Computing and Stability in Cortical Networks

By Peter E. Latham, Sheila Nirenberg. 2004

77 citations recorded by the source corpus.

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

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Citations in the corpus

Structural Synaptic Plasticity Has High Memory Capacity and Can Explain Graded Amnesia, Catastrophic Forgetting, and the Spacing Effect (2014, background): “support the sparsification of memory representations [82–85] and stabilize small cell assemblies that would appear unstable for a fixed low connectivity [86,87].”
The criticality hypothesis: how local cortical networks might optimize information processing (2008, background): “Networks operating between these two extremes, near the critical point, performed more effectively on a variety of computational tasks than networks that were tuned to have either subcritical or supercritical dynamics (see also Latham & Nirenberg 2004).”
Inhibitory Stabilization of the Cortical Network Underlies Visual Surround Suppression (2009, background): “Lateral inhibition may thus be implemented through a network mechanism that leads to a reduction in local recurrent excitation together with a paradoxical decrease in synaptic inhibition, rather than the expected increase in inhibition.”
Optimal Hierarchical Modular Topologies for Producing Limited Sustained Activation of Neural Networks (2009, unknown): “Connections between columns are modeled as exclusively excitatory, since it is appears to hold that there are no long-distance inhibitory connections within the cerebral cortex (Latham and Nirenberg, 2004).”
Balanced amplification: a new mechanism of selective amplification of neural activity patterns (2009, unknown): “…(Chagnac-Amitai and Connors, 1989; Haider et al., 2006; Shu et al., 2003; Higley and Contreras, 2006; Ozeki et al., 2009), an arrangement often considered by theorists (van Vreeswijk and Sompolinsky, 1998; Tsodyks et al., 1997; Latham and Nirenberg, 2004; Brunel, 2000; Lerchner et al., 2006).”
Memory Capacities for Synaptic and Structural Plasticity (2010, background): “The extreme sparsity requirement is problematic not only for applications (eg, see Rachkovskij & Kussul, 2001 ) but also for brain modeling because it is questionable whether neural cell assemblies that satisfy the sparseness condition are stable with realistic rates of spontaneous activity (Latham”