A spiking network model of cerebellar Purkinje cells and molecular layer interneurons exhibiting irregular firing

Lennon, William C.; Hecht-Nielsen, Robert; Yamazaki, Tadashi

doi:10.3389/fncom.2014.00157

Cited by 22 publications

(22 citation statements)

References 58 publications

(81 reference statements)

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“…The MLI neuron model is similar to the neuron model we used in our previous simulations (Lennon et al, 2014 ) except that it excludes inhibitory synaptic conductances and includes excitatory synaptic conductances described below. Briefly, the MLI is modeled as a conductance-based leaky integrate-and-fire neuron model (Gerstner and Kistler, 2002 ) with α-Amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) and N-Methyl-D-aspartic acid (NMDA) conductances and an intrinsic depolarizing current which is drawn from a gamma distribution, I spont ( t ) ~ Γ(κ, β) (in units nA), that causes the neuron to fire spontaneously.…”

Section: Methodsmentioning

confidence: 99%

“… MLI biophysical parameters (Midtgaard, 1992 ; Häusser and Clark, 1997 ; Carter and Regehr, 2002 ; Lachamp et al, 2009 ) AMPA receptor parameters (Carter and Regehr, 2002 ; Satake et al, 2012 ); NMDA receptor parameters (Gabbiani et al, 1994 ); τ n derived by hand to match (Carter and Regehr, 2002 ); κ, β from (Lennon et al, 2014 ) . …”

Section: Methodsmentioning

confidence: 99%

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A Model of In vitro Plasticity at the Parallel Fiber—Molecular Layer Interneuron Synapses

Lennon

Yamazaki

Hecht-Nielsen

2015

Front. Comput. Neurosci.

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Theoretical and computational models of the cerebellum typically focus on the role of parallel fiber (PF)—Purkinje cell (PKJ) synapses for learned behavior, but few emphasize the role of the molecular layer interneurons (MLIs)—the stellate and basket cells. A number of recent experimental results suggest the role of MLIs is more important than previous models put forth. We investigate learning at PF—MLI synapses and propose a mathematical model to describe plasticity at this synapse. We perform computer simulations with this form of learning using a spiking neuron model of the MLI and show that it reproduces six in vitro experimental results in addition to simulating four novel protocols. Further, we show how this plasticity model can predict the results of other experimental protocols that are not simulated. Finally, we hypothesize what the biological mechanisms are for changes in synaptic efficacy that embody the phenomenological model proposed here.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

A Model of In vitro Plasticity at the Parallel Fiber—Molecular Layer Interneuron Synapses

Lennon

Yamazaki

Hecht-Nielsen

2015

Front. Comput. Neurosci.

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“…This latter property contributes to generate transmission delays in the circuit (Subramaniyam et al, 2014 ). Concerning MLIs (Llano and Gerschenfeld, 1993 ; Alcami and Marty, 2013 ) no detailed conductance-based models are available yet and simplified IF models of these neurons were connected with the PCs to investigate the ML subcircuit (Santamaria et al, 2007 ; Lennon et al, 2014 ).…”

Section: Critical Dynamic Properties Of the Cerebellar Microcircuitmentioning

confidence: 99%

Modeling the Cerebellar Microcircuit: New Strategies for a Long-Standing Issue

D’Angelo

Antonietti

Casali

et al. 2016

Front. Cell. Neurosci.

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The cerebellar microcircuit has been the work bench for theoretical and computational modeling since the beginning of neuroscientific research. The regular neural architecture of the cerebellum inspired different solutions to the long-standing issue of how its circuitry could control motor learning and coordination. Originally, the cerebellar network was modeled using a statistical-topological approach that was later extended by considering the geometrical organization of local microcircuits. However, with the advancement in anatomical and physiological investigations, new discoveries have revealed an unexpected richness of connections, neuronal dynamics and plasticity, calling for a change in modeling strategies, so as to include the multitude of elementary aspects of the network into an integrated and easily updatable computational framework. Recently, biophysically accurate “realistic” models using a bottom-up strategy accounted for both detailed connectivity and neuronal non-linear membrane dynamics. In this perspective review, we will consider the state of the art and discuss how these initial efforts could be further improved. Moreover, we will consider how embodied neurorobotic models including spiking cerebellar networks could help explaining the role and interplay of distributed forms of plasticity. We envisage that realistic modeling, combined with closed-loop simulations, will help to capture the essence of cerebellar computations and could eventually be applied to neurological diseases and neurorobotic control systems.

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“…Initial attempts at modeling ML functions involved simplified representations of ML interneurons. Those models have shown the impact of increasing or decreasing SC transmission strength under the assumption of linear rate coding and in the absence of short-term plasticity (Santamaria et al, 2002;Lennon et al, 2014;Lennon et al, 2015). However, it is now clear from several studies that detailed membrane and synaptic dynamics are critical to understand the way a neural circuit operates (De Zeeuw et al, 2011;Arlt and Häusser, 2020).…”

Section: Introductionmentioning

confidence: 99%

Stellate cell computational modelling predicts signal filtering in the molecular layer circuit of cerebellum

Rizza

Locatelli

Masoli

et al. 2020

Preprint

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The functional properties of cerebellar stellate cells and the way they regulate molecular layer activity are still unclear. We have measured stellate cells electroresponsiveness and their activation by parallel fiber bursts. Stellate cells showed intrinsic pacemaking, along with characteristic responses to depolarization and hyperpolarization, and showed a marked short-term facilitation during repetitive parallel fiber transmission. Spikes were emitted after a lag and only at high frequency, making stellate cells to operate as delay-high-pass filters. A detailed computational model summarizing these physiological properties allowed to explore different functional configurations of the parallel fiber-stellate cell-Purkinje cell circuit. Simulations showed that, following parallel fiber stimulation, Purkinje cells almost linearly increased their response with input frequency but such an increase was inhibited by stellate cells, which leveled the Purkinje cell gain curve to its 4 Hz value. When reciprocal inhibitory connections between stellate cells were activated, the control of stellate cells over Purkinje cell discharge was maintained only at very high frequencies. These simulations thus predict a new role for stellate cells, which could endow the molecular layer with low-pass and band-pass filtering properties regulating Purkinje cell gain and, along with this, also burst delay and the burst-pause responses pattern.

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A spiking network model of cerebellar Purkinje cells and molecular layer interneurons exhibiting irregular firing

Cited by 22 publications

References 58 publications

A Model of In vitro Plasticity at the Parallel Fiber—Molecular Layer Interneuron Synapses

A Model of In vitro Plasticity at the Parallel Fiber—Molecular Layer Interneuron Synapses

Modeling the Cerebellar Microcircuit: New Strategies for a Long-Standing Issue

Stellate cell computational modelling predicts signal filtering in the molecular layer circuit of cerebellum

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