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Parallel synapses with transmission nonlinearities enhance neuronal classification capacity.

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Neurons use multiple nonlinear parallel synapses to boost computational power beyond simple models. This enhances classification accuracy in neural networks, even with few synapses per axon.

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Machine Learning

Background:

  • Cortical neurons form multiple synaptic connections on a single postsynaptic neuron.
  • Functional redundancy is avoided if parallel synapses have distinct computational properties.

Purpose of the Study:

  • To model the computational properties of nonlinear parallel synapses.
  • To evaluate the enhanced classification capacity of neurons with these synapses.
  • To assess the impact on neural network performance.

Main Methods:

  • Modeling individual synapses as sigmoidal transmission functions with learnable parameters (amplitude, slope, threshold).
  • Analyzing the classification capacity of a neuron with nonlinear parallel synapses compared to the Perceptron.
  • Applying the model to a feedforward neural network for MNIST image classification.

Main Results:

  • Neurons with nonlinear parallel synapses exhibit significantly higher classification capacity than the Perceptron.
  • Classification accuracy increases superlinearly with the number of presynaptic axons.
  • The model neuron can implement arbitrary monotonic aggregate transmission functions.
  • Applying parallel synapses to a feedforward network improved MNIST classification accuracy.

Conclusions:

  • Multiple nonlinear synapses per input axon substantially enhance a neuron's computational power.
  • Nonlinear parallel synapses offer a mechanism to increase neural computation efficiency.
  • This synaptic architecture has potential applications in artificial neural networks and understanding biological computation.