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Structural Plasticity Denoises Responses and Improves Learning Speed.

Robin Spiess1, Richard George2, Matthew Cook2

  • 1Department of Computer Science, Swiss Federal Institute of Technology (ETH Zurich) Zurich, Switzerland.

Frontiers in Computational Neuroscience
|September 24, 2016
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Summary
This summary is machine-generated.

Structural plasticity, the creation and elimination of synapses, enhances learning speed and reduces noise in neural networks. Combining it with spike-timing-dependent plasticity (STDP) improves performance and resource efficiency.

Keywords:
STDPhomoeostasislearningspiking neural networkstructural plasticity

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Area of Science:

  • Computational Neuroscience
  • Artificial Intelligence
  • Neuromorphic Engineering

Background:

  • Limited research exists on structural plasticity in computational models compared to synaptic weight learning.
  • The interplay between structural plasticity and spike-timing-dependent plasticity (STDP) remains unclear.

Purpose of the Study:

  • To investigate the combined effects of structural plasticity and STDP on learning and inference.
  • To evaluate the impact of structural plasticity on network response error, noise, and learning speed.
  • To explore the efficiency of structural plasticity in resource-limited scenarios.

Main Methods:

  • A large-scale functional model using leaky integrate-and-fire neurons, STDP, homeostasis, and recurrent connections.
  • Implementation of structural plasticity, including synapse creation and elimination (pruning).
  • Comparison of network performance with and without structural plasticity for input encoding and inference tasks.

Main Results:

  • Structural plasticity significantly reduces response noise and error in learning input representations.
  • Networks trained with structural plasticity demonstrate improved performance in inferring missing inputs.
  • Structural plasticity combined with pruning accelerates learning speed for inference tasks.

Conclusions:

  • Structural plasticity enhances STDP-based learning by improving accuracy and reducing noise.
  • This approach offers significant advantages in speed and performance, particularly in resource-constrained environments.
  • Structural plasticity provides an efficient method for synapse management without performance degradation, applicable to biological and artificial neural systems.