System and method for artificial brain for machines

Abstract

This invention related to artificial brain system, comprising; a plurality of interconnected processors that emulate biological neurons by receiving signals from environment with 10 sensors and equipments, a memory module stores synaptic weights and incorporates a sophisticated set of learning rules, including Reward-Modulated Spike-Timing- Dependent Plasticity (r-STDP), dynamically optimizing synaptic weights based on contextual sensory feedback and reward signals to reflect the adaptability of biological neural circuits, a control module designed to interpret complex input from various sensors 15 and actuators, a learning engine that modulates neural activity and synaptic plasticity through advanced reinforcement signals, promoting enhanced adaptive behavior and allowing the robotic system to evolve its capabilities in alignment with environmental challenges and communication interfaces that enable seamless data transfer and control command exchange between the processors and robotic actuators.

Description

[0001] SPECIFICATION

[0002] SYSTEM AND METHOD FOR ARTIFICIAL BRAIN FOR MACHINES

[0003] Field of the Invention:

[0004] This invention presents a pioneering artificial brain system for robotic platforms that harnesses distributed intelligence of cortical columns in the human brain and optimized reward-modulated spike-timing-dependent plasticity (r-STDP) on every synapse to facilitate adaptive and autonomous learning. The system incorporates parallel processing modules that act as independent models of the environment, utilizing reference frames to facilitate adaptive, real-time decision-making and learning.

[0005] Background of the Invention:

[0006] Neuromorphic engineering has evolved significantly, with various approaches being explored to create brain-inspired systems that mimic the functionality of spiking neural networks. Notable prior art includes:

[0007] • Spiking Neural Networks (SNNs): SNNs represent a class of artificial neural networks that utilize discrete spikes to transmit information, resembling the way biological neurons communicate. Research by Maass (1997) introduced the computational capabilities of SNNs, laying the groundwork for further developments in this field.

[0008] • Lava Framework: The Lava framework provides tools for building spiking neural networks and their applications in robotic systems. This framework supports the implementation of models such as Leaky Integrate-and-Fire (LIF) neurons, enabling advanced simulations of neural behavior (Kreiser et al., 2020).

[0009] • Reward-Modulated Spike-Timing-Dependent Plasticity (r-STDP): This learning rule, based on the biological principle of synaptic plasticity, adjusts the strength of connections between neurons based on the timing of spikes and the reward signal. Research has shown that integrating r-STDP into neuromorphic systems enhances learning and adaptability (Ozen et al., 2022). • Robotic Applications: Various implementations of neuromorphic systems in robotics have been explored. For instance, the use of neuromorphic chips for real-time processing and sensory integration in autonomous robots demonstrates the potential for improved decision-making and efficiency (Gao et al., 2019).

[0010] • Cognitive Architectures: Existing cognitive architectures, such as ACT-R and SOAR, provide insights into the design of systems that mimic human-like reasoning and problem-solving. However, these systems typically rely on traditional computational paradigms, lacking the biological realism and efficiency offered by neuromorphic approaches.

[0011] Traditional robotic control systems, which rely heavily on pre-programmed algorithms and centralized processing units, face several critical challenges. One major issue is high energy consumption, as conventional processors require significant computational resources, especially for real-time decision-making and learning in dynamic environments. This inefficiency leads to excessive power usage, limiting the practical deployment of robots in energy-constrained scenarios.

[0012] Additionally, high training and improvement costs pose a significant barrier. Traditional systems often require extensive offline training processes and data collection, necessitating costly infrastructure, hardware, and labor to continuously improve performance. This not only delays the deployment of robots but also makes iterative enhancements prohibitively expensive in many cases.

[0013] Furthermore, during live operation, these systems suffer from limited programming power. Their ability to adapt on-the-fly in real-time scenarios is often restricted due to the need for complex, centralized control. This constraint reduces their effectiveness in unstructured or unpredictable environments, where quick learning and adaptation are essential for true autonomy. As a result, such systems fail to achieve the level of autonomy necessary for many real-world applications, particularly those requiring dynamic reprogramming and continuous learning during operation. Neuromorphic computing has emerged as a transformative approach in the development of robotic systems, mimicking the architecture and functionality of spiking neural networks. Traditional robotic systems often rely on conventional computational methods, which can be inefficient and limited in adaptability. In contrast, artificial brain utilize spiking neural networks (SNNs) and reward-modulated learning algorithms, such as optimized Reward-Modulated Spike-Timing-Dependent Plasticity (r-STDP) on every synapse, to enable real-time processing and learning capabilities similar to biological organisms.

[0014] Recent advancements in neuromorphic hardware and software frameworks have facilitated the integration of complex sensory data processing, decision-making, and motor control in robotic systems. These innovations allow robots to exhibit more intelligent behaviors, adapt to dynamic environments, and improve performance through experience.

[0015] Despite these advancements, there remains a need for a comprehensive artificial brain system that effectively combines multiple neural processing layers and incorporates robust learning mechanisms.

[0016] Summary of the Invention:

[0017] The present invention introduces a system and method of artificial brain for machines, which surpasses traditional robotic architectures by emulating the complex neural structures of the biological brain, particularly through a detailed cortical connectome- inspired design. By incorporating spiking neural networks (SNNs) and Reward- Modulated Spike-Timing-Dependent Plasticity (r-STDP), this innovative system enables autonomous robots to learn, adapt, and optimize their behavior based on real-time interactions with dynamic environments. This marks a significant leap in autonomous intelligence for robotic platforms, allowing them to operate with minimal human intervention and adapt to complex tasks with unprecedented flexibility and efficiency. The primary objective of this invention is to establish a robust, scalable, and efficient artificial brain framework that empowers advanced sensory processing, decision-making, and learning in robotic systems. This system uniquely integrates spiking neural networks (SNNs) with sophisticated reward-modulated learning mechanisms, such as Reward- Modulated Spike-Timing-Dependent Plasticity (r-STDP), drawing inspiration from the dynamic and flexible nature of cortical connections. This system advances machine intelligence by emphasizing parallel learning across neural networks, allowing consensus-driven adaptability in dynamic environments.

[0018] The system organizes spiking neural networks into independent units, each leveraging reference frames to model specific aspects of the robot’s sensory and motor environment. These processors collaboratively align their outputs through a consensus mechanism, mimicking the voting behavior of cortical columns. The architecture allows each spiking neural network to construct spatially-grounded models of objects and environments, enhancing the robot’s interaction capabilities and situational awareness. By decentralizing processing and enabling continuous learning across bio-inspired neural networks, the system achieves unparalleled resilience to noise and adaptability to complex tasks.

[0019] The invention is centered on a neural architecture that mimics the organizational complexity of the brain’s cortex. Drawing from the cortical connectome, it enables parallel processing of sensory data and decision-making across multiple neuromorphic cores. By dynamically adjusting synaptic connections in response to successful or unsuccessful actions, the system allows for self-optimization and adaptive decisionmaking, improving performance across diverse environments without the need for additional programming or human oversight. The system’s advanced synaptic plasticity models empower the robot to continuously modify its neural pathways, fostering realtime learning and autonomous control. This no-code programming paradigm eliminates the need for reprogramming during live operation, providing a scalable, energy-efficient, and intelligent control system that reduces both training costs and energy consumption. Overall, the fusion of r-STDP with the cortical connectome architecture gives robotic systems unprecedented levels of cognitive flexibility, adaptability, and environmental awareness, offering a powerful solution for tackling complex and dynamic environments. The invention not only enhances robot intelligence and autonomy but also significantly reduces operational costs and energy demands, setting a new standard for neuromorphic systems in robotics.

[0020] This invention features a sophisticated, layered architecture that seamlessly integrates spiking neural networks (SNNs) with dynamic synaptic plasticity, enabling real-time decision-making, precise motor control, and nuanced environmental interactions. Each layer of the modular structure is designed to emulate distinct functional components of the brain, such as sensory perception, motor execution, and higher cognitive functions, effectively mirroring the complexity of the biological connectome.

[0021] Guided by external reward signals, the learning mechanism empowers the robot to continuously optimize its behavior based on task performance and environmental feedback, thus enhancing its adaptability to varying contexts. This neuromorphic architecture is meticulously engineered for energy-efficient operation, utilizing event- driven computation to minimize power consumption while delivering exceptional performance in intricate environments. With its versatile design, this system is adaptable across a spectrum of robotic platforms, marking a significant leap in autonomy, adaptability, and efficiency, and setting a new standard for intelligent robotic systems inspired by the intricacies of the human brain.

[0022] By mimicking the spiking neural network’s efficiency in energy utilization and its ability to support flexible, distributed learning across the brain, the invention significantly enhances scalability, learning efficiency, and real-time decision-making. The resulting neuromorphic system overcomes the limitations of conventional control mechanisms, offering a robust, scalable, and energy-efficient architecture that enables robots to autonomously navigate dynamic environments with minimal external control. This marks a substantial leap forward in the field of robotic intelligence, paving the way for unprecedented levels of adaptability and autonomy. This invention is poised to significantly enhance the capabilities of robotic systems across a spectrum of applications, including autonomous navigation, robotic assistance, and complex task execution. By providing a brain-like architecture that mirrors the cognitive processes of living organisms through a cortical connectome-inspired framework, the system paves the way for the next generation of intelligent and adaptable robots.

[0023] This invention aims to achieve the following objectives:

[0024] • Cortically Inspired Adaptive Learning: To create a robotic control system that mirrors the cortical connectome, allowing robots to engage in real-time, unsupervised learning. By leveraging reward-based neural plasticity and biologically inspired connectivity patterns, the system facilitates continuous adaptation and self-improvement through interactions with dynamic environments, without the need for manual reprogramming.

[0025] • Highly Efficient Processing: To enhance computational efficiency in robotic systems by utilizing neuromorphic hardware that emulates the parallel and asynchronous processing found in biological neural circuits. This enables significant reductions in power consumption and latency, optimizing real-time responsiveness and resource allocation.

[0026] • Modular and Scalable Architecture: To implement a flexible, modular design that can scale across various robotic applications. The system integrates multiple neuromorphic processing units, each modeled after specific aspects of the cortical connectome, fostering functional diversity and specialization across different robotic tasks.

[0027] • Advanced Autonomy and Cognitive Functionality: To amplify robotic autonomy and cognitive capabilities by integrating the adaptability and complexity of spiking neural networks (SNNs). The system draws on the cortical connectome model to empower robots with sophisticated decision-making, problem-solving, and adaptability in complex, dynamic environments, driving highly autonomous behavior.

[0028] Brief Description of the Drawings: The accompanying drawings, together with the specification, illustrate exemplary embodiments of the present invention, and, together with the description, serve to explain the principles of the present invention.

[0029] Figure 1 is a detailed schematic diagram depicting the overall architecture of the present invention, which integrates a cortical connectome-inspired framework.

[0030] Figure 2 is a block diagram illustrating the individual components of the artificial brain, including the reward-modulated spike-timing-dependent plasticity (r-STDP) processes and their integration with sensory input and motor output pathways.

[0031] Figure 3 is a flowchart depicting the operational methodology of the system, detailing how sensory data is processed and utilized for decision-making in real-time.

[0032] Figure 4 is a graphical representation of the learning mechanisms employed by the system, emphasizing the adaptive capabilities through synaptic plasticity and reward based learning.

[0033] Detailed Description of the Embodiments:

[0034] Figure 1 illustrates the overall architecture of the artificial brain system, showcasing the main components, including spiking neural network layers, sensory input modules (including camera 1, microphone 2, lidar 3, etc.), visual memory 4, auditory memory 5, tactile memory 6, multiple processors 7 (CPU) and actuators 8 dedicated to reward modulation and adaptive learning. This figure highlights the layered organization inspired by the cortical connectome, emphasizing how information flows through interconnected neural pathways, reminiscent of biological brain structures. The actuators 8 move the wheels 9, arms 10, grippers 11 or any other equipments of robot according to the incoming signals and generate a feedback signal with each value measured by the sensors.

[0035] The layers mentioned above are input layer, hidden layer and output layer. Input layer captures and preprocesses sensory data from the robotic system's environment, translating it into spike trains that represent salient features of the input. By utilizing advanced sensory modalities, the system can adapt to a wide range of environmental conditions. Hidden layers composed of multiple neuron-like units, these layers leverage the principles of r-STDP to engage in continuous learning from past experiences. Each unit within these layers is interconnected based on a cortical connectome-inspired architecture, facilitating rich, hierarchical representations of information. This structure allows the system to optimize behavior through complex feedback loops, effectively simulating higher cognitive functions such as pattern recognition, abstraction, and contextual understanding. The output layer generates actions or responses based on the processed information, empowering the robot to engage effectively with its surroundings. The output can include a range of motor commands, decision-making processes, or communication signals, enabling nuanced interactions with the environment.

[0036] This invention describes a system and method of artificial brain for robotic systems that significantly advances robotic learning and adaptability by replicating the neuroplasticity and connectivity patterns observed in biological brains. The system leverages neuromorphic computing principles, such as optimized Reward-Modulated Spike- Timing-Dependent Plasticity (r-STDP) on every synapse, combined with insights from the cortical connectome — the detailed mapping of neural pathways within the brain. By modeling the structural and functional organization of the cortical connectome, the system creates a sophisticated, dynamic learning architecture that mimics the brain’s ability to reorganize and optimize neural pathways in response to external stimuli and feedback.

[0037] This invention enables robots to dynamically adapt and restructure their spiking neural networks, facilitating improved decision-making, motor control, and sensory integration. The system not only enhances a robot’s ability to learn from experience but also allows for more complex, context-sensitive behaviors through the incorporation of cortical-like connectivity patterns, leading to higher levels of autonomy and task optimization. This novel fusion of r-STDP with cortical connectome modeling marks a breakthrough in creating robotic systems capable of real-time adaptation, self-learning, and efficient resource management in complex environments.

[0038] The system is built upon a network of interconnected neuromorphic processors 7, each designed to emulate distinct cognitive and motor / actuator 8 functions by mirroring the organization and functionality of the cortical connectome — the intricate map of neural connections in the brain. These processors are linked through a network of spiking neurons that utilize temporal coding to efficiently transmit and process information, closely mimicking the dynamic communication patterns observed in spiking neural networks. The architecture is inherently modular and scalable, allowing seamless adaptation to robotic platforms of varying complexity, from simple task-driven robots to those requiring advanced sensory and cognitive capabilities.

[0039] At the heart of the system lies the reward-modulated spike-timing-dependent plasticity (r-STDP) mechanism tailored for the synaptic processes in our artificial brain, which enhances the adaptability of neuromorphic systems by dynamically adjusting synaptic weights in response to spike timing and external reward signals. This learning model, combined with the detailed emulation of the cortical connectome, enables robots to autonomously modify their neural pathways in response to external stimuli, resulting in superior decision-making, actuator 8 control, and sensory integration. This dynamic and biologically inspired approach allows the system to replicate high-level brain functions such as learning, adaptation, and self-optimization across a wide range of real-world environments and tasks.

[0040] Figure 2 shows a learning and adaptation method that enables the robot to dynamically engage with its environment through a sophisticated array of sensors, including high- resolution cameras 1, microphones 2, advanced tactile inputs 3 etc. The sensory data is processed by the artificial brain system, which is architected based on a cortical connectome that mimics the intricate neural wiring of biological brains. This connectome facilitates a rich, multidimensional representation of the robot’s interactions, allowing for the modulation of neural connections in real time in response to reward signals.

[0041] As the robot interacts with its environment, it not only refines its sensory processing capabilities but also cultivates optimized neural pathways for executing tasks, significantly enhancing its efficiency and responsiveness to environmental fluctuations. The r-STDP algorithm plays a crucial role in this process by modulating the timing of spikes between neurons with cortical connectome, dynamically strengthening or weakening synaptic connections based on the outcomes of various tasks. This approach not only fosters continuous learning but also enables the development of complex cognitive functions, empowering the robot to adapt its strategies and behaviors based on evolving conditions in its surroundings.

[0042] The system of the present invention includes a plurality of interconnected processors 7 that emulate biological neurons by receiving signals from environment with sensors and equipments, a memory module (including visual memory 4, auditory memory 5, tactile memory 6) stores synaptic weights and incorporates a sophisticated set of learning rules, including Reward-Modulated Spike-Timing-Dependent Plasticity (r-STDP), dynamically optimizing synaptic weights based on contextual sensory feedback and reward signals to reflect the adaptability of biological neural circuits, a control module designed to interpret complex input from various sensors and actuators 8, a learning engine that modulates neural activity and synaptic plasticity through advanced reinforcement signals, promoting enhanced adaptive behavior and allowing the robotic system to evolve its capabilities in alignment with environmental challenges and communication interfaces that enable seamless data transfer and control command exchange between the processors and robotic actuators. The processors 7 employ spike-based computation that closely simulates the timing, firing patterns, and connectivity principles of biological neurons and enhance the system’s ability to process sensory inputs and generate nuanced motor commands. The system's synaptic plasticity mechanism allows for real-time adjustment of synaptic weights among interconnected processing units based on temporal correlations in spiking activity.

[0043] The method of the present invention includes; receiving multifaceted sensory inputs from the robotic system’s environment; processing these sensory inputs through interconnected neuromorphic units that simulate the behavior of spiking neurons, informed by principles derived from the cortical connectome; dynamically adjusting synaptic weights according to r-STDP learning rules, reinforcing or weakening synaptic connections based on reward feedback to replicate the learning mechanisms of biological systems; generating refined motor control signals based on collective neural activity to elicit adaptive responses in the robotic system, reflecting the sophisticated integration found in biological organisms and continuously updating synaptic weights and modulating neural activity to enhance the performance of the robotic system through a continuous cycle of reinforcement learning.

Claims

CLAIMS1- An artificial brain system for robotic platforms, comprising;- a plurality of interconnected processors that emulate biological neurons by receiving signals from environment with sensors and equipments,- a memory module stores synaptic weights and incorporates a sophisticated set of learning rules, including Reward-Modulated Spike-Timing-Dependent Plasticity (r-STDP), dynamically optimizing synaptic weights based on contextual sensory feedback and reward signals to reflect the adaptability of biological neural circuits,- a control module designed to interpret complex input from various sensors and actuators,- a learning engine that modulates neural activity and synaptic plasticity through advanced reinforcement signals, promoting enhanced adaptive behavior and allowing the robotic system to evolve its capabilities in alignment with environmental challenges,- communication interfaces that enable seamless data transfer and control command exchange between the processors and robotic actuators.2- The artificial brain system for robotic platforms according to Claim 1, wherein it has processors employ spike-based computation that closely simulates the timing, firing patterns, and connectivity principles of biological neurons and enhance the system’s ability to process sensory inputs and generate nuanced motor commands.3- The artificial brain system for robotic platforms according to Claim 1, wherein it has synaptic plasticity mechanism allows for real-time adjustment of synaptic weights among interconnected processing units based on temporal correlations in spiking activity.4- The artificial brain system for robotic platforms according to Claim 1, wherein it has a hierarchical architecture where processing units are organized into specialized functional layers including encompassing sensory processing, motor controlling, and decisionmaking.5- A method for controlling a robotic system utilizing an artificial brain system, comprising the steps of;- receiving multifaceted sensory inputs from the robotic system’s environment,- processing these sensory inputs through interconnected neuromorphic units that simulate the behavior of spiking neurons, informed by principles derived from the cortical connectome,- dynamically adjusting synaptic weights according to r-STDP learning rules, reinforcing or weakening synaptic connections based on reward feedback to replicate the learning mechanisms of biological systems,- generating refined motor control signals based on collective neural activity to elicit adaptive responses in the robotic system, reflecting the sophisticated integration found in biological organisms,- continuously updating synaptic weights and modulating neural activity to enhance the performance of the robotic system through a continuous cycle of reinforcement learning.

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