Populations of spiking neurons
Pulsed neural networks
Impact of Correlated Inputs on the Output of the Integrate-and-Fire Model
Neural Computation
On embedding synfire chains in a balanced network
Neural Computation
Self-organizing dual coding based on spike-time-dependent plasticity
Neural Computation
Generation of Synthetic Spike Trains with Defined Pairwise Correlations
Neural Computation
The high-conductance state of cortical networks
Neural Computation
Correlations and population dynamics in cortical networks
Neural Computation
Stimulus-dependent correlations in threshold-crossing spiking neurons
Neural Computation
Conditional mixture model for correlated neuronal spikes
Neural Computation
Mechanisms that modulate the transfer of spiking correlations
Neural Computation
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We investigate the firing characteristics of conductance-based integrate-and-fire neurons and the correlation of firing for uncoupled pairs of neurons as a result of common input and synchronous firing of multiple synaptic inputs. Analytical approximations are derived for the moments of the steady state potential and the effective time constant. We show that postsynaptic firing barely depends on the correlation between inhibitory inputs; only the inhibitory firing rate matters. In contrast, both the degree of synchrony and the firing rate of excitatory inputs are relevant. A coefficient of variation CV 1 can be attained with low inhibitory firing rates and (Poisson-modulated) synchronized excitatory synaptic input, where both the number of presynaptic neurons in synchronous firing assemblies and the synchronous firing rate should be sufficiently large. The correlation in firing of a pair of uncoupled neurons due to common excitatory input is initially increased for increasing firing rates of independent inhibitory inputs but decreases for large inhibitory firing rates. Common inhibitory input to a pair of uncoupled neurons barely induces correlated firing, but amplifies the effect of common excitation. Synchronous firing assemblies in the common input further enhance the correlation and are essential to attain experimentally observed correlation values. Since uncorrelated common input (i.e., common input by neurons, which do not fire in synchrony) cannot induce sufficient postsynaptic correlation, we conclude that lateral couplings are essential to establish clusters of synchronously firing neurons.