Submit a preprint


Nonlinear computations in spiking neural networks through multiplicative synapsesuse asterix (*) to get italics
Michele Nardin, James W Phillips, William F Podlaski, Sander W KeeminkPlease use the format "First name initials family name" as in "Marie S. Curie, Niels H. D. Bohr, Albert Einstein, John R. R. Tolkien, Donna T. Strickland"
<p>The brain efficiently performs nonlinear computations through its intricate networks<br>of spiking neurons, but how this is done remains elusive. While nonlinear computations<br>can be implemented successfully in spiking neural networks, this requires supervised<br>training and the resulting connectivity can be hard to interpret. In contrast, the<br>required connectivity for any computation in the form of a linear dynamical system can<br>be directly derived and understood with the spike coding network (SCN) framework.<br>These networks also have biologically realistic activity patterns and are highly robust to<br>cell death. Here we extend the SCN framework to directly implement any polynomial<br>dynamical system, without the need for training. This results in networks requiring<br>a mix of synapse types (fast, slow, and multiplicative), which we term multiplicative<br>spike coding networks (mSCNs). Using mSCNs, we demonstrate how to directly derive<br>the required connectivity for several nonlinear dynamical systems. We also show how<br>to carry out higher-order polynomials with coupled networks that use only pair-wise<br>multiplicative synapses, and provide expected numbers of connections for each synapse<br>type. Overall, our work demonstrates a novel method for implementing nonlinear<br>computations in spiking neural networks, while keeping the attractive features of<br>standard SCNs (robustness, realistic activity patterns, and interpretable connectivity).<br>Finally, we discuss the biological plausibility of our approach, and how the high accuracy<br>and robustness of the approach may be of interest for neuromorphic computing.</p>
You should fill this box only if you chose 'All or part of the results presented in this preprint are based on data'. URL must start with http:// or https://
You should fill this box only if you chose 'Scripts were used to obtain or analyze the results'. URL must start with http:// or https:// should fill this box only if you chose 'Codes have been used in this study'. URL must start with http:// or https://
nonlinear computation, spiking neural network, robustness, multiplicative synapses
NonePlease indicate the methods that may require specialised expertise during the peer review process (use a comma to separate various required expertises).
Synapse, Systems/Circuit Neuroscience
No need for them to be recommenders of PCI Neuro. Please do not suggest reviewers for whom there might be a conflict of interest. Reviewers are not allowed to review preprints written by close colleagues (with whom they have published in the last four years, with whom they have received joint funding in the last four years, or with whom they are currently writing a manuscript, or submitting a grant proposal), or by family members, friends, or anyone for whom bias might affect the nature of the review - see the code of conduct
e.g. John Doe []
2021-04-07 17:38:49
Marco Leite