2007 Special Issue: Edge of chaos and prediction of computational performance for neural circuit models

By Robert A. Legenstein, Wolfgang Maass. 2007

474 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/e7f630e9a2a9c1b1bd6d585c49edc40be77c9e0b

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In Artificial Intelligence

In Cognitive Neuroscience

In Electrical and Electronic Engineering

Citations in the corpus

Neural syntax: cell assemblies, synapsembles and readers (2010, unknown): “…of neuronal patterns can be strengthened by reinforcers (i.e., goals), which optimize the connectivity of the upstream assembly post-hoc so that it will activate the reader more effectively on future occasions (Izhikevich, 2007; Legenstein and Maass, 2007; Maass et al., 2002; Seung, 2003).”
Criticality in the brain: A synthesis of neurobiology, models and cognition. (2017, unknown): “…(Assis and Copelli, 2008; Boedecker et al., 2012; Deco et al., 2013; Gollo et al., 2013; Haldeman and Beggs, 2005; Hidalgo et al., 2014; Kastner et al., 2015; Legenstein and Maass, 2007; Livi et al., 2017; Mosqueiro and Maia, 2013; Publio et al., 2012; Schrauwen et al., 2009; Shew and Plenz, 2013).”
The criticality hypothesis: how local cortical networks might optimize information processing (2008, background): “Neural models have also picked up on this idea, and several authors have suggested that neural networks should operate close to the chaotic regime (Chialvo & Bak 1999; Bak & Chialvo 2001; Bertschinger & Natschlager 2004; Lin & Chen 2006; Legenstein & Maass 2007).”
Complementary roles of dimensionality and population structure in neural computations (2020, background): “A key hypothesis emerging from the collective dynamics paradigm states that the dimensionality of neural trajectories determines the complexity of the computations a network can implement [Legenstein and Maass, 2007; Buonomano and Maass, 2009; Rigotti et al., 2013; Fusi et al., 2016].”
Recent Advances in Physical Reservoir Computing: A Review (2018, background): “The measures called kernel quality and generalization ability (Büsing, Schrauwen, & Legenstein, 2010; Legenstein & Maass, 2007) are also useful for evaluating the effectiveness of a physical reservoir.”
Colloquium: Criticality and dynamical scaling in living systems (2017, background): “…such machines can perform real-time computations –responding rapidly to time varying input signals– in a coherent yet flexible way if they operate near a critical point (Bertschinger and Natschlager, 2004; Boedecker et al., 2012; Legenstein and Maass, 2007; Legenstein, 2005; Maass et al., 2002).”
At the Edge of Chaos: How Cerebellar Granular Layer Network Dynamics Can Provide the Basis for Temporal Filters (2015, methodology): “Although the edge-of-chaos criterion is not a universal predictor of maximal computational performance [22] we find that it applies for most of the configurations considered here.”
Neuronal Avalanches Imply Maximum Dynamic Range in Cortical Networks at Criticality (2009, background): “…map input differences into distinguishable network outputs, our result is also closely related to network-mediated separation, which has been predicted to peak in the critical state, at the transition from ordered to chaotic dynamics (Bertschinger and Natschlager, 2004;Legenstein and Maass, 2007).”