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Quantitative Biology > Neurons and Cognition

Title: Passive nonlinear dendritic interactions as a general computational resource in functional spiking neural networks

Abstract: Nonlinear interactions in the dendritic tree play a key role in neural computation. Nevertheless, modeling frameworks aimed at the construction of large-scale, functional spiking neural networks, such as the Neural Engineering Framework, tend to assume a linear superposition of post-synaptic currents. In this paper, we present a series of extensions to the Neural Engineering Framework that facilitate the construction of networks incorporating Dale's principle and nonlinear conductance-based synapses. We apply these extensions to a two-compartment LIF neuron that can be seen as a simple model of passive dendritic computation. We show that it is possible to incorporate neuron models with input-dependent nonlinearities into the Neural Engineering Framework without compromising high-level function and that nonlinear post-synaptic currents can be systematically exploited to compute a wide variety of multivariate, bandlimited functions, including the Euclidean norm, controlled shunting, and non-negative multiplication. By avoiding an additional source of spike noise, the function-approximation accuracy of a single layer of two-compartment LIF neurons is on a par with or even surpasses that of two-layer spiking neural networks up to a certain target function bandwidth.
Subjects: Neurons and Cognition (q-bio.NC); Artificial Intelligence (cs.AI); Neural and Evolutionary Computing (cs.NE)
Cite as: arXiv:1904.11713 [q-bio.NC]
  (or arXiv:1904.11713v2 [q-bio.NC] for this version)

Submission history

From: Andreas Stöckel [view email]
[v1] Fri, 26 Apr 2019 08:32:29 GMT (1981kb)
[v2] Fri, 14 Aug 2020 01:38:19 GMT (2207kb)

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