Information Rate Analysis of a Synaptic Release Site Using a Two-State Model of Short-Term Depression

Salmasi, Mehrdad; Stemmler, Martin B. ; Glasauer, Stefan und Loebel, Alex (2017): Information Rate Analysis of a Synaptic Release Site Using a Two-State Model of Short-Term Depression. In: Neural Computation, Bd. 29, Nr. 6: S. 1528-1560

Abstract

Synapses are the communication channels for information transfer between neurons;these are the points at which pulse-like signals are converted into the stochastic release of quantized amounts of chemical neurotransmitter. At many synapses, prior neuronal activity depletes synaptic resources, depressing subsequent responses of both spontaneous and spike-evoked releases. We analytically compute the information transmission rate of a synaptic release site, which we model as a binary asymmetric channel. Short-term depression is incorporated by assigning the channel a memory of depth one. A successful release, whether spike evoked or spontaneous, decreases the probability of a subsequent release;if no release occurs on the following time step, the release probabilities recover back to their default values. We prove that synaptic depression can increase the release site's information rate if spontaneous release is more strongly depressed than spike-evoked release. When depression affects spontaneous and evoked release equally, the information rate must invariably decrease, even when the rate is normalized by the resources used for synaptic transmission. For identical depression levels, we analytically disprove the hypothesis, at least in this simplified model, that synaptic depression serves energy- and information-efficient encoding.

• Dokumententyp: Zeitschriftenartikel
• Fakultät: Biologie > Department Biologie II > Neurobiologie
• Fakultätsübergreifende Einrichtungen: Graduate School of Systemic Neurosciences (GSN)
• Themengebiete: 500 Naturwissenschaften und Mathematik > 570 Biowissenschaften; Biologie ; 500 Naturwissenschaften und Mathematik > 500 Naturwissenschaften
• ISSN: 0899-7667
• Sprache: Englisch