Year: 2012
Type: conference-paper
Abstract: The ability of neurons to adapt their responses to greatly varying sensory signal statistics is central to efficient neural coding [2, 4]. With substantial spike-rate adaptation occurring on a time scale of just tens of milliseconds, adapting neurons necessarily generate at most tens of spikes in that period. From an adaptive coding perspective, this implies that for a neuron’s adaptation to be computable by downstream neurons, the adaptation effects have to be derivable from just the emitted spike-train. Spike-based models of adaptation are thus central when accounting for adaptation as adaptive neural coding. In variations of adaptive integrate-and-fire spiking neuron models, adaptation can be incorporated as a combination of two mechanisms: spike-triggered adaptation currents and a dynamical action-potential threshold [5, 6]. In such models, the adaptation mechanisms together increase the distance between the reversal potential and the threshold, effectively changing the gain of the neuron (illustrated in Figure 1(a)). The adaptive Spike Response Model (aSRM) [5, 6] in particular has been shown to be effective for modeling neural behavior in response to input currents with limited dynamic range [6]. On longer timescales, spike-triggered adaptation currents fit a power-law decay rather than an exponential decay, linking to observations of long-range power-law rate-adaptation [8, 6]. Still, in spite of its success, the additive model of adaptation in the aSRM effectively changes neural
Author: Sander M. Bohté
Institution: Centrum Wiskunde & Informatica
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W2162949026