ArticleFrontiers in computational neuroscience2013
Interaction of short-term depression and firing dynamics in shaping single neuron encoding.
Article in Frontiers in computational neuroscience, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Neural adaptation and fractional dynamics as a window to underlying neural excitability.PLoS computational biology · 2023Article
- Phase changes in neuronal postsynaptic spiking due to short term plasticity.PLoS computational biology · 2017Article
- Neural spike-timing patterns vary with sound shape and periodicity in three auditory cortical fields.Journal of neurophysiology · 2016Article
- Interplay between Subthreshold Oscillations and Depressing Synapses in Single Neurons.PloS one · 2016Article
- Mathematical analysis and algorithms for efficiently and accurately implementing stochastic simulations of short-term synaptic depression and facilitation.Frontiers in computational neuroscience · 2013Article
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3 authors.
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Abstract
We investigated how the two properties short-term synaptic depression of afferent input and postsynaptic firing dynamics combine to determine the operating mode of a neuron. While several computational roles have been ascribed to either, their interaction has not been studied. We considered two types of short-term synaptic dynamics (release-dependent and release-independent depression) and two classes of firing dynamics (regular firing and firing with spike-frequency adaptation). The input-output transformation of the four possible combinations of pre- and post-synaptic dynamics was characterized. Adapting neurons receiving input from release-dependent synapses functioned largely as coincidence detectors. The other three configurations showed properties consistent with integrators, each with distinct features. These results suggest that the operating mode of a neuron is determined by both the pre- and post-synaptic dynamics and that studying them together is necessary to understand emergent properties and their implications for neuronal coding.
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