Synaptic mechanisms and network dynamics underlying spatial working memory in a cortical network model.

By Albert Compte, Nicolas J.-B. Brunel, Patricia S. Goldman-Rakic, Xiao-Jing Wang. Cerebral Cortex, 2000

1,158 citations recorded by the source corpus.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience

View Paper: https://www.semanticscholar.org/paper/a4566401dba2111b30d55e8fc3b9b94412166ab0

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cellular and Molecular Neuroscience

In Cognitive Neuroscience

Citations in the corpus

Integrated Neural Processes for Defining Potential Actions and Deciding between Them: A Computational Model (2006, neutral): “Foramore detailedmodelofPFCworkingmemory,see Compteetal.”
Neural network dynamics. (2005, background): “…of a symmetry, and anything that breaks this symmetry, sensitivity of these models to small symmetry-breaking effects (Camperi & Wang 1998, Compte et al. 2000, Seung et al. 2000, Tegner et al. 2002, Renart et al. 2003), but the general even in the simple case of ﬁxed patterns of activity.”
A review of the integrate-and-fire neuron model: II. Inhomogeneous synaptic input and network properties (2006, methodology): “One such model is the Hopﬁeld model of associative memory (Hopﬁeld 1982), which has been subsequently considerably elaborated as a model of working memory (Compte et al. 2000; Amit and Mongillo 2003; Mongillo and Amit 2003; Giudice et al. 2003; Mongillo and Amit 2005).”
Attractor and integrator networks in the brain (2021, background): “all noise orthogonal to the manifold is corrected; thus, there is a net reduction of the effects of noise by the factor ≪ K N ~ / 1 (refs.(45,55)).”
Neurophysiological and Computational Principles of Cortical Rhythms in Cognition (2010, background): “Interestingly, in biophysically realistic models of working memory (182, 939, 1029), highly recurrent networks naturally give rise to fast coherent oscillations.”
Efficient codes and balanced networks (2016, background): “However, any increase in the excitatory drive will break the E/I balance and therefore lead to more regular firing 10,64 ( Fig.”
Synaptic Plasticity in Neural Networks Needs Homeostasis with a Fast Rate Detector (2013, methodology): “We used a generic balanced network model [7,10,44–46] to mimic brain-like spiking activity in a recurrent neural network.”
Vagal influence on working memory and attention. (2003, background): “Compte et al. (2000) have proposed that the prefrontal cortex holds sensory information temporarily online through sustained activity.”
Bridging the representational gap in the dynamic systems approach to development (2003, positive): “In addition to this population coding approach, the general principles of dynamic fields can be usefully integrated with a more biophysical approach that attempts to incorporate the details of neurotransmitter action, timing properties of neurons, etc. (Compte et al., 2000).”