Chaotic balanced state in a model of cortical circuits
Neural Computation
Impact of Correlated Inputs on the Output of the Integrate-and-Fire Model
Neural Computation
Memory Capacity of Balanced Networks
Neural Computation
Signal propagation in feedforward neuronal networks with unreliable synapses
Journal of Computational Neuroscience
Detection of M-sequences from spike sequence in neuronal networks
Computational Intelligence and Neuroscience - Special issue on Computational Intelligence in Biomedical Science and Engineering
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We investigate the formation of synfire waves in a balanced network of integrate-and-fire neurons. The synaptic connectivity of this network embodies synfire chains within a sparse random connectivity. This network can exhibit global oscillations but can also operate in an asynchronous activity mode. We analyze the correlations of two neurons in a pool as convenient indicators for the state of the network. We find, using different models, that these indicators depend on a scaling variable.Beyond a critical point, strong correlations and large network oscillations are obtained. We looked for the conditions under which a synfire wave could be propagated on top of an otherwise asynchronous state of the network. This condition was found to be highly restrictive, requiring a large number of neurons for its implementation in our network. The results are based on analytic derivations and simulations.