Toward an Integration of Deep Learning and Neuroscience

Area of Science:

• Neuroscience
• Machine Learning
• Computational Neuroscience

Background:

• Neuroscience traditionally focuses on neural codes, dynamics, and circuits.
• Machine learning (ML) often uses uniform architectures and brute-force optimization.
• Recent ML advances include structured architectures (attention, memory) and complex, layered cost functions.

Purpose of the Study:

• To re-examine the brain through the lens of recent ML developments.
• To hypothesize that the brain optimizes diverse, location- and time-dependent cost functions within a pre-structured architecture.
• To bridge the gap between neuroscience and ML perspectives on computation and learning.

Main Methods:

• Conceptual framework integrating ML concepts (structured architectures, cost functions) with neuroscience.
• Hypothesizing brain function based on ML principles.
• Interpreting neural circuitry and specialized brain systems as enabling efficient optimization.

Main Results:

• Proposes that the brain optimizes multiple, interacting cost functions.
• Suggests brain architecture is pre-structured for specific computational problems.
• Argues that diverse optimization strategies enhance data efficiency and behavioral relevance.

Conclusions:

• The brain can be viewed as a heterogeneously optimized system.
• This perspective aligns with neural circuitry and specialized brain systems.
• Suggests testable hypotheses for neuroscience research to refine and validate these ideas.