Redundancy Reduction and Sustained Firing with Stochastic Depressing Synapses

Abstract

Many synapses in the CNS transmit only a fraction of the action potentials that reach them. Although unreliable, such synapses do not transmit completely randomly, because the probability of transmission depends on the recent history of synaptic activity. We examine how a variety of spike trains, including examples recorded from area V1 of monkeys freely viewing natural scenes, are transmitted through a realistic model synapse with activity-dependent depression arising from vesicle depletion or postrelease refractoriness. The resulting sequences of transmitted spikes are significantly less correlated, and hence less redundant, than the presynaptic spike trains that generate them. The spike trains we analyze, which are typical of those recorded in a variety of brain regions, have positive autocorrelations because of the occurrence of variable length periods of sustained firing at approximately constant rates. Sustained firing may, at first, seem inconsistent with input from depressing synapses. We show, however, that such a pattern of activity can arise if the postsynaptic neuron is driven by a fixed population of direct, "feedforward" inputs accompanied by a variable number of delayed, "reverberatory" inputs. This leads to a prediction for the number and latency distribution of the inputs that typically drive a cortical neuron.

Nonrenewal spike train statistics: causes and functional consequences on neural coding

“…Indeed, synaptic depression can give rise to relative refractoriness (Häusser and Roth 1997), and models of synaptic depression show similarities with the previously described leaky integrate-and-fire model with threshold fatigue (Fuhrmann et al 2002;Goldman et al 2002). In fact, it has been shown that such models can transform a Poisson presynaptic spike train (note that Poisson spike trains are renewal in nature) into a postsynaptic spike train that displays a negative serial correlation coefficient at lag 1 (Goldman et al 2002). This can be understood intuitively as follows: it is clear that two or more presynaptic action potentials that occur in rapid succession will give rise to an amount of synaptic depression that is inversely proportional to the time between consecutive action potentials (i.e.…”

“…a decrease in the postsynaptic potential amplitude during repetitive presynaptic stimulation) have been pointed out before . Indeed, synaptic depression can give rise to relative refractoriness (Häusser and Roth 1997), and models of synaptic depression show similarities with the previously described leaky integrate-and-fire model with threshold fatigue (Fuhrmann et al 2002;Goldman et al 2002). In fact, it has been shown that such models can transform a Poisson presynaptic spike train (note that Poisson spike trains are renewal in nature) into a postsynaptic spike train that displays a negative serial correlation coefficient at lag 1 (Goldman et al 2002).…”

Short-Term Synaptic Depression Causes a Non-Monotonic Response to Correlated Stimuli

“…Neurons in cortical and subcortical areas often display positive autocorrelations in their spike times (Bair et al, 1994;Dan et al, 1996;Baddeley et al, 1997;Goldman et al, 2002), meaning that the emission of a spike by a presynaptic neuron increases the probability of observing a new discharge of the same neuron over a short time interval (e.g., ϳ10 -20 ms). In other words, spikes are not homogeneously spread in time, but they tend to come in clusters.…”

Spike-Frequency Adaptation Separates Transient Communication Signals from Background Oscillations

“…There are numerous processes that can produce spike-frequency adaptation, including both intrinsic mechanisms and network interactions such as inhibition or synaptic depression. Recently, there has been much attention focused on possible functional implications of synaptic depression for computations such as gain control (Abbott et al, 1997), coincidence detection (Senn et al, 1998), and decorrelation (Goldman et al, 2002). Adaptation induced by slow intrinsic ionic currents of the spike generator is, however, also commonly observed in neurons and may enhance their response to highfrequency input French et al, 2001), mask low-intensity stimuli (Sobel and Tank, 1994;Wang, 1998), induce contrast adaptation (Sanchez-Vives et al, 2000), remove temporal correlations from the input (Wang et al, 2003), or affect network synchrony and rhythms (Crook et al, 1998;Ermentrout et al, 2001;van Vreeswijk and Hansel, 2001;Fuhrmann et al, 2002).…”

“…Both can account for negative ISI correlations in the output spike train (Goldman et al, 2002), and both respond to changes in the stimulus with strong transients. However, each depressing synapse filters its input individually, and the gain to each of the inputs of the neuron is therefore adjusted selectively (Abbott et al, 1997).…”