Active electro-physiological system for determining function of biological neuronal networks

Abstract

An active electro-physiological system and method are provided. The present technology is directed to use a multi electrode array (MEA) chip to emit a temporal-spatial patterned stimulation signal data to a neuronal tissue (e.g., a brain organoid) and receive response signal data from the neuronal tissue. The system actively determines a representational function of a neuronal tissue based on correlation between temporal-spatial patterned stimulations and responses. The system performs functional phenotyping according to chemical and / or biological reagents as an external input in contact with the neuronal tissue. The active electro- physiological system diagnoses a disease by using cells and tissues of a clinical patient as the neuronal tissue placed in the MEA chip and / or as an external input to the neuronal tissue. The active electro-physiological system further performs biocomputing by training the neuronal tissue to classify input data and / or predict output data according to a representational function of the neuronal tissue.

Description

ACTIVE ELECTRO-PHYSIOLOGICAL SYSTEM FOR DETERMINING FUNCTION OF BIOLOGICAL NEURONAL NETWORKSBACKGROUND

[0001] The concept of representational function (e.g., information processing capability) has become central to understanding activities associated with cognitive matters and brain functions. Neuronal activity has been interpreted as serving the function of representing content, transforming representations of content. Examples of content includes a visual image, a sound, an order, and the like. Representation links cognition to brain activity. Accordingly, understanding about representation has enabled building functional models of how organs, a brain, for example, processes information.

[0002] The conventional passive electrophysiology systems for evaluating representational function can only measure the spontaneous neuronal activity of neuronal tissues (e.g., organoids) and in vitro cultures. The ability of neuronal systems to process and compute the external information cannot be evaluated in these conventional systems. There is a need for an active electrophysiology system for improved evaluation of representational function of biological neuronal networks.

[0003] There is a need to develop a system that actively evaluates the in vitro neuron systems to process and compute external information as a functional phenotyping method. Further, there has been a need to identify how treatment / genetic manipulation / gene expression manipulation could affect such functional phenotype of the in vitro neuron systems. Such treatment could be and not limited to environmental factors, toxin, small molecules, biologies as well as cell therapy. This could elucidate mechanisms underlying and not limited to environmental toxin / factor related cognitive decline, evaluate impact of smoking / addiction / other medications on cognitive functions, as well as function as a tool to evaluate potential impact / side effect of biologies. It could also be integrated into current in vitro neuronal culture models of neural degenerative disease such Alzheimer’s Disease (AD), Parkinson’ Disease (PD), amyotrophic lateral sclerosis (ALS) and Down’s Syndrome to evaluate their functional phenotype and how various treatment affects such phenotype.

[0004] Additionally, or alternatively, genetic manipulation / perturbation such as gene knock-out using a genome editing method (e.g., clustered regularly interspace short palindromic repeats (CRISPR)) may be performed to the system to study the impact of genetic mutations / perturbations on the functional phenotype. In aspects, the geneticmanipulation / perturbation may lead to discovery of novel mechanisms / drug targets of various diseases that impact cognitive functions of the nervous system. Impact of gene expression changes on neuron functions could also be studied in our system by introduction of temporary or constitutive transfection or transduction of transgenes (e.g., siRNA, miRNA, IncRNA, and the like) to study how such introduced perturbation of gene expression directly impact the system’s capacity to process information. Such studies may result in discovery of novel mechanism and drug discovery.

[0005] It is with respect to these and other general considerations that the aspects disclosed herein have been made. Although relatively specific problems may be discussed, it should be understood that the examples should not be limited to solving the specific problems identified in the background or elsewhere in this disclosure.SUMMARY

[0006] Aspects of the present disclosure relate to determining representational function associated with a neuronal tissue (e.g., an organoid or animal or patient -derived specimen). In particular, the subject matter is directed to generating temporal and / or spatial patterned electrical stimulation as active interaction with the neuronal tissues (e.g., organoids) and receiving electrical response signals from the organoid. The system uses a multi-electrode array of an electrophysiological system to emit patterned stimulation signals and receive response signals. The system further determines correlation between patterns of the electrical stimulation and the electrical response. Given the correlation, the system determines a representational function of the organoid associated with semantic information as input. Using the active interaction with the organoid, the system determines functional phenotypes of an organoid according to use of distinct types of drugs and stimulants. The system emits electrical pulses as stimulation. Additionally, or alternatively, the subject matter is directed to generating pattered light pulse stimulation by emitting a laser.

[0007] The representational interpretation provides a powerful explanatory framework that makes it easier to understand neuronal activity in the context of the overall function of the brain. Representation links cognition to brain activity and enables us to build functional theories of brain information processing. By applying the temporal and / or spatial patterned stimulation encoded with information to the organoid, the organoid classifies electrical patterns as the presentational function of organoid.

[0008] Additionally, or alternatively, the present technology uses biological reagents and / or animal or clinical patient-derived materials as an external input to the neuronal tissue. The system diagnoses a neurological disease base on differences of the neuronal tissue reacting to the temporal-spatial patterned stimulations with and without the external input.

[0009] Additionally, or alternatively, tissues of a clinical patent are directly in contact with the multi-electrode array as the neuronal tissues to diagnose the tissues based on response signals to given temporal-spatial patterned stimulation signals.

[0010] Further, the present system provides functionalities of biocomputing by using the neuronal tissue as a processor of biocomputing. The representational functions associated with the neuronal tissue represents computational operations to classify, predict, and infer output data from given input data.

[0011] In aspects, the term “temporal and / or spatial patterned stimulation” (i.e., temporal- spatial patterned stimulation) includes spatial patterned stimulation over a predetermined time duration. The predetermined time duration includes an instantaneous time. The spatial patterned stimulation includes a stimulation based on a pattern in a multi-dimensional space. The stimulation includes a pulse signal. The pulse signal includes either or both of an electric pulse signal and a light pulse signal according to a laser emission.

[0012] In aspects, the term “neuronal tissue” includes an organoid and a tissue collected from a patient or an animal. Examples of an organoid include a human brain organoid and an organoid created from other neuronal cells of an organism. Aspects as described herein by using an organoid are not limited to use of the organoid but include use of the neuronal tissue.

[0013] In aspects, the term “external input” includes biological reagents, drugs, and cells. The external input further includes materials that are derived from a clinical patient. The external input is added to be in contact with the neuronal tissue when the neuronal tissue receives temporal-spatial patterned stimulation signal data and respond to the stimulation signal data.

[0014] This Summary introduces a selection of concepts in a simplified form, which is further described below in the Detailed Description. This Summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter. Additional aspects, features, and / or advantagesof examples will be set forth in part in the following description and, in part, will be apparent from the description, or may be learned by practice of the disclosure.BRIEF DESCRIPTIONS OF THE DRAWINGS

[0015] Non-limiting and non-exhaustive examples are described with reference to the following figures.

[0016] FIG. 1 illustrates an overview of an example electro-physiological system in accordance with aspects of the present disclosure.

[0017] FIG. 2 illustrates an overview of an example electro-physiological system in accordance with aspects of the present disclosure.

[0018] FIG. 3 illustrates an example of a multi-electrode array with a spatial pattern of electronic stimulation signals in accordance with aspects of the present disclosure.

[0019] FIG. 4 illustrates an example of temporal, spatial electric response signal data in accordance with aspects of the present disclosure.

[0020] FIG. 5 illustrates an example method for determining organoid representational function using an electro-physiological system in accordance with aspects of the present disclosure.

[0021] FIG. 6 illustrates an example method for determining a phenotype according to distinct stimulants to an organoid in accordance with aspects of the present disclosure.

[0022] FIG. 7 illustrates a simplified block diagram of a device with which aspects of the present disclosure may be practiced in accordance with aspects of the present disclosure.DETAILED DESCRIPTION

[0023] Various aspects of the disclosure are described more fully below with reference to the accompanying drawings, which form a part hereof, and which show specific example aspects. However, different aspects of the disclosure may be implemented in many different ways and should not be construed as limited to the aspects set forth herein; rather, these aspects are provided so that this disclosure will be thorough and complete and will fully convey the scope of the aspects to those skilled in the art. Practicing aspects may be as methods, systems, or devices. Accordingly, aspects may take the form of a hardware implementation, an entirelysoftware implementation or an implementation combining software and hardware aspects. The following detailed description is, therefore, not to be taken in a limiting sense.

[0024] The concept of representation is central to the cognitive features associated with brain cells. The technological domain interprets neuronal activity as serving the function of representing content and transforming representations of content. Classifying a given content as semantic information to determine representational function has an objective to produce successful behaviors of a brain. The content may include a visual image, a sound or odor, a semantic interpretation of sensory input, a proposition, a goal, a planned action, or a motor sequence. The representational interpretation provides an explanatory framework to understand neuronal activity in the context of the overall function of the brain. In particular, representation links cognition to brain activity and thus enables building functional models associated with brain information processing.

[0025] In aspects, brain organoids include in vitro three-dimensional aggregates. The in vitro three-dimensional aggregates are derived through the self-organization and differentiation of human pluripotent stem cells that become brain-like tissues. Types of the brain organoids may include but not limited to human cortical organoids and human midbrain-like organoids. Such tissues have an ability to recapitulate aspects of a developing brain’s structure and function. Compared to the traditional two-dimensional in vitro neuronal cultures, use of brain organoids advances processing of information in complexity, connectivity, neuroplasticity, and neurogenesis, accomplished with low energy consumption and fast learning. The traditional organoid electrophysiological measurement only measures spontaneous activity without injecting stimulation. Accordingly, there has been a need for a technical solution for the technical problem of actively interacting with an in-vitro three-dimensional organoid and determining a representational function of the organoid using an electrophysiological system and apparatus.

[0026] The present disclosure addresses these and other issues by a system and method to determine the representational function (or information processing capability) of human brain organoids (e.g., pattern recognition) and functional phenotyping of external input (e.g., drugs, cells, and other biological reagents and clinical patient-derived materials). After plating a human brain organoid onto a multi el ectrode array (MEA) chip, the system generates and applies the spatial patterned electrical stimulation encoded with information to the plated organoid via MEA. The multi el ectrode array chip may include, but not limited to, 26400 electrodes per chip and / or 64 electrodes per well, for example. The evoked response oforganoid neuronal network (ONN) may be decoded to represent the encoded information. The representational function of organoids develops as the maturation of organoids and could be regulated by neural modulation drugs.

[0027] The disclosure includes a method to actively interact with human cortical organoids and to determine their pattern recognition abilities. The electro-physiological system in the disclosure actively evaluates electrical stimulation and response signal data and determines organoid representational functions. By applying a spatial patterned electrical stimulation encoded with information to the organoid, the system detects the organoid classifying electrical patterns as the presentational function.

[0028] The disclosure further emits a variety of stimulation to the organoid network by spatial patterned electrical stimulations and read the evoked neuronal activity of the organoid. Given the evoked neuronal activity, the disclosure analyzes the organoid’s activity by a readout function (logistic regression analysis and principal component analysis (i.e., PCA)) to access the information carried by neuronal activity trails and determines the organoid’s performance in the pattern recognition.

[0029] In examples, four different patterned stimulation is repeated 30 times each with 5 seconds interval, and 30 seconds interval when switching patterns. The evoked electrical activity of the organoids may be recorded and subjected to further analysis. The evoked activity from the top-20 most active channels in the spontaneous recordings are selected, and neural activity spikes are detected using an adaptive threshold algorithm (threshold to 5.5x of standard of the background noise for each channel).

[0030] In examples, the spikes are binned in 10 milliseconds (ms) time bins to a 2D matrix (x = channel, y =time, value = a total number of spikes of one channel in the 10ms time bin). To avoid artefacts introduced by stimulation, the spikes from the first 10 ms after stimulation are removed. The 2D matrix may then be split into 2: 1 (train: test) to train a logistic regression algorithm to decode the neural activity, and the pattern recognition accuracy are obtained from test data decoded by trained logistic regression algorithm. Use of the 10 ms time bins and the 2: 1 split of the 2D matrix are examples and the present disclosure is not limited to the 10ms time bins and the 2: 1 split.

[0031] In example, the 2D neural activity matrix may further be fed to the principal component analysis to visualize the organoid activity patterns representing spatial information. The 2D matrix representing evoked neural activity is first windowed at [10 ms: 200 ms] and isthen linear transformed to a ID array. The ID array is then input to the principal component analysis to reduce the dimension to, for example, seven principal components. The neural activity patterns may be visualized by plotting each stimulation evoked activity trail in the space of the first two components.

[0032] In aspects, the organoid is exposed to, or in touch with an external input (e.g., a drug, cells, biological reagents, materials that are derived from a clinical patient, and the like) when the organoid receives temporal-spatial patterned stimulation signals and respond to the temporal-spatial patterned stimulation signals. Response of the organoid maybe compared to responses by the organoid without an exposure to the external input as a drug assay.

[0033] In some other aspects, the response by the organoid to pulse stimulation may be used for a diagnosis operation to detect a neurological disease. In examples, a representational function may be associated with the neurological disease. In some other examples, differences in correlation data between the temporal spatial patterned stimulation signals and response signals by the organoid with or without the external input may be used to diagnose the neurological disease.

[0034] In some other aspects, correlation data between the spatial patterned stimulation data and received set of temporal-spatial response signals data represent a transformation of input data into output data as a part of processing information according to biocomputing. In examples, output of the transformation indicates a class as a result of classification of input data. In some other examples, the present system performs learning of the representational function by using a set of input data and external data as training data.

[0035] Various embodiments in the present disclosure thus enable the use of a multielectrode array to generate spatial patterned electrical stimulation for reception by an organoid and further receive response signals through the multi-electrode array and determine representation function of the organoid according to correlation data based on the response signal data. In aspects, the disclosure technology addresses the above-detailed issues by generating the spatial patterned data from semantic information for encoding and further determining a representational function by decoding the temporal and spatial patterned response signal data.

[0036] FIG. 1 illustrates an overview of an example electro-physiological system in accordance with aspects of the present disclosure. A system 100 may include pulse pattern generator 102, pulse signal generator 104, response signal receiver 106, response signal dataprocessor 108, and representational function output processor 110, temporal and / or spatial pattern data 120, response signal data 122, correlation data 124, and representational function data 126.

[0037] The pulse pattern generator 102 generates and stores a set of the temporal and / or spatial pattern data 120 for electrical stimulation as semantic information in an encoded form. The semantic information may indicate a type of temporal information and / or spatial information encoded by electrical stimulation pulses as sensed by the multi el ectrode chip as spikes. In aspects, the temporal and / or spatial pattern data 120 include one or more frames of spatial pattens over a predetermined time duration.

[0038] In aspects, the set of temporal and / or spatial pattern data includes two-dimensional array of location information associated with electrodes. Location information indicates a location of an electrode in a two-dimensional array of electrodes. Location information further indicates a status (e.g., ON or OFF) of generating an electric pulse stimulation at a given time. In some aspects, there may be a plurality of sets of spatial patten data over multiple points of time, each set of spatial pattern data corresponding to a specified moment of time. For example, the multiple points of time correspond to a series of times in a periodic interval in milli-seconds. In aspects, a two-dimensional pattern of electrical stimulation represents semantic information, which corresponds as input to an organoid for classifying the input.

[0039] The pulse signal generator 104 generates pulse signals through a multi-electrode array as a spatial pattern electrical stimulation using the temporal and / or spatial pattern data 120. In aspects, the multi-electrode array may be a part of a multi -el ectrode array (MEA) chip, which accepts an organoid (e.g., a three-dimensional human brain organoid) for receiving the spatial pattern electrical stimulation. The pulse may include an electric pulse. Additionally, or alternatively, the pulse may be a light pulse by emitting a laser. The MEA includes a two- dimensional array of electrodes, each electrode being capable or emitting pulse stimulation and further detect response signals.

[0040] The response signal receiver 106 receives and may store the response signal data 122 from the multi-electrode array. The response signal may be emitted by the organoid and detected by the MEA chip. In aspects, the response signal data 122 includes one or more frames of response signals in a two-dimensional array of positions over time.

[0041] In aspects, the response signal receiver 106 may receive an electrical response of neurons. Additionally, or alternatively, the response signal receiver 106 receive response datafrom the organoid as a frame of image data or an influx stream of frames of image data over time. Values of pixel data of the image data represent a state of at least a part of the organoid in response to receiving a pulse stimulation.

[0042] The response signal data processor 108 processes the response signal data 112 and generates correlation data 124. In aspects, the response signal data processor 108 processes the response signal data using methods including regression and multi-content analysis (MCA). The response signal data processor 108 generates correlation data, which correlates a dimensional pattern of response signal data over time with the temporal and / or spatial pattern electrical stimulation data. Given the correlation data, the response signal data processor determines a representational function associated with the organoid.

[0043] The representational function output processor 110 outputs the representational function data 126 through distinct types of output devices including but not limited to a display and a speaker. In aspects, the representational function output processor 110 transmits the representational function data to a server over a network (not shown) for a further data analysis.

[0044] As will be appreciated, the various methods, devices, applications, features, etc., described with respect to FIG. 1 are not intended to limit the system 100 to being performed by the particular applications and features described. Accordingly, additional controller configurations may be used to practice the methods and systems herein and / or features and applications described may be excluded without departing from the methods and systems disclosed herein.

[0045] FIG. 2 illustrates an overview of an example electro-physiological system in accordance with aspects of the present disclosure. The example electro-physiological system 200 includes spatial patterned pulse generator 202, multi -el ectrode array 204, spatial response signal receiver 206, and response signal data processor 208. An example of an electrode 210 is a part of the multi-electrode array 204. The multi-electrode array 204 accepts an organoid 212. The organoid 212 may be a three-dimensional organoid that receives and transmits electric signals in vitro in the multi-electrode array. Electrodes in the multi-electrode array may emit laser and / or electric pulse stimulations. In aspects, at least a part of the three-dimensional organoid touches at least a part of electrodes in the multi-electrode array.

[0046] In contrast to the present disclosure, the traditional system monitoring spontaneous activity includes multi-electrode array, signal receiver and basic data processor. Rather, the disclosed technology actively simulates (e.g., spatial patterned pulse generator 202) theorganoid. The present disclosure further provides a machine-language based decoder (e.g., linear regression and principal component analysis). In aspects, the disclosed technology includes testing the organoid network processing of external input. The external input may be the basis of cognitive function, whereas traditional methods limit themselves to measuring organoid intrinsic activities.

[0047] In aspects, the organoid 212 classifies patterns according to a representational function of the organoid 212. The organoid 212 include a three-dimensional functional neuronal network. The three-dimensional functional neuronal network receives and processes input pulse signals as spatial information in the neuronal activity at an instantaneous time. Additionally, or alternatively, the input pulse signals may change over a time duration, thereby receiving the input signals as temporal-spatial information. Response signals as output of a neural activity may be received in a multi-dimensional space over time as temporal-spatial response information. The neuronal activity may be decoded by a read-out function to access the organoid representational performance. The pattern recognition of human cortical organoids may be characterized by providing active input to organoids and decode their neuronal activity response.

[0048] The spatial patterned pulse generator 202 connects to each electrode of a two- dimensional, multi-electrode array 204 and generates a pattern of pulses as a spatial patterned stimulation at a time or over a predetermined time duration to the organoid 212 on the multielectrode array 204. The spatial patterned stimulation may be based on electric pulse and / or light pulse based on laser emission. The spatial patterned stimulation over a predetermined time duration may be referred to as temporal-spatial patterned stimulation.

[0049] In response to receiving the temporal-spatial patterned stimulation over a time duration, the organoid 212 may emit response signals. The response signals may include but not limited to electric signals. The spatial response signal receiver 206 receives response signal data from at least a part of the multi-electrode array 204. The received response signal data may be in a two-dimensional form. There may be a plurality of received response signal data in the two-dimensional form over time as temporal-spatial response signal data.

[0050] The response signal data processor 208 uses the response signal data to generate correlation data and to further determine representational functional data associated with the organoid. In aspects, the response signal data processor 208 performs a regression data analysisover time to determine a characteristic of the response signal data in the two-dimensional space over time.

[0051] Additionally, or alternatively, the response signal data processor 208 performs principal component analysis (PCA) to identify information carried by neuronal activity trails in the received response signal data over time and further obtains performance data associated with the organoid in the pattern recognition. In effect, the response signal data processor 208 identifies a representational function of the organoid, which indicates how the organoid classifies given semantic information when the semantic information is encoded in the spatial patterned electrical stimulation.

[0052] Additionally, or alternatively, the present technology may add an external input to the neuronal tissue before emitting pulse signals according to the temporal spatial patterned stimulation data. Examples of the external input includes but not limited to biological reagents, a drug, a cell, blood, a material that is derived from a clinical patient, and the like. The neuronal tissue touches (e.g., attached to) the external input while receiving stimulation signal data. In aspects, a difference of response signal data and / or correlation data between a presence and a lack of the external input to the neuronal tissue at least in part indicate a diagnosis of a neurological disease.

[0053] Additionally, or alternatively, tissues of a clinical patient are directly in contact with the multi-electrode array as the neuronal tissues to diagnose the clinical patient’s tissues based on response signals to given temporal-spatial patterned stimulation signals.

[0054] Additionally, or alternatively, representational functions of the neuronal tissues indicate functions of biocomputing. In aspects, the representational functions associated with the neuronal tissue represent computational operations to classify, predict, and infer output data from given input data. In some aspects, the temporal spatial patterned stimulation data represent the input data for classification, prediction, inferencing, and the like. In some other aspects, the external input to the neuronal tissue may represent the input data for classification, prediction, inferencing, and the like., according to the output data. In yet some other aspects, a combination of the temporal and spatial patterned stimulation data and the external input represent input for performing the representational functions of biocomputing.

[0055] Additionally, or alternatively, the disclosed technology provides learning of a representational function of as performed using the neuronal tissue. In aspects, either one or acombination of temporal-spatial patterned stimulation signals and an external input as input training data to the neuronal tissue. The learning of the representational function may include iterative operations of emitting the temporal-spatial patterned stimulation signals to the neuronal tissue with or without presence of the external input and receiving response signals from the neuronal tissue.

[0056] FIG. 3 illustrates an example of a multi-electrode array with a spatial pattern of stimulation signals in accordance with aspects of the present disclosure. The example spatial pattern 300 illustrates a two-dimensional array of electrodes in a multi-electrode array (e.g., the multi-electrode array 204 as shown in FIG. 2). Each electrode either emits (i.e., “ON”) or not emit (i.e., no marking in the figure) a pulse as stimulation to an organoid that is placed on the multi-electrode array. In examples, the pulse may include an electric pulse. In some examples, the pulse may be based on emitting a laser. In some other examples, the pulse may be a part of a sequence of pulses that include both electric pulses and light pulses. Each electrode may be addressable independently. For example, an electrode 302 is addressable as the tenth electrode horizontally (i.e., “10” in horizonal 306) and the first electrode vertically (i.e., “01” in vertical 304). Accordingly, the electrode 302 may be identified using an identifier “1001.”

[0057] In the example spatial pattern 300, the electrode 302 is specified as the mode “ON,” which is to emit an electric pulse as a part of the spatial patterned electric electrical stimulation. The pattern of the “ON” electrodes represent a letter ‘X’ in the example spatial patten 300. The pattern ‘X’ is an example of semantic information, which is encoded in the example spatial pattern 300. As detailed above, the electrophysical system according to the disclosure generates the encoded semantic information as either one or both of the temporal stimulation and the spatial stimulation to an organoid through the multi -el ectrode array and then determines representational function of the organoid in response to the given stimulation.

[0058] As will be appreciated, the various methods, devices, applications, features, etc., described with respect to FIG. 3 is not intended to limit use of the spatial pattern 300. Accordingly, additional and / or alternative processes and configurations may be used to practice the methods and systems herein and / or features and applications described may be excluded without departing from the methods and systems disclosed herein.

[0059] FIG. 4 illustrates an example of spatial electric response signal data in accordance with aspects of the present disclosure. The response signal data 400 includes the spatialresponse signal data 402 and correlation data 404. The spatial response signal data 402 indicates a set of response signal data received by electrodes of the multi-electrode array over time (i.e., temporal-spatial response signal data). In aspects, the set of response signal data represents electric pulse signals emitted by the organoid in response to receiving the spatial patterned electrical stimulation over time. Response signal data may not be limited to electric signal data. Additionally, or alternatively, the set of response signal data is based on an influx stream of image data over time. Pixel values of the image data over time may represent temporal-spatial patterned response signal data.

[0060] In aspects, the spatial response signal data 402 indicates timing of receiving electric response signal received by respective electrodes in the multi-electrode array in a two- dimensional array overtime as the temporal-spatial response signal data. Each electrode of the multi-electrode array is identified by electrode identifier 406 along the vertical axis. Time 408 is indicated along the horizontal axis.

[0061] The marking of “OFF” indicates an electrode not having detected an electric pulse as a response signal at a particular time. The marking “ON” indicates an electrode having an electric pulse as a response signal at a particular time. The marking “.. .” indicates continuation of the state of receiving an electric signal as time lapses. Time “0” corresponding an initial time of detecting response signal data, which is subsequent to generating a spatial patterned electrical stimulation.

[0062] For example, an electrode with an electrode identifier 406 of “0105” at time ‘2’ indicates “ON.” That is, an electrode at a first horizontal position and a fifth vertical position in the multi-electrode array received a pulse at time unit of 2. In aspects, response signal data processor (i.e., the response signal data processor 108 as illustrated in FIG. 1) generates the spatial response signal data 402 in time sequence.

[0063] In aspects, the spatial response signal data 402 may be translated into correlation data 404. The correlation data 404 correlates distinct patterns to a set of electrodes at a particular time of receiving response signal data. For example, the correlation data 404 indicates pattern A 412, pattern B 414, and pattern C 416 at distinct two-dimensional locations. In some aspects, the vertical dimension of the correlation data 404 corresponds to location of electrodes in the multi-electrode array, while the horizontal dimension of the correlation data 404 corresponds to a time lapse since generating the spatial patterned electrical stimulation.

[0064] As will be appreciated, the various methods, devices, applications, features, etc., described with respect to FIG. 4 are not intended to be limited to use of the response signal data 400. Accordingly, additional data structures or configurations may be used to practice the methods and systems herein and / or features and applications described may be excluded without departing from the methods and systems disclosed herein.

[0065] FIG. 5 illustrates an example method for determining organoid representational function using an electro-physiological system in accordance with aspects of the present disclosure. A general order of the operations for the example method 500 is shown in FIG. 5. Generally, the method 500 begins with start operation 502 and end with end operation 518. The method 500 may include more or fewer steps or may arrange the order of the steps differently than those shown in FIG. 5. The method 500 can be executed as a set of computerexecutable instructions executed by a cloud system and encoded or stored on a computer readable medium. Further, the method 500 can be performed by gates or circuits associated with a processor, an ASIC, an FPGA, a SOC or other hardware device. Hereinafter, the method 500 shall be explained with reference to the systems, components, devices, modules, software, data structures, data characteristic representations, signaling diagrams, methods, etc., described in conjunction with FIGS. 1, 2, 3, 4, 6, and 7.

[0066] Following start operation 502, the method 500 begins with retrieve operation 504, which retrieves pattern data for encoding. In aspects, the pattern data represents semantic information for determining a representational function associated with the semantic information according to an organoid.

[0067] At generate spatial patterned electrical stimulation data operation 506, spatial patterned electrical stimulation data is generated. An example of the spatially patterned simulation data is as shown in FIG. 3. In aspects, the spatially patterned stimulation data may be electric. Additionally, or alternatively, the spatially patterned stimulation data may be based on one or more laser emissions. The spatial pattern stimulation data indicate a two-dimensional data that specify a status of each electrode or laser beam emitter for emitting an electric or laser pulse in the multi-electrode array. In some aspects, there may be a sequence of spatial patterned stimulation data in series over time as temporal-spatial pattern information.

[0068] At generate pulse stimulation operation 508, a pulse (e.g., an electric pulse and / or a laser pulse) is generated as stimulation in the multi-electrode array according to the spatial patterned stimulation data. In aspects, the generate pulse stimulation operation 508 generateselectric pulse over time according to the sequence of spatial patterned stimulation data. In some aspects, the generate pulse stimulation operation 508 may be proceed by changing a mode of the multi-electrode array from a pulse emitting mode to an electric signal detection mode.

[0069] In aspects, the recording electrodes include the stimulation electrodes. In examples, the stimulation electrodes connect to a digital-to-analog converter (i.e., DAC) 200 microseconds prior to emitting each stimulation pulse. The DAC outputs the bipolar stimulation pulses. After emitting the stimulation, the disclosed technology disconnects the DAC from the electrodes. The disclosed technology further removes a 10ms activity immediately after emitting the electronical stimulation to avoid recording noises from the stimulation.

[0070] At receive operation 510, response electric signals are received by electrodes of the multi-electrode array in response to generating spatial patterned pulses. In some aspects, the receive operation 510 is preceded by changing a mode of the multi -el ectrode array from a pulse emitting mode to a signal detection mode. In aspects, a pulse may be electric pulse. In some other aspects, the pulse may be based on a laser emission. In aspects, the receive operation 510 receives response signal data associated with each electrode of the multi-electrode array over time.

[0071] At generate correlation data 512, correlation data is generated by decoding the response electric signal data over time. In aspects, the correlation data is based on a two- dimensional table and indicates a signal reception status associated with each electrode over time (i.e., see the spatial response signal data 402 and the correlation data 404 as shown in FIG. 4.) In some aspects, the correlation data may be generated by determining pattern or characteristics according to time regression of the spatial response signal data. In some other aspects, a trend may be determined by performing principal content analysis over time.

[0072] At determine representational function operation 514, a representational function of the organoid in response to semantic information is determined. In aspects, the determine representational function operation 514 uses the correlation data, which indicate a specific pattern, to determine a representational function in response to semantic information associated with a particular spatial patterned electrical stimulation data. In aspects, a representational function is associated with classification of input semantic information by the organoid.

[0073] At display operation 516, the representational function of the organoid is displayed as an example of an output operation of result data. For example, the display operation 516may display a determined class or pattern as a representational function of the organoid for given semantic information using a display device. End operation 518 ends the method 500.

[0074] As should be appreciated, operations 502-518 are described for purposes of illustrating the present methods and systems and are not intended to limit the disclosure to a particular sequence of steps, e.g., steps may be performed in different order, additional steps may be performed, and disclosed steps may be excluded without departing from the present disclosure.

[0075] FIG. 6 illustrates an example method for determining a phenotype according to distinct stimulants to an organoid in accordance with aspects of the present disclosure. A general order of the operations for the example method 600 is shown in FIG. 6. Generally, the method 600 begins with start operation 602 and end with end operation 612. The method 600 may include more or fewer steps or may arrange the order of the steps differently than those shown in FIG. 6. The method 600 can be executed as a set of computer-executable instructions executed by a cloud system and encoded or stored on a computer readable medium. Further, the method 600 can be performed by gates or circuits associated with a processor, an ASIC, an FPGA, a SOC or other hardware device. Hereinafter, the method 600 shall be explained with reference to the systems, components, devices, modules, software, data structures, data characteristic representations, signaling diagrams, methods, etc., described in conjunction with FIGS. 1, 2, 3, 4, 5, and 7.

[0076] Following start operation 602, the method 600 begins with retrieve operation 604, which receives spatial patterned electrical stimulation data. In aspects, the spatial patterned electrical stimulation data may include electrical stimulation data associated with a two- dimensional layout of electrodes for emitting stimulation signals. In some other aspects, the spatial patterned electrical stimulation data may correspond to a frame of a set of multiple frames of the spatial patterned electrical stimulation data over timer, representing temporal- spatial patterned electrical stimulation data. The retrieve operation 604 may retrieve the spatial patterned electrical stimulation data from a database that stores the spatial patterned electrical stimulation data. Examples of indices used for retrieval may include but not limited to a type of a representational function, a neuronal tissue, and the like.

[0077] At generate the electrical pulses operation 606, electrical pulses are generated by electrodes of the multi-electrode array according to the spatial patterned electrical stimulation data. In aspects, the electrical pulses are generated to distinct in vitro environment with distinctstimulants and / or reagents as external input (and a control environment), with expectation that response signal data would be distinct according to presence of the external input.

[0078] At determine a phenotype operation 608, a phenotype is determined for the organoid using distinct types of stimulants. The phenotype is based on identifying correlation of patterns of specific electrodes responding to the spatial patterned electrical stimulation over time when an organoid is used with a particular stimulant. In aspects, the stimulants may be well-known small molecules and enhance / interfere with the function phenotypes (e.g., D,L-2-amino-5- phosphonovaleric acid (AP5), chaperedoxin (e.g., CNQX_, muscimol, baclofen, and the like). Additionally, or alternatively, the stimulants may include therapeutic agents (anti-amyloid beta antibodies, anti-Phospho-Tau antibodies, environmental pollutants, and the like). In aspects, the phenotype may refer the system’s capacity to process and withhold information as a surrogate readout for cognitive functions of the neurons. In some other aspects, the external input includes a material derived from a clinical patient, blood from the clinical patient, and the like.

[0079] At display operation 610, the determined result of analyzing phenotypes is displayed. For example, the difference may be in phenotypes associated with particular drugs or stimulants used with the organoid in vitro according to use of distinct stimulants. End operation 612 ends the method 600.

[0080] As should be appreciated, operations 602-612 are described for purposes of illustrating the present methods and systems and are not intended to limit the disclosure to a particular sequence of steps, e.g., steps may be performed in different order, additional steps may be performed, and disclosed steps may be excluded without departing from the present disclosure.

[0081] In aspects, cell identities in the organoids interact with external disturbance (e.g., neuromodulation drug and immune cells). The cell identities further indicate altered representational performance, which highlights the potential of representational function as a phenotype in the fields of brain biology, brain diseases, and therapeutics development. A complex neuronal network with a plurality of types of neural cells forms and maintains the representational function of the human brain. In aspects, disfunction of a single cell component may cause imbalance of the brain network and impair representational function of the human brain.

[0082] In aspects, the present case may further target glutamatergic and GABAergic synaptic transmission of 4-month human cortical organoids via pharmacological intervention to test the representational function of an unbalanced cortical organoid network. From the spontaneous recordings, cortical organoids may show significantly reduced electrical activity when treated with antagonists of glutamatergic receptor (NMD AR and AMP AR, AP5+CNQX) and agonists of GABAergic receptor (GABAA, muscimol; GABAB, baclofen), and subsequently show mostly diminished functional connectivity with a few connections, as parameterized by functional connectivity indexes (e.g., density, averaged clustering, and global efficiency). In some aspects, organoids treated with a GABAergic antagonist (bicuculline) may indicate more electrical activity and network events with shorter time duration, and more functional connectivity, while the global efficiency and other functional connectivity index shows no substantial difference compared with untreated groups. Such exemplary results from spontaneous activity may match previous reports and partially show organoid functional network altered by neuromodulators. Direct function impairment in the treated neuronal network may remain unknown. In some other aspects, application of electrical stimulation of four spatial patterns to the treated cortical organoids may characterize their representational function by pattern recognition performance. Analyzing the evoked electrical activity may indicate that these neural modulators could impact the evoked activity patterns, as visualized by raster plots and post stimulation histograms. The activity patterns of evoked activity trails by different spatial information may become less separative. The pattern recognition performance of the treated organoids may become significantly reduced, even in the cases of the bicuculine treated organoids with higher firing rate and number of network events, as well as with examples with more functional connectivity. In aspects, organoids may recapitulate the brain representational function impacted by drug administration. Accordingly, a function test of human cortical organoids according to the present technology may represent a functional phenotype for new therapeutics discovery and development.

[0083] FIG. 7 illustrates a simplified block diagram of a device with which aspects of the present disclosure may be practiced in accordance with aspects of the present disclosure. The device, which represent at least a part of the electrophysiology system, may be a mobile computing device, for example. One or more of the present embodiments may be implemented in an operating environment 700. This is only one example of a suitable operating environment and is not intended to suggest any limitation as to the scope of use or functionality. Other well- known computing systems, environments, and / or configurations that may be suitable for useinclude, but are not limited to, personal computers, server computers, hand-held or laptop devices, multiprocessor systems, microprocessor-based systems, programmable consumer electronics such as smartphones, network PCs, minicomputers, mainframe computers, distributed computing environments that include any of the above systems or devices, and the like.

[0084] In its most basic configuration, the operating environment 700 typically includes at least one processing unit 702 and memory 704. Depending on the exact configuration and type of computing device, memory 704 (e.g., Instructions for actively interacting with organoid, including generating spatial patterned electrical stimulation and receiving response signal data using a multi -el ectrode array as disclosed herein) may be volatile (such as RAM), non-volatile (such as ROM, flash memory, etc.), or some combination of the two. This most basic configuration is illustrated in FIG. 7 by dashed line 706. Further, the operating environment 700 may also include storage devices (removable, 708, and / or non-removable, 710) including, but not limited to, magnetic or optical disks or tape. Similarly, the operating environment 700 may also have input device(s) 714 such as remote controller, keyboard, mouse, pen, voice input, on-board sensors, etc. and / or output device(s) 712 such as a display, speakers, printer, motors, etc. Also included in the environment may be one or more communication connections 716, such as LAN, WAN, a near-field communications network, a cellular broadband network, point to point, etc.

[0085] Operating environment 700 typically includes at least some form of computer readable media. Computer readable media can be any available media that can be accessed by the at least one processing unit 702 or other devices comprising the operating environment. By way of example, and not limitation, computer readable media may comprise computer storage media and communication media. Computer storage media includes volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. Computer storage media includes, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other tangible, non-transitory medium which can be used to store the desired information. Computer storage media does not include communication media. Computer storage media does not include a carrier wave or other propagated or modulated data signal.

[0086] Communication media embodies computer readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media. The term “modulated data signal” means a signal that has one or more of its characteristics set or changed in such a manner as to encode information in the signal. By way of example, and not limitation, communication media includes wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, RF, infrared and other wireless media.

[0087] The operating environment 700 may be a single computer operating in a networked environment using logical connections to one or more remote computers. The remote computer may be a personal computer, a server, a router, a network PC, a peer device or other common network node, and typically includes many or all of the elements described above as well as others not so mentioned. The logical connections may include any method supported by available communications media. Such networking environments are commonplace in offices, enterprise-wide computer networks, intranets and the Internet.

[0088] The description and illustration of one or more aspects provided in this application are not intended to limit or restrict the scope of the disclosure as claimed in any way. The claimed disclosure should not be construed as being limited to any aspect, for example, or detail provided in this application. Regardless of whether shown and described in combination or separately, the various features (both structural and methodological) are intended to be selectively included or omitted to produce an embodiment with a particular set of features. Having been provided with the description and illustration of the present application, one skilled in the art may envision variations, modifications, and alternate aspects falling within the spirit of the broader aspects of the general inventive concept embodied in this application that do not depart from the broader scope of the claimed disclosure.

[0089] Any of the one or more above aspects in combination with any other of the one or more aspect. Any of the one or more aspects as described herein.

Claims

CLAIMS:

1. A method for active electro-physiological processing, comprising: retrieving pulse patten data over time; generating temporal-spatial patterned stimulation data; generating, based on the temporal-spatial patterned stimulation data, a pulse as stimulation to a neuronal tissue using a multi-electrode array; receiving a set of temporal-spatial response signals using the multi-electrode array; generating correlation data, wherein the correlation data represent correlation between the temporal-spatial patterned stimulation data and the received set of temporal-spatial response signals; determining, based on the correlation data, a representational function associated with a neural tissue; and displaying the representational function.

2. The method according to claim 1, wherein the temporal-spatial patterned stimulation data include a plurality of frames of pulse patterns over a predetermined time duration, and each frame of the plurality of frames of pulse patterns corresponds to a time in a time sequence.

3. The method according to claim 1, wherein the temporal-spatial patterned stimulation data include a two-dimensional frame of stimulation data at a time, and the two- dimensional frame of the temporal-spatial patterned stimulation data corresponds to with a layout of electrodes in the multi-electrode array.

4. The method according to claim 1, wherein the correlation data includes a two- dimensional graph indicating a relative change of locations of a pulse signal in the set of temporal-spatial response signals received over time.

5. The method according to claim 1, wherein the temporal-spatial patterned stimulation data include a location of an electrode for generating the pulse through the electrode of the multi-electrode array at a time.

6. The method according to claim 1, wherein the temporal-spatial patterned stimulation data represent an encoded semantic information.

7. The method according to claim 1, wherein the pulse includes an electric pulse.

8. The method according to claim 1, wherein the pulse includes a light pulse according to a laser emission.

9. The method according to claim 1, wherein the neuronal tissue includes a brain organoid.

10. The method according to claim 1, further comprising: adding a material as an external input, wherein a surface of the neuronal tissue touches the material as a part of an assay, wherein the material includes at least one of a drug, cells, a chemical, a biological reagent, or a clinical patient-derived material; generating, based on the temporal-spatial patterned stimulation data, the pulse as stimulation to the neuronal tissue in contact with the material using the multi-electrode array; receiving another set of temporal-spatial response signals using the multi-electrode array; generating external-input-induced correlation data, wherein the extemal-input- induced correlation data represent correlation between the temporal-spatial patterned stimulation data and the received set of temporal-spatial response signals with the external input; and determining, based on a difference between the external -input-induced correlation data and the correlation data, a functional phenotyping of the external input.

11. A system for active electro-physiological processing, the system comprising: a multi-electrode array, wherein at least an electrode in the multi-electrode array touches a neuronal tissue for sending a pulse signal to and for receiving a response signal from; a processor configured to execute operations comprising: retrieving pulse patten data over time; generating temporal-spatial patterned stimulation data; generating, based on the temporal-spatial patterned stimulation data, a pulse as stimulation to a neuronal tissue using the multi-electrode array;receiving a first set of temporal-spatial response signals from the neuronal tissue using the multi-electrode array; generating first correlation data, wherein the first correlation data represent first correlation between the temporal-spatial patterned stimulation data and the first set of temporal-spatial response signals; determining, based on the first correlation data; when a material is added as an external input and the material is in contact with the neuronal tissue, generating, based on the temporal-spatial patterned stimulation data, a second pulse as stimulation to the neuronal tissue in contact with the material using the multielectrode array, wherein a surface of the neuronal tissue touches the material as a part of an assay, wherein the material includes at least one of a drug, cells, a chemical, a biological reagent, or a clinical patient-derived material; in response to generating the second pulse, receiving a second set of temporal-spatial response signals using the multi-electrode array; generating, based on the second set of temporal-spatial response signals, second correlation data, wherein the second correlation data represent correlation between the temporal-spatial patterned stimulation data and the second set of temporal-spatial response signals with the external input; determining, based on a difference between the second correlation data and the first correlation data, a functional phenotyping of the external input; and displaying the functional phenotyping of the external input.

12. A system for active electro-physiological processing, the system comprising: a multi-electrode array; a processor configured to execute operations comprising: retrieving pulse patten data over time; generating temporal-spatial patterned stimulation data; generating, based on the temporal-spatial patterned stimulation data, a pulse as stimulation to a first neuronal tissue using the multi -electrode array, wherein at least an electrode in the multi-electrode array touches the first neuronal tissue for sending a pulse signal to and for receiving a response signal from; receiving a first set of temporal-spatial response signals from the neuronal tissue using the multi-electrode array;generating first correlation data, wherein the first correlation data represent first correlation between the temporal-spatial patterned stimulation data and the first set of temporal-spatial response signals; generating, based on the temporal-spatial patterned stimulation data, the pulse as stimulation to a second neuronal tissue using the multi -el ectrode array, wherein the multielectrode array touches the second neuronal tissue, the second neuronal tissue is distinct from the first neuronal tissue, and the second neuronal tissue includes a tissue associated with a clinical patient; in response to generating the pulse, receiving a second set of temporal-spatial response signals using the multi-electrode array; generating, based on the second set of temporal-spatial response signals, second correlation data, wherein the second correlation data represent correlation between the temporal-spatial patterned stimulation data and the second set of temporal-spatial response signals; determining, based on a difference between the second correlation data and the first correlation data, a functional phenotyping associated with a disease; and displaying, based on the functional phenotyping, a diagnosis result of the tissue associated with the clinical patient.

13. The system according to claim 12, wherein the tissue associated with the clinical patient includes blood cells associated with the clinical patient.

14. A system for active electro-physiological processing for classifying data through biocomputing, the system comprising: a multi-electrode array; a processor configured to execute operations comprising: receiving input data for source data for classification; retrieving pulse patten data according to the input data, wherein the pulse patten data includes a series of pulse patten data over a predetermined time duration; generating, based on the pulse patten data, temporal-spatial patterned stimulation data; generating, based on the temporal-spatial patterned stimulation data, a pulse as stimulation to a neuronal tissue using the multi-electrode array; receiving a set of temporal-spatial response signals as an output of biocomputing from the neuronal tissue using the multi-electrode array;generating correlation data, wherein the correlation data represent correlation between the temporal-spatial patterned stimulation data and the received set of temporal-spatial response signals, and the correlation data represents a classification function of the biocomputing for classifying the source data; determining, based on the correlation data, a class associated with the input data according to a representational function associated with the neuronal tissue; and displaying the output data.

15. A system for active electro-physiological processing for predicting data through biocomputing, the system comprising: a multi-electrode array; a processor configured to execute operations comprising: receiving input data as source data for prediction; retrieving pulse patten data according to the input data, wherein the pulse patten data includes a series of pulse patten data over a predetermined time duration; generating, based on the pulse patten data, temporal-spatial patterned stimulation data; generating, based on the temporal-spatial patterned stimulation data, a pulse as stimulation to a neuronal tissue using the multi-electrode array; receiving a set of temporal-spatial response signals as an output of biocomputing from the neuronal tissue using the multi-electrode array; generating correlation data, wherein the correlation data represent correlation between the temporal-spatial patterned stimulation data and the received set of temporal-spatial response signals; determining, based on the correlation data, inferred output data based on the input data according to a representational function associated with the neuronal tissue; and displaying the output data.

16. A system for active electro-physiological processing for learning data through biocomputing, the system comprising: a multi-electrode array; a processor configured to execute operations comprising: receiving input data as training data for training a neuronal tissue;retrieving pulse patten data according to the input data, wherein the pulse patten data includes a series of pulse patten data over a predetermined time duration; generating, based on the pulse patten data, temporal-spatial patterned stimulation data; generating, based on the temporal-spatial patterned stimulation data, a pulse as stimulation to a neuronal tissue using the multi-electrode array; receiving a set of temporal-spatial response signals as an output of biocomputing from the neuronal tissue using the multi-electrode array; generating correlation data, wherein the correlation data represent correlation between the temporal-spatial patterned stimulation data and the received set of temporal-spatial response signals; determining, based on the correlation data, inferred output data based on the input data according to a representational function associated with the neuronal tissue; iteratively generating, based on a predetermined condition associated with ending iterations, the pulse and receiving additional sets of the temporal-spatial response signals; and displaying the output data.

17. A device comprising a processor configured to execute operations comprising: retrieving pulse patten data over time; generating temporal-spatial patterned stimulation data; generating, based on the temporal-spatial patterned stimulation data, a pulse as stimulation to a neuronal tissue using a multi-electrode array; receiving a set of temporal-spatial response signals using the multi-electrode array; generating correlation data, wherein the correlation data represent correlation between the temporal-spatial patterned stimulation data and the received set of temporal-spatial response signals; determining, based on the correlation data, a representational function associated with the neuronal tissue; and displaying the representational function.

18. The device according to claim 17, wherein the temporal-spatial patterned stimulation data include a plurality of frames of pulse patterns over a predetermined time duration, and each frame of the plurality of frames of pulse patterns corresponds to a time in a time sequence.

19. The device according to claim 17, wherein the temporal-spatial patterned stimulation data include a two-dimensional frame of stimulation data at a time, and the two- dimensional frame of the temporal-spatial patterned stimulation data corresponds to with a layout of electrodes in the multi-electrode array.

20. The device according to claim 17, wherein the correlation data includes a two- dimensional graph indicating a relative change of locations of a pulse signal in the set of temporal-spatial response signals received over time.

21. The device according to claim 17, wherein the temporal-spatial patterned stimulation data include a location of an electrode for generating the pulse through the electrode of the multi-electrode array at a time.

22. The device according to claim 17, wherein the temporal-spatial patterned stimulation data represent of an encoded semantic information.

23. The device according to claim 17, wherein the pulse includes an electric pulse.

24. A system for determining a representational function associated with an organoid, the system comprising: a multi-electrode array; and a processor configured to execute operations comprising: retrieving pulse patten data over time; generating temporal-spatial patterned stimulation data; generating, based on the temporal-spatial patterned stimulation data, a pulse as stimulation to the organoid using the multi-electrode array; receiving a set of temporal-spatial response signals using the multi-electrode array; generating correlation data, wherein the correlation data represent correlation between the temporal-spatial patterned stimulation data and the received set of temporal-spatial response signals; determining, based on the correlation data, the representational function associated with the organoid; and displaying the representational function.

25. The system according to claim 24, wherein the temporal-spatial patterned stimulation data include a plurality of frames of pulse patterns over a predetermined time duration, and each frame of the plurality of frames of pulse patterns corresponds to a time in a time sequence.

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