The processing unit 13 determines whether or not to end the process. If the process is to be ended, the process returns to step S201, and if not, the process returns to step S202.

[0107] The process ends when, for example, an end instruction is received from an external device (not shown).

[0108] In the flowchart of Fig. 2, the firing transmission and other processes in steps S204 to S206 and the long-term storage process in step S209 are usually performed in parallel or in parallel. Also, in the flowchart of Fig. 2, the next transmission process in step S207 is executed in a loop the number of times corresponding to the number of firing nodes, but the next transmission processes the number of times corresponding to the number of firing nodes may be performed in parallel or in parallel. In other words, in the flowchart of Fig. 2, multiple processes may be performed in parallel or in parallel. It does not matter what is processed in parallel or in parallel.

[0109] In the flowchart of FIG. 2, the timing of the image acquisition process and the image output process (the processes of S203 and S204) is not limited to the above timing.

[0110] Furthermore, in the flowchart of FIG. 2, the process ends when the power is turned off or an interrupt occurs to end the process.

[0111] Next, an example of the next transmission process in step S207 will be described with reference to the flowchart of FIG.

[0112] (Step S301) The transmission unit 131 acquires the node identifier of a node of interest. The node of interest is, for example, the i-th firing node of step S204.

[0113] (Step S302) The transmitter 131 acquires transmission information paired with the node identifier acquired in step S301. The transmission information paired with the node identifier is a node connected by an edge to the node identified by the node identifier, and may be transmission information paired with a node that previously fired. The transmission information is, for example, one or more pieces of feature information, signal strength, or one or more pieces of feature information and signal strength.

[0114] (Step S303) The transmission unit 131 obtains one or more edge identifiers that pair with the node identifier obtained in step S301 from the NN storage unit 111. Each of the one or more edge identifiers is the identifier of an edge ahead of the node. The node ahead is the destination to which a signal is transmitted from the node. The transmission of a signal can be considered as the passing of transmission information.

[0115] (Step S304) The transmission unit 131 determines whether or not one or more edge identifiers were acquired in step S303. If an edge identifier was acquired, the process proceeds to step S305; if an edge identifier was not acquired, the process returns to the upper level processing. If an edge identifier paired with the node identifier was not acquired, this means that the node identified by the node identifier is a node in the output layer of the NN node group.

[0116] If the NN has a structure that does not have an output layer, the transmission unit 131 may acquire an edge identifier in this step. If the NN has a structure that does not have an output layer, all nodes are connected by edges to the next node to which the transmission information is transmitted.

[0117] (Step S305) The transmission unit 131 assigns 1 to the counter i.

[0118] (Step S306) The transmitter 131 determines whether the i-th edge identifier exists among the edge identifiers acquired in step S303. If the i-th edge identifier exists, the process proceeds to step S307; if not, the process returns to the upper level process.

[0119] (Step S307) The transmission unit 131 acquires one or more edge attribute values ​​paired with the i-th edge identifier from the NN storage unit 111. The one or more edge attribute values ​​include, for example, a weight.

[0120] (Step S308) The transmission unit 131 determines whether or not to transmit a signal via the edge, using one or more edge attribute values ​​(e.g., weights) acquired in step S307. If the signal is to be transmitted, the process proceeds to step S309; ​​if not, the process proceeds to step S314.

[0121] Note that the transmission unit 131 normally increases the probability of determining to transmit a signal as the weight increases. The transmission unit 131 obtains the probability of determining to transmit a signal using, for example, an increasing function with the weight as a parameter, and determines whether to transmit a signal according to the probability. The transmission unit 131 generates a random number, for example, and uses the random number to determine whether to transmit a signal according to the probability.

[0122] (Step S309) The transmitter 131 acquires node information of the node at the end of the edge. More specifically, the transmitter 131 acquires the node identifier of the node to which the signal is to be transmitted, which is a node identifier that pairs with the i-th edge identifier, and acquires the node information of the node identified by the node identifier from the NN storage unit 111.

[0123] (Step S310) The transmission unit 131 performs firing determination processing for the node using the node information acquired in step S309. An example of the firing determination processing will be described with reference to the flowchart of FIG.

[0124] (Step S311) If the determination result in step S310 is "fire", the communication unit 131 proceeds to step S312, and if the determination result is "not fire", the communication unit 131 proceeds to step S314.

[0125] (Step S312) The transmission unit 131 performs an in-fire process for the node identified by the node information acquired in step S309. The in-fire process is the process shown in the flowchart of FIG.

[0126] (Step S313) The transmission unit 131 performs a next transmission process for the node identified by the node information acquired in step S309. The next transmission process is the process shown in the flowchart of FIG.

[0127] (Step S314) The transmission unit 131 increments the counter i by 1. The process returns to step S306.

[0128] In the flowchart of FIG. 3, before making the firing decision in step S310, it is preferable that the transmission unit 131 waits for a time specified by the wait time paired with the node identifier acquired in step S301, and acquires all the information transmitted during that time.

[0129] 3, it is preferable that the processing unit 13 or a deletion unit (not shown) deletes ignition information that has been stored for a predetermined time or more from the storage unit 11. Note that the predetermined time is not important.

[0130] Next, an example of the firing determination process in step S310 will be described with reference to the flowchart of FIG.

[0131] (Step S401) The transmission unit 131 acquires a firing condition corresponding to a node of interest from the firing condition storage unit 112. For example, the transmission unit 131 acquires from the firing condition storage unit 112 a firing condition that is paired with a node identifier included in the node information of the node of interest.

[0132] (Step S402) The transmission unit 131 acquires transmission information corresponding to the node of interest.

[0133] Here, the transmitter 131 may acquire a signal strength obtained by subtracting the signal strength included in the transmission information corresponding to the transmission source node. Furthermore, the transmitter 131 may acquire transmission information that has passed through two or more edges. When two or more signal strengths are included in the transmission information that has passed through two or more edges, the transmitter 131 may acquire a signal strength obtained by adding up the two or more signal strengths. When two or more signal strengths are included in the transmission information that has passed through two or more edges, the transmitter 131 may acquire a signal strength obtained by calculating the sum of the two or more signal strengths and subtracting the value of the sum.

[0134] (Step S403) The transmission unit 131 determines whether the transmission information acquired in step S402 satisfies the firing condition acquired in step S401. If the firing condition is satisfied, the process proceeds to step S404, and if the firing condition is not satisfied, the process proceeds to step S405.

[0135] (Step S404) The communication unit 131 assigns "fire" to the variable "determination result" and returns to the upper level process.

[0136] (Step S405) The communication unit 131 assigns "not fired" to the variable "determination result" and returns to the upper level process.

[0137] In the flowchart of FIG. 4, the transmission unit 131 may determine whether or not to fire based on the probability according to the firing probability information included in the node information.

[0138] In the flowchart of FIG. 4, if the transmission unit 131 does not acquire the firing condition corresponding to the node of interest in step S401, the process proceeds to step S404.

[0139] Next, an example of the firing process in step S312 will be described with reference to the flowchart of FIG.

[0140] (Step S501) The transmission unit 131 acquires the node identifier of the node that will fire. Note that this node identifier is, for example, the node identifier of the node information acquired in step S309.

[0141] (Step S502) The transmission unit 131 acquires the transmission information and associates the transmission information with the node identifier acquired in step S501.

[0142] The transmission information here is, for example, the transmission information acquired in step S302. Note that if the transmission information includes signal strength, the transmission unit 131 may acquire new signal strength by subtracting the signal strength acquired in step S302, and associate the transmission information including the new signal strength with the node identifier acquired in step S501. The process of subtracting the acquired signal strength is a process of imitating the weakening of signal strength due to signal transmission. Any algorithm may be used to subtract the acquired signal strength. The process of subtracting the acquired signal strength is, for example, a process of obtaining a new signal strength using a reduction function with the signal strength as a parameter. Such a process is, for example, a process of subtracting a constant value from the signal strength, or a process of multiplying the signal strength by a positive number less than 1.

[0143] Furthermore, when the transmission information includes characteristic information, the characteristic information is characteristic information associated with the node from which the firing is transmitted, and the characteristic information associated with the node from which the firing is transmitted may be only characteristic information used to determine the firing condition of the node from which the firing is transmitted.

[0144] (Step S503) The transmission unit 131 acquires timer information that identifies the time of firing. The transmission unit 131 generates firing information that includes the node identifier acquired in step S501 and the timer information. The transmission unit 131 stores the firing information in the storage unit 11.

[0145] The transmission unit 131 may, for example, obtain timer information specifying the time of firing from a clock (not shown). The transmission unit 131 may, for example, obtain the node identifier of the node that caused the firing and the edge identifier of the edge that was passed through when the firing occurred, compose firing information having the node identifier and the edge identifier, and store the firing information in the storage unit 11. The stored firing information may also include transmission information.

[0146] (Step S504) The transmission unit 131 updates one or more node attribute values ​​paired with the node identifier acquired in step S501, and returns to the upper-level processing.

[0147] Here, the transmission unit 131 increments, for example, the number of times information paired with the node identifier acquired in step S501 by 1. The transmission unit 131 increments, for example, the firing probability information paired with the node identifier acquired in step S501. Note that the amount of increment is not important. For example, the transmission unit 131 substitutes the original firing probability information into an increasing function that uses the firing probability information as a parameter, executes the increasing function, acquires new firing probability information, and rewrites the original firing probability information with the new firing probability information.

[0148] Next, an example of the long-term storage process in step S209 will be described with reference to the flowchart of FIG.

[0149] (Step S601) The corpus call OSAM unit 132 refers to the storage unit 11 and acquires the node identifier of each of one or more firing nodes. The node identifier of each of one or more firing nodes is, for example, the node identifier included in all of the firing information stored in the storage unit 11. The node identifier of each of one or more firing nodes is, for example, the node identifier included in the firing information stored in the storage unit 11 that has a timer time within a predetermined time.

[0150] (Step S602) The corpus call summing unit 132 obtains the number of node identifiers obtained in step S601, and adds the number to a buffer (not shown).

[0151] (Step S603) The corpus call sum unit 132 acquires variation information using the number of node identifiers (number of nodes) at two or more points in time stored in a buffer (not shown). Note that if the buffer (not shown) stores only a number of nodes equal to or less than a threshold (for example, one), the variation information will be, for example, "∞." The amount of information (number of points in time) on the number of nodes used when acquiring variation information is not important, but it is preferable that it be three or more.

[0152] (Step S604) The corpus call OSAM unit 132 determines whether the variation information acquired in step S603 satisfies the steady-state condition. If the variation information satisfies the steady-state condition, the process proceeds to step S605; if not, the process proceeds to step S616.

[0153] (Step S605) The corpus call sum unit 132 acquires an ignition pattern having the node identifier of one or more ignition nodes by referring to the storage unit 11. Note that the corpus call sum unit 132 may acquire an ignition pattern having one or more node identifiers acquired in step S601, or may acquire a new ignition pattern based on the node identifiers of one or more current ignition information by referring to the storage unit 11.

[0154] (Step S606) The positional algorithm unit 133 acquires the number of node identifiers corresponding to the area identifier "positional algorithm" in the firing pattern acquired in step S605. This number is the number of fired nodes in the positional algorithm.

[0155] (Step S607) The corpus call analysis unit 132 acquires the positivity level using the number of fired nodes of the positive sentence acquired in step S606.

[0156] (Step S608) The negative amygdala unit 134 acquires the number of node identifiers corresponding to the area identifier “negative amygdala” from the firing pattern acquired in step S605. This number is the number of firing nodes of negative amygdala.

[0157] (Step S609) The corpus call OSAM unit 132 acquires the degree of negativity using the number of fired nodes of the negative Amygdala acquired in step S608.

[0158] (Step S610) The corpus call analysis unit 132 acquires signal strength using one or two pieces of information of the positivity acquired in step S607 and the negativity acquired in step S609.

[0159] (Step S611) The corpus call osm unit 132 determines whether the signal strength acquired in step S610 satisfies the accumulation condition. If the accumulation condition is satisfied, the process proceeds to step S612, and if not, the process proceeds to step S616.

[0160] (Step S612) The corpus call SAM unit 132 constructs long-term memory information including the firing pattern acquired in step S605 and the signal intensity acquired in step S610, and stores the long-term memory information in the cortex unit 113. Note that it is preferable that the long-term memory information here include one or more pieces of feature information.

[0161] (Step S613) The processing unit 13 determines whether or not to terminate the long-term storage process. If the long-term storage process is to be terminated, the process returns to the upper process, and if not, the process returns to step S601.

[0162] In the flowchart of FIG. 6, the corpus call OSAM unit 132 accumulates the long-term memory information in the cortex unit 113 in step S612, thereby realizing long-term memory.

[0163] In the flowchart of FIG. 6, it is preferable that the processes of steps S606 and S607 for acquiring the positivity and the processes of steps S608 and S609 for acquiring the negativity are performed in parallel or in parallel.

[0164] In the flowchart of FIG. 6, the timing of performing the image acquisition process does not matter.

[0165] Next, an example of the image acquisition process in step S203 will be described with reference to the flowchart in FIG.

[0166] (Step S701) The daydreaming unit 135 acquires the firing patterns accumulated in step S612.

[0167] (Step S702) The fantasy unit 135 assigns 1 to a counter i.

[0168] (Step S703) The daydreaming unit 135 determines whether or not the i-th image, etc. exists in the image storage unit 115. If the i-th image, etc. exists, the process proceeds to step S704, and if not, the process proceeds to step S707.

[0169] (Step S704) The daydreaming unit 135 acquires the firing pattern included in the i-th image or the like from the image storage unit 115.

[0170] (Step S705) The daydreaming unit 135 obtains the similarity between the firing pattern obtained in step S701 and the firing pattern obtained in step S704.

[0171] (Step S706) The fantasy unit 135 increments the counter i by 1. The process returns to step S703.

[0172] (Step S707) The daydreaming unit 135 determines one or more firing patterns that match a similarity condition from among the similarities acquired in step S705. The similarity condition is, for example, that the similarity is maximum. The similarity condition is, for example, that the similarity is in the top N (N is a natural number equal to or greater than 2) and that the firing patterns are selected randomly. The number of firing patterns selected randomly may be one, or two or more.

[0173] (Step S708) The fantasy unit 135 acquires an image paired with one or more firing patterns that meet the similarity condition from the image storage unit 115. The process returns to the upper level process.

[0174] As described above, according to this embodiment, it is possible to simulate the mechanism of memory in the brain.

[0175] Furthermore, according to this embodiment, it is possible to simulate the dreaming that occurs in the mind.

[0176] The processing in this embodiment may be realized by software. This software may be distributed by software download or the like. This software may also be recorded on a recording medium such as a CD-ROM and distributed. This also applies to the other embodiments in this specification. The software that realizes the information processing device 1 in this embodiment is the following program.That is, this program is a computer program that can access an NN storage unit that stores neural network information having two or more pieces of node information each having a node identifier and one or more pieces of edge information each having an edge identifier and specifying connections between nodes, the NN storage unit storing one or more firing patterns that are information on one or more firing nodes, and a cortex unit storing one or more pieces of long-term memory information each having a firing pattern and a signal strength that indicates the strength of a signal, the computer program acquiring one or more firing patterns from the one or more firing patterns stored in the hippocampus unit, acquiring node identifiers for one or more firing nodes corresponding to each of the one or more firing patterns, and identifying nodes that are connected by edges to the nodes identified by the one or more node identifiers and that fire based on the firing conditions corresponding to the nodes. a positive amygdala section that acquires the number of fired nodes among one or more nodes corresponding to an area identifier indicating a positive amygdala and acquires a positivity level using the number of fired nodes; a negative amygdala section that acquires the number of fired nodes among one or more nodes corresponding to an area identifier indicating a negative amygdala and acquires a negativity level using the number of fired nodes; and a corpus callosum section that determines whether the firing state in the neural network satisfies a steady-state condition, acquires a firing pattern when it is determined that the steady-state condition is satisfied, acquires the positivity level acquired by the positive amygdala section and the negativity level acquired by the negative amygdala section, acquires a signal strength using information on one or more of the positivity level or the negativity level, and stores long-term memory information having the firing pattern and the signal strength in the cortex section.

[0177] 8 shows the appearance of a computer that executes the programs described herein to realize the information processing device 1 and the like according to the various embodiments described above. The above-described embodiments can be realized by computer hardware and a computer program executed thereon. FIG. 8 is an overview of this computer system 300, and FIG. 9 is a block diagram of the system 300.

[0178] In FIG. 8, computer system 300 includes computer 301 including a CD-ROM drive, keyboard 302, mouse 303, and monitor 304.

[0179] 9, in addition to a CD-ROM drive 3012, the computer 301 includes an MPU 3013, a bus 3014 connected to the CD-ROM drive 3012 etc., a ROM 3015 for storing programs such as a boot-up program, a RAM 3016 connected to the MPU 3013 for temporarily storing instructions for application programs and providing temporary storage space, and a hard disk 3017 for storing application programs, system programs, and data. Although not shown here, the computer 301 may further include a network card for providing connection to a LAN.

[0180] A program that causes computer system 300 to execute the functions of information processing device 1 and the like of the above-described embodiment may be stored on CD-ROM 3101, inserted into CD-ROM drive 3012, and then transferred to hard disk 3017. Alternatively, the program may be transmitted to computer 301 via a network (not shown) and stored on hard disk 3017. The program is loaded into RAM 3016 when executed. The program may also be loaded directly from CD-ROM 3101 or the network.

[0181] The program does not necessarily have to include an operating system (OS) or a third-party program that causes the computer 301 to execute the functions of the information processing device 1 of the above-described embodiment. The program only needs to include instructions that call appropriate functions (modules) in a controlled manner and achieve the desired results. How the computer system 300 operates is well known, and a detailed description thereof will be omitted.

[0182] In addition, in the above program, the steps of transmitting information and receiving information do not include processing performed by hardware, such as processing performed by a modem or interface card in the transmission step (processing that can only be performed by hardware).

[0183] The computer that executes the program may be a single computer or a plurality of computers, that is, it may perform centralized processing or distributed processing.

[0184] Furthermore, in each of the above embodiments, each process may be realized by centralized processing in a single device, or may be realized by distributed processing in a plurality of devices.

[0185] The present invention is not limited to the above-described embodiment, and various modifications are possible, and it goes without saying that these modifications are also included within the scope of the present invention.

[0186] As described above, the information processing device 1 according to the present invention has the effect of being able to simulate the mechanism of memory in the brain, and is useful as a robot or the like.

Claims

1. An NN storage unit that stores neural network information having two or more node information items each having a node identifier corresponding to an area identifier for identifying any one of two or more areas including a positive amygdala or a negative amygdala, and one or more edge information items each having an edge identifier for specifying a connection between nodes; a hippocampus unit that stores one or more firing patterns which are information on one or more firing nodes; a cortex unit that stores one or more long-term memory information items each having a firing pattern and a signal strength indicating the strength of a signal; a transmission unit that acquires one or more firing patterns among the one or more firing patterns stored in the hippocampus unit, acquires the node identifiers of the one or more firing nodes corresponding to each of the one or more firing patterns, and performs a firing transmission process for determining the node identifier of a node that is connected to the node identified by each of the one or more node identifiers by an edge and that fires based on a firing condition corresponding to the node; a positive amygdala unit that acquires the number of fired nodes among the one or more nodes corresponding to the area identifier indicating a positive amygdala and acquires a positivity degree using the number of the nodes; a negative amygdala unit that acquires the number of fired nodes among the one or more nodes corresponding to the area identifier indicating a negative amygdala and acquires a negativity degree using the number of the nodes; and a corpus callosum unit that determines whether or not a firing state in the neural network satisfies a steady state condition, acquires a firing pattern when it is determined that the steady state condition is satisfied, acquires the positivity degree acquired by the positive amygdala unit and the negativity degree acquired by the negative amygdala unit, acquires a signal strength using one or more pieces of information among the positivity degree or the negativity degree, and accumulates the long-term memory information having the firing pattern and the signal strength in the cortex unit.

2. The information processing apparatus according to claim 1, wherein the corpus callosum unit determines whether or not the signal strength satisfies an accumulation condition, and accumulates the long-term memory information in the cortex unit when it is determined that the accumulation condition is satisfied.

3. The steady state condition is that the variation information regarding the variation in the number of firings in a time series is less than or equal to a threshold value, and the corpus callosum osum unit acquires the number of firing nodes at two or more time points in the neural network, acquires variation information using the two or more numbers of firing nodes, determines whether the variation information satisfies the steady state condition, acquires the firing pattern when it is determined that the steady state condition is satisfied, and stores the long-term memory information having the firing pattern and the signal strength in the cortex unit. The information processing apparatus according to claim 1 or claim 2.

4. The steady state condition is that after the transmission unit starts the firing transmission process using the one or more firing patterns of the hippocampus unit, a predetermined time or more has elapsed, and the corpus callosum osum unit determines whether the steady state condition is satisfied when a predetermined time or more has elapsed after the transmission unit starts the firing transmission process using the one or more firing patterns of the hippocampus unit, acquires the firing pattern when it is determined that the steady state condition is satisfied, and stores the long-term memory information having the firing pattern and the signal strength in the cortex unit. The information processing apparatus according to claim 1 or claim 2.

5. Two or more pieces of long-term memory information are stored in the cortex unit, and the corpus callosum osum unit sorts the two or more pieces of long-term memory information in the cortex unit in a state sorted by signal strength. The information processing apparatus according to any one of claims 1 to 4.

6. The information processing apparatus according to any one of claims 1 to 5, further comprising a dream unit that acquires an image corresponding to the firing pattern accumulated by the corpus callosum osum unit and outputs the image.

7. The information processing apparatus according to any one of claims 1 to 6, further comprising a reception unit that receives a start instruction, and when the reception unit receives the start instruction, the transmission unit uses partial neural network information having two or more node information and one or more edge information of an area corresponding to an area identifier that identifies one or more areas that match the sleep condition to perform the firing transmission process.

8. An information processing method realized by a transmission unit, a positive amygdala unit, a negative amygdala unit, and a corpus callosum unit, including an NN storage unit that stores neural network information corresponding to an area identifier for identifying any one of two or more areas including a positive amygdala or a negative amygdala, having two or more node information having node identifiers, and one or more edge information having edge identifiers for specifying connections between nodes, a hippocampus unit that stores one or more firing patterns that are information of one or more firing nodes, and a cortex unit that stores one or more long-term memory information having a firing pattern and a signal intensity indicating the intensity of a signal, the transmission unit acquires one or more firing patterns among the one or more firing patterns stored in the hippocampus unit, acquires the node identifiers of the one or more firing nodes corresponding to the one or more firing patterns, and for the nodes identified by the one or more node identifiers, determines the node identifiers of the nodes that are connected by an edge and that fire based on the firing conditions corresponding to the nodes, a transmission step of performing a firing transmission process, the positive amygdala unit acquires the number of fired nodes among one or more nodes corresponding to an area identifier indicating a positive amygdala, and acquires a positivity using the number of the nodes, a positive amygdala step, the negative amygdala unit acquires the number of fired nodes among one or more nodes corresponding to an area identifier indicating a negative amygdala, and acquires a negativity using the number of the nodes, a negative amygdala step, the corpus callosum unit determines whether the firing state in the neural network satisfies a steady state condition, acquires the firing pattern when it is determined that the steady state condition is satisfied, and acquires the positivity acquired by the positive amygdala unit and the negativity acquired by the negative amygdala unit, acquires a signal intensity using one or more information of the positivity or the negativity, and accumulates long-term memory information having the firing pattern and the signal intensity in the cortex unit, a corpus callosum step.

9. A computer accessible to an NN storage unit that stores neural network information having two or more node information items each having a node identifier corresponding to an area identifier for identifying any one of two or more areas including a positive amygdala or a negative amygdala, and one or more edge information items each having an edge identifier for specifying a connection between nodes, a hippocampus unit that stores one or more firing patterns that are information on one or more firing nodes, and a cortex unit that stores one or more long-term memory information items each having a firing pattern and a signal intensity indicating the intensity of a signal. The computer acquires one or more firing patterns among the one or more firing patterns stored in the hippocampus unit, acquires the node identifiers of one or more firing nodes corresponding to each of the one or more firing patterns, and for the nodes identified by each of the one or more node identifiers, performs a firing transmission process of determining the node identifier of the node that is connected by an edge and fires based on the firing condition corresponding to the node. The computer acquires the number of fired nodes among one or more nodes corresponding to an area identifier indicating a positive amygdala, and acquires a positivity degree using the number of the nodes. The computer acquires the number of fired nodes among one or more nodes corresponding to an area identifier indicating a negative amygdala, and acquires a negativity degree using the number of the nodes. The computer determines whether the firing state in the neural network satisfies a steady state condition, acquires the firing pattern when it is determined that the steady state condition is satisfied, acquires the positivity degree acquired by the positive amygdala unit and the negativity degree acquired by the negative amygdala unit, acquires a signal intensity using one or more information items of the positivity degree or the negativity degree, and functions as a corpus callosum unit that accumulates long-term memory information having the firing pattern and the signal intensity in the cortex unit.

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