How Synaptic Release Probability Shapes Neuronal Transmission: Information-Theoretic Analysis in a Cerebellar Granule Cell

Arleo, Angelo; Nieus, Thierry; Bezzi, Michele; D’Errico, Anna; D’Angelo, Egidio; Coenen, Olivier J. M. D.

doi:10.1162/neco_a_00006-arleo

Cited by 52 publications

(69 citation statements)

References 83 publications

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“…We modeled stochastic pre-synaptic release of the excitatory and inhibitory phasic components (i.e., the α 1 receptor). The stochastic quantal release model was implemented as in Arleo et al (2010) by splitting each synapse into a fixed number of independent releasing sites (RS) and independent post synaptic densities (PSD). The presynaptic RSs and the postsynaptic PSDs were endowed with the same kinetic scheme as in the deterministic model.…”

Section: Methodsmentioning

confidence: 99%

Regulation of output spike patterns by phasic inhibition in cerebellar granule cells

Nieus

Mapelli

D’Angelo

2014

Front. Cell. Neurosci.

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The complex interplay of multiple molecular mechanisms taking part to synaptic integration is hard to disentangle experimentally. Therefore, we developed a biologically realistic computational model based on the rich set of data characterizing the cerebellar glomerulus microcircuit. A specific issue was to determine the relative role of phasic and tonic inhibition in dynamically regulating granule cell firing, which has not been clarified yet. The model comprised the excitatory mossy fiber—granule cell and the inhibitory Golgi cell—granule cell synapses and accounted for vesicular release processes, neurotransmitter diffusion and activation of different receptor subtypes. Phasic inhibition was based on stochastic GABA release and spillover causing activation of two major classes of postsynaptic receptors, α1 and α6, while tonic inhibition was based on steady regulation of a Cl− leakage. The glomerular microcircuit model was validated against experimental responses to mossy fiber bursts while metabotropic receptors were blocked. Simulations showed that phasic inhibition controlled the number of spikes during burst transmission but predicted that it specifically controlled time-related parameters (firing initiation and conclusion and first spike precision) when the relative phase of excitation and inhibition was changed. In all conditions, the overall impact of α6 was larger than that of α1 subunit-containing receptors. However, α1 receptors controlled granule cell responses in a narrow ±10 ms band while α6 receptors showed broader ±50 ms tuning. Tonic inhibition biased these effects without changing their nature substantially. These simulations imply that phasic inhibitory mechanisms can dynamically regulate output spike patterns, as well as calcium influx and NMDA currents, at the mossy fiber—granule cell relay of cerebellum without the intervention of tonic inhibition.

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Section: Methodsmentioning

confidence: 99%

Regulation of output spike patterns by phasic inhibition in cerebellar granule cells

Nieus

Mapelli

D’Angelo

2014

Front. Cell. Neurosci.

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“…Synaptic vesicle release has only recently been studied in a probabilistic or information-theoretic manner (3)(4)(5). A systematic perspective on how stochastic vesicle release affects neural code processing at synapses is still lacking.…”

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confidence: 99%

Improved signaling as a result of randomness in synaptic vesicle release

Zhang

Peskin

2015

Proc. Natl. Acad. Sci. U.S.A.

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The probabilistic nature of neurotransmitter release in synapses is believed to be one of the most significant sources of noise in the central nervous system. We show how p 0 , the probability of release per docked vesicle when an action potential arrives, affects the dynamics of the rate of vesicle release in response to changes in the rate of arrival of action potentials. Furthermore, we examine the theoretical capability of a synapse in the estimation of desired signals using information from the stochastic vesicle release events under the framework of optimal linear filter theory. We find that a small p 0 , such as 0.1, reduces the error in the reconstruction of the input, or in the reconstruction of the time derivative of the input, from the time series of vesicle release events. Our results imply that the probabilistic nature of synaptic vesicle release plays a direct functional role in synaptic transmission.synaptic transmission | stochastic vesicle release | release probability | optimal filter R andomness is present in almost all levels of all nervous systems, such as in ionic channels of individual neurons, in synapses between neurons, and in environmental stimuli. The probabilistic nature of the synaptic vesicle release process is believed to be one of the most significant sources of randomness. Stochastic vesicle release affects information transfer from a presynaptic neuron to a postsynaptic neuron, and hence may play not only an important role in synaptic plasticity (1, 2) but also a significant role in determining the functionality of certain synapses, a point we will argue in this article. The functional role of stochastic vesicle release in synaptic transmission is likely to be more significant in those synapses in the central nervous system, where the size of the readily releasable vesicle pool is usually smaller than those in the periphery.Synaptic vesicle release has only recently been studied in a probabilistic or information-theoretic manner (3-5). A systematic perspective on how stochastic vesicle release affects neural code processing at synapses is still lacking. On the experimental front, a comprehensive, quantitative knowledge about the nature of vesicle docking, priming, fusing, undocking, replenishing, and recycling is far from complete, and hence a biophysically detailed model of the entire vesicle release process is not yet possible.Despite current limited understanding of the synaptic vesicle release process, a probabilistic model that captures the essential elements of that process can still be built to study the effect of various sources of randomness on synaptic transmission. Rosenbaum et al. (3) recently constructed such a model to study how variability in vesicle dynamics affects information transfer from one neuron to another. They found that the depletion of docked vesicles at higher rates of arrival of action potentials makes the synapse act as a highpass filter from a signal processing point of view. Motivated by Rosenbaum et al.'s (3) model of the presynaptic vesicle r...

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“…31.6) has revealed that the whole set of complex properties of intrinsic excitability and synaptic transmission can be reproduced by appropriate mechanisms derived from experimental observations. These models allowed an explanation of properties like resonance Nieus et al 2006) and synaptic plasticity (Nieus et al 2006), Na + channel localization and spike generation (Diwakar et al 2009), stochastic release, and mutual information (MI) transfer (Arleo et al 2010). The granule cell models, together with those of the Golgi cell, are currently being used to generate new network models reproducing all known granular layer spatiotemporal dynamics ) and reconnecting molecular and cellular properties of the granule cell to global network computations.…”

Section: Granule Cell Modelsmentioning

confidence: 99%

“…Recently, a computational model has shown that at least half of the information passing through the mossy fiber-granule cell relay is conveyed by first spike delay (Arleo et al 2010). New experiments and computational models are required to fully understand the functional role of the granule cells.…”

Section: New Views On the Granule Cell Function: Transformations Of Smentioning

confidence: 99%

Cerebellar Granule Cell

D’Angelo

2013

Handbook of the Cerebellum and Cerebellar Disorders

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The cerebellar granule cells are the most numerous neurons of the brain. They are also among the most simple by emitting just four short unbranched dendrites innervated by the mossy fibers and a thin axon that branches into the parallel fibers. The granule cells are the only excitatory neurons of the cerebellum and receive inhibition uniquely through the Golgi cells. Since their early discovery by Camillo Golgi and Ramon y Cajal at the end of the nineteenth century, the granule cells have been the object of intensive investigation. The milestones have been the field recording of their activity in the 1960s, the discovery of their GABAergic inhibition in the 1970s and of their glutamatergic excitation in the 1980s, and the characterization of their membrane and synaptic mechanisms with patch-clamp techniques in the 1990s. Then in the last two decades, a series of careful electrophysiological and imaging investigations have unveiled major yet undisclosed functional properties of granule cells leading to a reevaluation of the function of the whole cerebellar cortex. The initial guess that granule cells simply retransmit incoming information (integrate and fire behavior) has been challenged by recent results showing that granule cells and their synapses are endowed with nonlinear transmission properties and are wired in a way allowing them to operate complex transformations of input signals in the spatiotemporal domain. Moreover, the prediction that the mossy fiber-granule cell synapse would lack long-term plasticity has been reversed by the discovery of complex forms of long-term potentiation and depression (LTP and LTD). These properties allow the granule neurons to provide a substantial contribution to the computational and learning properties of the cerebellum.

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How Synaptic Release Probability Shapes Neuronal Transmission: Information-Theoretic Analysis in a Cerebellar Granule Cell

Cited by 52 publications

References 83 publications

Regulation of output spike patterns by phasic inhibition in cerebellar granule cells

Regulation of output spike patterns by phasic inhibition in cerebellar granule cells

Improved signaling as a result of randomness in synaptic vesicle release

Cerebellar Granule Cell

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