Polychronization: Computation with Spikes

By Eugene M. Izhikevich. 2006

1,150 citations recorded by the source corpus.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience, Electrical and Electronic Engineering

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Citations in the corpus

Born to Learn: the Inspiration, Progress, and Future of Evolved Plastic Artificial Neural Networks (2017, background): “ Hamker, 2015). Finally, plasticity rules for spiking neural networks (Maass and Bishop, 2001) aim to demonstrate unique learning mech5 anisms that emerge from spiking dynamics (Markram et al., 1997; Izhikevich, 2006, 2007), as well as model biological synaptic plasticity (Gerstner and Kistler, 2002”
Toward an Integration of Deep Learning and Neuroscience (2016, background): “…electrical connections, transient synaptic dynamics, excitatory/inhibitory balance, spontaneous oscillatory activity, axonal conduction delays (Izhikevich, 2006) and others, will inﬂuence what and how such networks learn. the thalamus appear to have the information from other areas ﬂowing…”
Synchronization and desynchronization in epilepsy: controversies and hypotheses (2012, background): “Synchrony, the concept of a stable functional relationship where elements do similar things at the same time, needs to be broadened to that of polychronicity in neural networks, when time lags must be incorporated into the firing time of each neuron as population activity is considered (Izhikevich 2”
Loihi: A Neuromorphic Manycore Processor with On-Chip Learning (2018, background): “This provides support for novel forms of temporal computation such as polychronous dynamics, 10 in which subsets of neurons might synchronize over periods of varying timescales.”
NeuCube: A spiking neural network architecture for mapping, learning and understanding of spatio-temporal brain data (2014, neutral): “…models, alongwith their respective applications, have been already developed and explored (Bohte, 2004; Bohte, Kok, & La Poutre, 2005; Brette et al., 2007 Gerstner, 1995, 2001; Hodgkin & Huxley, 1952; Hopfield, 1982; Izhikevich, 2006; Maass, Natschlaeger, & Markram, 2002; Thorpe & Gautrais, 1998.”
ROBUST TIMING AND MOTOR PATTERNS BY TAMING CHAOS IN RECURRENT NEURAL NETWORKS (2012, background): “Some studies have incorporated more realistic learning rules in recurrent networks, but these are only transiently active or are not robust to high noise (Fiete et al., 2010; Izhikevich, 2006; Liu and Buonomano, 2009).”
SpiNNaker: Mapping neural networks onto a massively-parallel chip multiprocessor (2008, background): “At the same time it does not exhibit any critical model dependence: the hardware does not embed the characteristics of a particular neural model into its design, and so it is able to accomodate new and developing models of neuronal computation, e.g. [17].”
Evolving spiking neural networks for audiovisual information processing (2010, background): “Izhikevich (2006) created the term ‘‘polychronization’’ to define the spatio-temporal behaviour of a group of neurons that are ‘‘time-locked’’ to each other, a term to distinguish it from synchronous or asynchronous spiking activity behaviour.”
Evolvable Neuronal Paths: A Novel Basis for Information and Search in the Brain (2011, background): “The same supra-astronomical property has been described for the more complex organizations known as polychronous groups (stereotyped neuronal spike patterns) observed by Eugene Izhikevich in recent models of spiking neuronal networks with delays [8].”
Decoupling through synchrony in neuronal circuits with propagation delays. (2008, positive): “To illustrate how the opposing forces of Hebbian STDP interact within a circuit, we consider a randomly driven recurrent network of excitatory regular spiking (RS) Izhikevich neurons with axonal delays (Figure 4) (Izhikevich, 2003, 2006).”
A review of learning in biologically plausible spiking neural networks (2019, neutral): “The existence of synaptic delay in the mammalian neocortex is proven by experimental research and depending on the type and location of the neurons, the synaptic delay could be as short as 0.1 ms and as long as 40 ms (Izhikevich, 2006; Paugam-Moisy, Hélène, Martinez, & Bengio, 2008; Swadlow, 1992).”
Large-scale model of mammalian thalamocortical systems (2008, background): “It is known (11, 25, 26) that simple models of spiking networks can self-organize to exhibit collective delta-, alpha-, and gammafrequency rhythms.”
Broadcast and Weight: An Integrated Network For Scalable Photonic Spike Processing (2014, background): “The asynchronous nature of broadcast-andweight provides a mechanism to perhaps even exploit heterogeneity in spike timings in order to implement advanced spatiotemporal algorithms, such as [71].”
Competitive STDP-Based Spike Pattern Learning (2009, background): “Subsequent neurons with appropriate conduction delays could easily code for the whole patterns at low cost (Izhikevich, 2006).”