Name
Title | ||
|---|---|---|
Effect of synaptic plasticity on sensory coding and steady-state filtering properties in the electric sense. |
Abstract | ||
|---|---|---|
Our modeling study examines short-term plasticity at the synapse between afferents from electroreceptors and pyramidal cells in the electrosensory lateral lobe (ELL) of the weakly electric fish Apteronotus leptorhynchus. It focusses on steady-state filtering and coherence-based coding properties. While developed for electroreception, our study exposes general functional features for different mixtures of depression and facilitation. Our computational model, constrained by the available in vivo and in vitro data, consists of a synapse onto a deterministic leaky integrate-and-fire (LIF) neuron. The synapse is either depressing (D), facilitating (F) or both (FD), and is driven by a sinusoidally or randomly modulated Poisson process. Due to nonlinearity, numerically computed input–output transfer functions are used to determine the filtering properties. The gain of the response at each sinusoidally modulated frequency is computed by dividing the fitted amplitudes of the input and output cycle histograms of the LIF models. While filtering is always low-pass for F alone, D alone exhibits a gain resonance (non-monotonicity) at a frequency that decreases with increasing recovery time constant of synaptic depression (τd). This resonance is mitigated by the presence of F. For D, F and FD, coherence improves as the synaptic conductance time constant (τg) increases, yet the mutual information per spike decreases. The information per spike for D and F follows opposite trends as their respective time constants increase. The broadband but non-monotonic gain and coherence functions seen in vivo suggest that D and perhaps FD dynamics are involved at this synapse. Our results further predict that the likely synaptic configuration is a slower τg, e.g. via a mixture of AMPA and NMDA synapses, and a relatively smaller synaptic facilitation time constant (τf) and larger τd (with τf smaller than τd and τg). These results are compatible with known physiology. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1016/j.biosystems.2007.11.002 | Biosystems |
Keywords | Field | DocType |
Short-term synaptic plasticity,Synaptic dynamics,Electroreception | Synapse,Neuroscience,Biological system,Biology,Artificial intelligence,Steady state,Neural facilitation,Amplitude,Coherence (physics),Synaptic plasticity,Conductance,Time constant,Machine learning | Journal |
Volume | Issue | ISSN |
92 | 1 | 0303-2647 |
Citations | PageRank | References |
0 | 0.34 | 6 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Krisztina Szalisznyó | 1 | 3 | 2.40 |
| André Longtin | 2 | 260 | 47.87 |
| Leonard Maler | 3 | 78 | 11.44 |