Matching Recall and Storage in Sequence Learning with Spiking Neural Networks

By Johanni Brea, Walter Senn, Jean-Pascal Pfister. 2013

118 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Cognitive Neuroscience, Electrical and Electronic Engineering

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Citations in the corpus

Deep learning in neural networks: An overview (2014, methodology): “Many computational models of such spiking neurons have been proposed and analyzed (e.g., Gerstner and van Hemmen, 1992; Zipser et al., 1993; Stemmler, 1996; Tsodyks et al., 1996; Maex and Orban, 1996; Maass, 1996, 1997; Kistler et al., 1997; Amit and Brunel, 1997; Tsodyks et al., 1998; Kempter et al”
Eligibility Traces and Plasticity on Behavioral Time Scales: Experimental Support of NeoHebbian Three-Factor Learning Rules (2018, background): “But there are also existing models, where the implementation of a certain function requires the possibility that one subpopulation has plasticity rules under a third factor whereas another one does not (Brea et al., 2013; Rezende and Gerstner, 2014).”
Synaptic Consolidation: From Synapses to Behavioral Modeling (2015, background): “…could also represent novelty, surprise, or “interestingness” of a stimulus (Redgrave and Gurney, 2006; Bethus et al., 2010; Lisman et al., 2011), similar to classic (Carpenter and Grossberg, 1987) or modern (Schmidhuber, 2006; Brea et al., 2013; Rezende and Ger-stner, 2014) learning theories.”
Surrogate Gradient Learning in Spiking Neural Networks: Bringing the Power of Gradient-based optimization to spiking neural networks (2019, background): “In a similar vein, variational learning approaches were shown to be capable of learning useful hidden-layer representations in SNNs [27]–[29].”
SuperSpike: Supervised Learning in Multilayer Spiking Neural Networks (2017, background): “The problem of sequence learning in recurrent neural networks has also been studied as a variational learning problem [34, 35] and by combining adaptive control theory with heterogeneous neurons [36].”
Stochastic variational learning in recurrent spiking networks (2014, methodology): “These interesting properties of the variational approximation have been explored by a diversity of models (Dayan, 2000; Friston and Stephan, 2007; Jimenez Rezende et al., 2011; Brea et al., 2013; Nessler et al., 2013).”
Neuromodulated Spike-Timing-Dependent Plasticity, and Theory of Three-Factor Learning Rules (2016, background): “…learning in neural circuits so that a formal neural network memorizes novel stimuli or sequences, (Brea et al., 2011; Rezende et al., 2011; Brea et al., 2013; Rezende and Gerstner, 2014) or learns rewarding skills (Xie and Seung, 2004; Pfister et al., 2006; Baras and Meir, 2007; Farries…”