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Area of Science:

  • Computational Neuroscience
  • Information Theory

Background:

  • Rate distortion theory optimizes information transmission under capacity limits.
  • Bounded rational decision making models limited information processing in biological systems.
  • Spiking neurons process sensory input and generate motor output.

Purpose of the Study:

  • To interpret a spiking neuron as a bounded rational decision maker.
  • To maximize expected reward under a constrained mutual information between input and output.
  • To derive a synaptic weight update rule for rate distortion optimizing neurons.

Main Methods:

  • Modeling spiking neurons using rate distortion theory.
  • Formulating an objective function balancing reward and information cost.
  • Deriving a synaptic weight update rule based on firing behavior deviation.
  • Simulating the neuron's information extraction and reward optimization.

Main Results:

  • The computational constraint translates to penalizing deviations in firing behavior.
  • A novel synaptic weight update rule was derived.
  • Simulations demonstrated efficient extraction of reward-relevant information.
  • The neuron effectively traded off synaptic strengths against collected reward.

Conclusions:

  • Spiking neurons can be understood as optimizing reward under information processing constraints.
  • The derived update rule enables efficient, adaptive information processing in neurons.
  • This framework provides insights into neural computation and decision making.