The Brain as an Efficient and Robust Adaptive Learner.

Area of Science:

• Neuroscience
• Computational Neuroscience
• Computational Biology

Background:

• The brain's ability to learn complex functions from noisy spiking activity is a key challenge.
• Recurrent neural networks are models for sensory and motor tasks, but face the credit assignment problem for learning.
• Synaptic plasticity rules struggle to assign credit for errors in recurrent networks due to local information constraints.

Purpose of the Study:

• To propose a model for how neural circuits can learn complex dynamical tasks using local synaptic plasticity.
• To integrate adaptive control and efficient coding theories to explain neural learning mechanisms.
• To demonstrate that biologically plausible mechanisms can overcome the credit assignment problem.

Main Methods:

• Combining adaptive control theory and efficient coding principles.
• Modeling recurrent neural networks with specific biologically inspired mechanisms.
• Analyzing the role of top-down feedback and excitation-inhibition balance in synaptic plasticity.

Main Results:

• Neural circuits can learn arbitrary dynamical systems with local plasticity rules.
• The proposed model associates top-down feedback with balanced excitation-inhibition.
• Networks produce irregular spike trains, mimicking experimental observations.
• Population-level computation is efficient and robust despite single-neuron variability.

Conclusions:

• Local synaptic plasticity, guided by top-down feedback and E-I balance, enables learning of complex dynamic tasks.
• This framework addresses the credit assignment problem in recurrent neural networks.
• Neural variability at the single-neuron level may underlie robust population-level computation.