- Poster presentation
- Open Access
Stabilizing working memory in spiking networks with biologically plausible synaptic dynamics
© Seeholzer et al; licensee BioMed Central Ltd. 2014
- Published: 21 July 2014
- NMDA Receptor
- Synaptic Weight
- Cortical Network
- Connection Probability
- NMDA Channel
Behavior often requires remembering continuously structured information, e.g. positions in the visual field, over delay periods of up to seconds. How can a neural circuit reliably store this information using biophysical mechanisms that work on timescales of milliseconds? Recurrently connected networks with continuous attractors [1, 2] provide a solution by creating a self-sustained bump-shaped neural activity profile that can be positioned along a continuous degree of freedom. This freedom of position, however, renders the activity bump highly sensitive to the sources of variability expected in cortical networks: low connection probabilities, suboptimal synaptic weights or heterogeneity of neuronal parameters. These can lead to a quick drift of the bump position and thus detrimental loss of acuity of the encoded memory. Short-term facilitation (STF) stabilizes drift in continuous attractors, as shown recently in simplified neural network models [3, 4].
In neurons STF acts by dynamically regulating neurotransmitter release, mainly onto NMDA channels, however these simplified models neglect detailed synaptic integration mechanisms. It is thus unclear whether comparable stabilization can be achieved with biologically plausible synaptic dynamics, which limit the effects of STF, like activity dependent saturation of NMDA receptors  and conductance based synaptic transmission.
Research supported by the European Research Council (Agreement #268 689, MultiRules) and the Swiss National Science Foundation (Agreement #200020_147200).
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