What matters in neuronal locking?
Neural Computation
Chaotic balanced state in a model of cortical circuits
Neural Computation
Spatial decorrelation in orientation-selective cortical cells
Neural Computation
Dynamics of Strongly Coupled Spiking Neurons
Neural Computation
An amplitude equation approach to contextual effects in visual cortex
Neural Computation
Response Variability in Balanced Cortical Networks
Neural Computation
Stationary Bumps in Networks of Spiking Neurons
Neural Computation
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Orientation tuning in a ring of pulse-coupled integrate-and-fire (IF) neurons is analyzed in terms of spontaneous pattern formation. It is shown how the ring bifurcates from a synchronous state to a non-phase-locked state whose spike trains are characterized by clustered but irregular fluctuations of the interspike intervals (ISIs). The separation of these clusters in phase space results in a localized peak of activity as measured by the time-averaged firing rate of the neurons. This generates a sharp orientation tuning curve that can lock to a slowly rotating, weakly tuned external stimulus. Under certain conditions, the peak can slowly rotate even to a fixed external stimulus. The ring also exhibits hysteresis due to the subcritical nature of the bifurcation to sharp orientation tuning. Such behavior is shown to be consistent with a corresponding analog version of the IF model in the limit of slow synaptic interactions. For fast synapses, the deterministic fluctuations of the ISIs associated with the tuning curve can support a coefficient of variation of order unity.