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Building functional networks of spiking model neurons

Abstract

Most of the networks used by computer scientists and many of those studied by modelers in neuroscience represent unit activities as continuous variables. Neurons, however, communicate primarily through discontinuous spiking. We review methods for transferring our ability to construct interesting networks that perform relevant tasks from the artificial continuous domain to more realistic spiking network models. These methods raise a number of issues that warrant further theoretical and experimental study.

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Figure 1: Structure of autonomous and driven networks.
Figure 2: Driven networks approximating a continuous target output.
Figure 3: Two autonomous networks of spiking neurons constructed to integrate the input fin (top, black traces).
Figure 4: Autonomous networks solving a temporal XOR task.

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Acknowledgements

We thank C. Machens, M. Churchland and D. Thalmeier for helpful discussions. Our research in this area was supported by US National Institutes of Health grant MH093338, the Gatsby Charitable Foundation through the Gatsby Initiative in Brain Circuitry at Columbia University, the Simons Foundation, the Swartz Foundation, the Harold and Leila Y. Mathers Foundation, the Kavli Institute for Brain Science at Columbia University, the Max Kade Foundation and the German Federal Ministry of Education and Research BMBF through the Bernstein Network (Bernstein Award 2014).

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Abbott, L., DePasquale, B. & Memmesheimer, RM. Building functional networks of spiking model neurons. Nat Neurosci 19, 350–355 (2016). https://doi.org/10.1038/nn.4241

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