Rate Dynamics of Leaky Integrate-and-Fire Neurons with Strong Synapses - Nordlie, Eilen; Tetzlaff, Tom; Einevoll, Gaute T. (Frontiers in Computational Neuroscience, 2010)
Firing-rate models provide a practical tool for studying the dynamics of trial- or population-averaged neuronal signals. A wealth of theoretical and experimental studies has been dedicated to the derivation or extraction of such models by investigating the firing-rate response characteristics of ensembles of neurons. The majority of these studies assumes that neurons receive input spikes at a high rate through weak synapses (diffusion approximation). For many biological neural systems, however, this assumption cannot be justified. So far, it is unclear how time-varying presynaptic firing rates are transmitted by a population of neurons if the diffusion assumption is dropped. Here, we numerically investigate the stationary and non-stationary firing-rate response properties of leaky integrate-and-fire (LIF) neurons receiving input spikes through excitatory synapses with alpha-function shaped postsynaptic currents for strong synaptic weights. Input spike trains are modelled by inhomogeneous Poisson point-processes with sinusoidal rate. Average rates, modulation amplitudes and phases of the period-averaged spike responses are measured for a broad range of stimulus, synapse and neuron parameters. Across wide parameter regions, the resulting transfer functions can be approximated by a linear 1st-order low-pass filter. Below a critical synaptic weight, the cutoff frequencies are approximately constant and determined by the synaptic time constants. Only for synapses with unrealistically strong weights are the cutoff frequencies significantly increased. To account for stimuli with larger modulation depths, we combine the measured linear transfer function with the nonlinear response characteristics obtained for stationary inputs. The resulting linear-nonlinear model accurately predicts the population response for a variety of non-sinusoidal stimuli.