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Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
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BrainCog: A spiking neural network based, brain-inspired cognitive intelligence engine for brain-inspired AI and
Yi Zeng1,2,3,4,5, Dongcheng Zhao1, Feifei Zhao1
1Brain-inspired Cognitive Intelligence Lab, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.
Patterns (New York, N.Y.)
|August 21, 2023
Summary
BrainCog is a new platform for brain-inspired artificial intelligence (AI) and brain simulation. It integrates biological components to enable advanced AI models and cognitive function research.
Area of Science:
- Neuroscience
- Artificial Intelligence
- Computational Neuroscience
Background:
- Spiking neural networks (SNNs) offer a framework for brain-inspired AI and simulation.
- Current SNN infrastructures support either simulation or AI, not both.
- A unified platform is needed to bridge biological intelligence and AI development.
Purpose of the Study:
- Introduce BrainCog, a novel SNN-based platform.
- Provide infrastructure for both brain simulation and brain-inspired AI.
- Facilitate research into biological intelligence and AI creation.
Main Methods:
- BrainCog integrates diverse biological neuron models, encoding strategies, and learning rules.
- The platform incorporates various brain areas and supports hardware-software co-design.
- It offers user-friendly components for building complex cognitive functions.
Main Results:
- BrainCog supports simulations across multiple scales, from single neurons to brain structures.
- It enables the implementation of cognitive functions like perception, decision-making, and social cognition.
- The BORN AI engine demonstrates BrainCog's capability in building advanced AI models.
Conclusions:
- BrainCog provides a unified SNN infrastructure for brain simulation and AI.
- It facilitates the study of biological intelligence and the development of advanced AI.
- The platform supports a wide range of cognitive functions and multi-scale simulations.
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