9,182 research outputs found
Sensitivity analysis of oscillator models in the space of phase-response curves: Oscillators as open systems
Oscillator models are central to the study of system properties such as
entrainment or synchronization. Due to their nonlinear nature, few
system-theoretic tools exist to analyze those models. The paper develops a
sensitivity analysis for phase-response curves, a fundamental one-dimensional
phase reduction of oscillator models. The proposed theoretical and numerical
analysis tools are illustrated on several system-theoretic questions and models
arising in the biology of cellular rhythms
SuperSpike: Supervised learning in multi-layer spiking neural networks
A vast majority of computation in the brain is performed by spiking neural
networks. Despite the ubiquity of such spiking, we currently lack an
understanding of how biological spiking neural circuits learn and compute
in-vivo, as well as how we can instantiate such capabilities in artificial
spiking circuits in-silico. Here we revisit the problem of supervised learning
in temporally coding multi-layer spiking neural networks. First, by using a
surrogate gradient approach, we derive SuperSpike, a nonlinear voltage-based
three factor learning rule capable of training multi-layer networks of
deterministic integrate-and-fire neurons to perform nonlinear computations on
spatiotemporal spike patterns. Second, inspired by recent results on feedback
alignment, we compare the performance of our learning rule under different
credit assignment strategies for propagating output errors to hidden units.
Specifically, we test uniform, symmetric and random feedback, finding that
simpler tasks can be solved with any type of feedback, while more complex tasks
require symmetric feedback. In summary, our results open the door to obtaining
a better scientific understanding of learning and computation in spiking neural
networks by advancing our ability to train them to solve nonlinear problems
involving transformations between different spatiotemporal spike-time patterns
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