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

A nerve cell receives multiple inputs from upstream neurons by way of its synapses. Neuron processing functions are thus influenced by changes in the biophysical properties of the synapse, such as long-term potentiation (LTP) or depression (LTD). This observation has opened new perspectives on the biophysical basis of learning and memory, but its quantitative impact on the information transmission of a neuron remains partially elucidated. One major obstacle is the high dimensionality of the neuronal input-output space, which makes it unfeasible to perform a thorough computational analysis of a neuron with multiple synaptic inputs. In this work, information theory was employed to characterize the information transmission of a cerebellar granule cell over a region of its excitatory input space following synaptic changes. Granule cells have a small dendritic tree (on average, they receive only four mossy fiber afferents), which greatly bounds the input combinatorial space, reducing the complexity of information-theoretic calculations. Numerical simulations and LTP experiments quantified how changes in neurotransmitter release probability (p) modulated information transmission of a cerebellar granule cell. Numerical simulations showed that p shaped the neurotransmission landscape in unexpected ways. As p increased, the optimality of the information transmission of most stimuli did not increase strictly monotonically; instead it reached a plateau at intermediate p levels. Furthermore, our results showed that the spatiotemporal characteristics of the inputs determine the effect of p on neurotransmission, thus permitting the selection of distinctive preferred stimuli for different p values. These selective mechanisms may have important consequences on the encoding of cerebellar mossy fiber inputs and the plasticity and computation at the next circuit stage, including the parallel fiber–Purkinje cell synapses.

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LTP Regulates Burst Initiation and Frequency at Mossy Fiber–Granule Cell Synapses of Rat Cerebellum: Experimental Observations and Theoretical Predictions

Journal of Neurophysiology ◽

10.1152/jn.00696.2005 ◽

2006 ◽

Vol 95 (2) ◽

pp. 686-699 ◽

Cited By ~ 85

Author(s):

Thierry Nieus ◽

Elisabetta Sola ◽

Jonathan Mapelli ◽

Elena Saftenku ◽

Paola Rossi ◽

...

Keyword(s):

Mathematical Model ◽

Granule Cell ◽

Mossy Fiber ◽

Gain Control ◽

Long Term Potentiation ◽

Receptor Activation ◽

Excitatory Postsynaptic Potential ◽

Release Probability ◽

Whole Cell Patch Clamp ◽

Nmda Current

Long-term potentiation (LTP) is a synaptic change supposed to provide the cellular basis for learning and memory in brain neuronal circuits. Although specific LTP expression mechanisms could be critical to determine the dynamics of repetitive neurotransmission, this important issue remained largely unexplored. In this paper, we have performed whole cell patch-clamp recordings of mossy fiber–granule cell LTP in acute rat cerebellar slices and studied its computational implications with a mathematical model. During LTP, stimulation with short impulse trains at 100 Hz revealed earlier initiation of granule cell spike bursts and a smaller nonsignificant spike frequency increase. In voltage-clamp recordings, short AMPA excitatory postsynaptic current (EPSC) trains showed short-term facilitation and depression and a sustained component probably generated by spillover. During LTP, facilitation disappeared, depression accelerated, and the sustained current increased. The N-methyl-d-aspartate (NMDA) current also increased. In agreement with a presynaptic expression caused by increased release probability, similar changes were observed by raising extracellular [Ca2+]. A mathematical model of mossy fiber–granule cell neurotransmission showed that increasing release probability efficiently modulated the first-spike delay. Glutamate spillover, by causing tonic NMDA and AMPA receptor activation, accelerated excitatory postsynaptic potential (EPSP) temporal summation and maintained a sustained spike discharge. The effect of increasing neurotransmitter release could not be replicated by increasing receptor conductance, which, like postsynaptic manipulations enhancing intrinsic excitability, proved very effective in raising granule cell output frequency. Independent regulation of spike burst initiation and frequency during LTP may provide mechanisms for temporal recoding and gain control of afferent signals at the input stage of cerebellar cortex.

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Morphological constraints on cerebellar granule cell combinatorial diversity

10.1101/212324 ◽

2017 ◽

Cited By ~ 1

Author(s):

Jesse I. Gilmer ◽

Abigail L. Person

Keyword(s):

Granule Cell ◽

Granule Cells ◽

Cell Layer ◽

Mossy Fiber ◽

Mossy Fibers ◽

Cerebellar Granule Cell ◽

Granule Cell Layer ◽

Information Encoding ◽

Cerebellar Granule ◽

Local Sampling

AbstractCombinatorial expansion by the cerebellar granule cell layer (GCL) is fundamental to theories of cerebellar contributions to motor control and learning. Granule cells sample approximately four mossy fiber inputs and are thought to form a combinatorial code useful for pattern separation and learning. We constructed a spatially realistic model of the cerebellar granule cell layer and examined how GCL architecture contributes to granule cell (GrC) combinatorial diversity. We found that GrC combinatorial diversity saturates quickly as mossy fiber input diversity increases, and that this saturation is in part a consequence of short dendrites, which limit access to diverse inputs and favor dense sampling of local inputs. This local sampling also produced GrCs that were combinatorially redundant, even when input diversity was extremely high. In addition, we found that mossy fibers clustering, which is a common anatomical pattern, also led to increased redundancy of GrC input combinations. We related this redundancy to hypothesized roles of temporal expansion of GrC information encoding in service of learned timing, and show that GCL architecture produces GrC populations that support both temporal and combinatorial expansion. Finally, we used novel anatomical measurements from mice of either sex to inform modeling of sparse and filopodia-bearing mossy fibers, finding that these circuit features uniquely contribute to enhancing GrC diversification and redundancy. Our results complement information theoretic studies of granule layer structure and provide insight into the contributions of granule layer anatomical features to afferent mixing.Significance StatementCerebellar granule cells are among the simplest neurons, with tiny somata and on average just four dendrites. These characteristics, along with their dense organization, inspired influential theoretical work on the granule cell layer (GCL) as a combinatorial expander, where each granule cell represents a unique combination of inputs. Despite the centrality of these theories to cerebellar physiology, the degree of expansion supported by anatomically realistic patterns of inputs is unknown. Using modeling and anatomy, we show that realistic input patterns constrain combinatorial diversity by producing redundant combinations, which nevertheless could support temporal diversification of like-combinations, suitable for learned timing. Our study suggests a neural substrate for producing high levels of both combinatorial and temporal diversity in the GCL.

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