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Function approximation in inhibitory networks.

Bryan Tripp1, Chris Eliasmith2

  • 1Department of Systems Design Engineering, University of Waterloo, Canada; Centre for Theoretical Neuroscience, University of Waterloo, Canada.

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|March 11, 2016
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Summary
This summary is machine-generated.

Artificial neural networks can mimic complex brain functions by using neurons that are both excitatory and inhibitory. This study demonstrates how this can be achieved in biologically realistic neural networks, including those in the basal ganglia.

Keywords:
Basal gangliaDale’s principleFunction approximationInhibitionRecurrent dynamics

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Area of Science:

  • Computational Neuroscience
  • Artificial Intelligence
  • Neurobiology

Background:

  • Artificial neural networks often use neurons with both excitatory and inhibitory connections, unlike most biological neurons which are typically one or the other.
  • This difference presents a puzzle regarding computational constraints and potential physiological alternatives.

Purpose of the Study:

  • To investigate if mixed excitatory and inhibitory functional connections, common in artificial neural networks, can be realized in biologically plausible neural networks.
  • To extend previous work showing this is possible in excitatory-dominant networks to inhibition-dominant networks, such as those in the basal ganglia.
  • To assess the computational capacity and dynamic properties of these realized mixed connections.

Main Methods:

  • Building upon Parisien et al. (2008), the study models how mixed functional connections can be achieved using purely excitatory and inhibitory pathways.
  • The research extends these models to networks with a high proportion of inhibitory neurons, simulating basal ganglia circuitry.
  • The functional capacity and dynamics of these networks were analyzed, including their viability in recurrent network configurations.

Main Results:

  • Mixed excitatory and inhibitory functional connections can be effectively realized in inhibition-dominated neural networks, mirroring basal ganglia architecture.
  • The function-approximation capacity of these biologically plausible mixed connections is comparable to idealized mixed-weight connections.
  • Recurrent networks utilizing these realized mixed connections demonstrate flexible and diverse dynamic behaviors.

Conclusions:

  • The findings suggest that the brain, particularly structures like the basal ganglia, may implement complex computations using networks that efficiently achieve mixed excitatory and inhibitory functional connectivity.
  • This work provides a new framework for understanding neural computation in the basal ganglia and potentially in inhibitory cortical circuits.
  • The study bridges the gap between artificial neural network architectures and biological neural realism, offering insights into neural processing mechanisms.