2014
DOI: 10.1103/physrevx.4.011053
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Statistical Physics of Neural Systems with Nonadditive Dendritic Coupling

Abstract: How neurons process their inputs crucially determines the dynamics of biological and artificial neural networks. In such neural and neural-like systems, synaptic input is typically considered to be merely transmitted linearly or sublinearly by the dendritic compartments. Yet, single-neuron experiments report pronounced supralinear dendritic summation of sufficiently synchronous and spatially close-by inputs. Here, we provide a statistical physics approach to study the impact of such nonadditive dendritic proce… Show more

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Cited by 16 publications
(15 citation statements)
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“…This simple description was shown to capture the time-average firing rate of biophysical neuron models with detailed morphology and active dendritic conductances supporting NMDA spikes [25,31,52]. Modeling studies have shown that this architecture provides multiple advantages, namely, specific sensory computations [53], enhanced memory capacity [30,35], enhanced dynamic range [54,55], and flexible gating of specific pathways [32]. These computational advantages are based on a phenomenological description of the time-averaged firing rate, which could remain consistent with the timingdependent mechanisms we describe here.…”
Section: Discussionmentioning
confidence: 99%
See 1 more Smart Citation
“…This simple description was shown to capture the time-average firing rate of biophysical neuron models with detailed morphology and active dendritic conductances supporting NMDA spikes [25,31,52]. Modeling studies have shown that this architecture provides multiple advantages, namely, specific sensory computations [53], enhanced memory capacity [30,35], enhanced dynamic range [54,55], and flexible gating of specific pathways [32]. These computational advantages are based on a phenomenological description of the time-averaged firing rate, which could remain consistent with the timingdependent mechanisms we describe here.…”
Section: Discussionmentioning
confidence: 99%
“…We therefore model a dendrite-soma system as two interconnected integrate-and-fire units, a system studied in the context of connected pairs of neurons [26,27]. To model dendrite-soma systems, we consider that each compartment has independent intrinsic noise, a distinct refractory period, and common stimulation of intensity s. These effects are distinct from dendritic N-methyl D-Aspartate (NMDA) spikes [25,[28][29][30][31][32], calcium spikes [21,22,33], or other simplified models of dendritic activity lacking either a back-propagating action potential or a clear refractory period [34,35].…”
Section: B Integrate-and-fire Descriptionmentioning
confidence: 99%
“…This simple description was shown to capture time-average firing rate of biophysical neuron models with detailed morphology and active dendritic conductances supporting NMDA-spikes [25,31,52]. Modelling studies have shown that this architecture provides multiple advantages, namely specific sensory computations [53], enhanced memory capacity [30,35], enhanced dynamic range [54,55] and flexible gating of specific pathways [32]. These computational advantages are based on a phenomenological description of the timeaveraged firing rate, which could remain consistent with the timing-dependent mechanisms described here.…”
Section: Discussionmentioning
confidence: 99%
“…We therefore modeled a dendrite-soma system as two interconnected integrateand-fire units, a system studied in the context of connected pairs of neurons [26,27]. To model dendrite-soma systems, we considered that each compartment has independent intrinsic noise, a distinct refractory period and common stimulation of intensity s. These effects are distinct from dendritic NMDA-spikes [25,[28][29][30][31][32], of calcium spikes [21,22,33] or other simplified models of dendritic activity lacking either a back-propagating action potentials or a clear refractory period [34,35].…”
Section: B Integrate-and-fire Descriptionmentioning
confidence: 99%
“…According to Eqs. (43), (44), the perturbation δV (t + k ) changes until t − k+1 by a factor e −γ(t k+1 −t k ) . Further, it generates a perturbation δt k+1 = ∂t k+1 /∂V (t + k ) δV (t + k ) of t k+1 .…”
Section: B Mean-field Lyapunov Exponentsmentioning
confidence: 99%