Abstract
Populations of neurons display an extraordinary diversity in the behaviors they affect and display. Machine learning techniques have recently emerged that allow us to create networks of model neurons that display behaviors of similar complexity. Here we demonstrate the direct applicability of one such technique, the FORCE method, to spiking neural networks. We train these networks to mimic dynamical systems, classify inputs, and store discrete sequences that correspond to the notes of a song. Finally, we use FORCE training to create two biologically motivated model circuits. One is inspired by the zebra finch and successfully reproduces songbird singing. The second network is motivated by the hippocampus and is trained to store and replay a movie scene. FORCE trained networks reproduce behaviors comparable in complexity to their inspired circuits and yield information not easily obtainable with other techniques, such as behavioral responses to pharmacological manipulations and spike timing statistics.
Highlights
Populations of neurons display an extraordinary diversity in the behaviors they affect and display
We explored the potential of the FORCE method in training spiking neural networks to perform an arbitrary task
We have shown that FORCE training can take initially chaotic networks of spiking neurons and use them to mimic the natural tasks and functions demonstrated by populations of neurons
Summary
Populations of neurons display an extraordinary diversity in the behaviors they affect and display. We demonstrate the direct applicability of one such technique, the FORCE method, to spiking neural networks We train these networks to mimic dynamical systems, classify inputs, and store discrete sequences that correspond to the notes of a song. A broad class of techniques have been derived that allow us to enforce a certain behavior or dynamics onto a neural network[1,2,3,4,5,6,7,8,9,10] These top-down techniques start with an intended task that a recurrent spiking neural network should perform, and a ij =. In order to apply either approach, the task has to be specified in terms of closed-form differential equations This is a constraint on the potential problems these networks can solve Both the NEF and spike-based coding techniques have led to a resurgence in the top-down analysis of network function[3]
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