Year: 2001
Type: preprint
Abstract: . Neuromodulators, like dopamine, have considerable influence on the processing capabilities of neural networks. This has for instance been shown in the working memory functions of prefrontal cortex, which may be regulated by alter-ing the dopamine level. Experimental work provides evidence on the biochemical and electrophysiological actions of dopamine receptors, but there are few theories concerning their significance for computational properties ((Servan-Schreiber et al., 1990), (Hasselmo, 1994b)). We point to experimental data on neuromodulatory regu-lation of temporal properties of excitatory neurons and depolarization of inhibitory neurons, and suggest computational models employing these effects. Changes in membrane potential may be modelled by the firing threshold, and temporal prop-erties by a parameterization of neuronal responsiveness according to the preceding spike interval. We apply these concepts to two examples using spiking neural net-works. In the first case, there is a change in the input synchronization of neuronal groups, which leads to changes in the formation of synchronized neuronal ensembles. In the second case, the threshold of interneurons influences lateral inhibition, and the switch from a winner-take-all network to a parallel feedforward mode of processing. Both concepts are interesting for the modeling of cognitive functions and may have explanatory power for behavioral changes associated with dopamine regulation.
Author: Gabriele Scheler
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W155371647