Passive nonlinear dendritic interactions as a general computational resource in functional spiking neural networks
This addresses a bottleneck in neural modeling for computational neuroscience and neuromorphic engineering by enabling more efficient function approximation in spiking networks.
The paper tackled the problem of incorporating nonlinear dendritic interactions into large-scale spiking neural networks, showing that a single layer of two-compartment LIF neurons can match or surpass the function-approximation accuracy of two-layer networks up to a certain bandwidth.
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.