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A global framework for a systemic view of brain modeling.

Frederic Alexandre^1,2,3

¹INRIA Bordeaux Sud-Ouest, Talence, France. Frederic.Alexandre@inria.fr.

Brain Informatics

|February 16, 2021

Summary

This summary is machine-generated.

This study presents a systemic brain theory integrating sensorimotor loops and memory systems. It offers a framework for understanding behavior, cognition, and autonomous learning in both brains and machines.

Keywords:

Brain modeling Cognitive functions Memory system

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Area of Science:

Neuroscience
Computational Neuroscience
Cognitive Science

Background:

The brain's complexity arises from its heterogeneous structure and diverse functions.
Understanding brain function requires a framework integrating global information flows and cognitive processes.

Purpose of the Study:

To present a systemic description of the brain as a contribution to brain theory.
To provide a general framework for integrating computational neuroscience models with cognitive functions.
To offer an enactive view integrating sensorimotor loops with internal and external worlds.

Main Methods:

Describing behavior from a survival-oriented perspective.
Integrating Pavlovian and instrumental conditioning with semantic, episodic, and working memory.
Viewing cognition as a dynamical system of interacting memories.

Main Results:

The framework explains diverse response preparations, from reflexes to goal-directed planning.
It highlights the coexistence, collaboration, and competition of different response types.
Cognition is described as a dynamical system where memories interact and influence each other.

Conclusions:

The proposed brain architecture of learning systems has implications for Machine Learning.
A biologically informed view of conditioning can enhance Reinforcement Learning.
This framework provides a basis for developing truly autonomous learning systems.