Mean Field and Capacity in Realistic Networks of Spiking Neurons Storing Sparsely Coded Random Memories

DOI10.1162/0899766042321805zbMath1061.92018OpenAlexW2143373656WikidataQ42163407 ScholiaQ42163407MaRDI QIDQ4664515

Giancarlo La Camera, Daniel J. Amit, Emanuele Curti, Gianluigi Mongillo

Publication date: 5 April 2005

Published in: Neural Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1162/0899766042321805

Mathematics Subject Classification ID

Neural biology (92C20) Neural nets applied to problems in time-dependent statistical mechanics (82C32)

Related Items (5)

Fast and Robust Learning by Reinforcement Signals: Explorations in the Insect Brain ⋮ The dynamics of memory retrieval in hierarchical networks ⋮ Mean-field analysis of selective persistent activity in presence of short-term synaptic depression ⋮ The response of cortical neurons to in vivo-like input current: Theory and experiment ⋮ Unnamed Item

Cites Work

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• Slow stochastic Hebbian learning of classes of stimuli in a recurrent neural network
• Dynamics of a recurrent network of spiking neurons before and following learning
• Constraints on learning in dynamic synapses
• Spike-Driven Synaptic Dynamics Generating Working Memory States
• Quantitative study of attractor neural network retrieving at low spike rates: I. substrate—spikes, rates and neuronal gain
• Neural networks and physical systems with emergent collective computational abilities.
• Neurons with graded response have collective computational properties like those of two-state neurons.
• Characteristics of Random Nets of Analog Neuron-Like Elements

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