Single-Neuron and Network Computation in Realistic Models of the Cerebellar Cortex

D’Angelo, Egidio; Masoli, Stefano; Rizza, Martina Francesca; Casali, Stefano

doi:10.1016/b978-0-12-801386-1.00011-3

Cited by 2 publications

(3 citation statements)

References 107 publications

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“…The features used as templates were extracted from GrC spike discharges under the assumption that these contain all the information required to optimize G i-max values. The present models can be defined “realistic” as far as they reflect a modeling strategy that implements neuronal membranes with biophysically-detailed mechanisms (see discussion in De Schutter, 2001 ; Santamaria et al, 2007 ; D'Angelo et al, 2016 ).…”

Section: Methodsmentioning

confidence: 99%

Single Neuron Optimization as a Basis for Accurate Biophysical Modeling: The Case of Cerebellar Granule Cells

Masoli

Rizza

Sgritta

et al. 2017

Front. Cell. Neurosci.

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In realistic neuronal modeling, once the ionic channel complement has been defined, the maximum ionic conductance (Gi-max) values need to be tuned in order to match the firing pattern revealed by electrophysiological recordings. Recently, selection/mutation genetic algorithms have been proposed to efficiently and automatically tune these parameters. Nonetheless, since similar firing patterns can be achieved through different combinations of Gi-max values, it is not clear how well these algorithms approximate the corresponding properties of real cells. Here we have evaluated the issue by exploiting a unique opportunity offered by the cerebellar granule cell (GrC), which is electrotonically compact and has therefore allowed the direct experimental measurement of ionic currents. Previous models were constructed using empirical tuning of Gi-max values to match the original data set. Here, by using repetitive discharge patterns as a template, the optimization procedure yielded models that closely approximated the experimental Gi-max values. These models, in addition to repetitive firing, captured additional features, including inward rectification, near-threshold oscillations, and resonance, which were not used as features. Thus, parameter optimization using genetic algorithms provided an efficient modeling strategy for reconstructing the biophysical properties of neurons and for the subsequent reconstruction of large-scale neuronal network models.

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Section: Methodsmentioning

confidence: 99%

Single Neuron Optimization as a Basis for Accurate Biophysical Modeling: The Case of Cerebellar Granule Cells

Masoli

Rizza

Sgritta

et al. 2017

Front. Cell. Neurosci.

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“…These properties emerge from the specific ionic channel complement and involve differentially the soma, dendrites and axons. For most of these neurons, there are advanced Hodgkin-Huxley style models, which have helped understanding how the specific electroresponsive properties are generated and as noted above, have set landmarks for realistic modeling strategy (for an extended review see D’Angelo et al, 2016 ).…”

Section: Critical Dynamic Properties Of the Cerebellar Microcircuitmentioning

confidence: 99%

“…The most recent realistic computational models of the cerebellum have been built using an extensive amount of information taken from the anatomical and physiological literature and incorporate neuronal and synaptic models capable of responding to arbitrary input patterns and of generating multiple response properties (Maex and De Schutter, 1998 ; Medina et al, 2000 ; Santamaria et al, 2002 , 2007 ; Santamaria and Bower, 2005 ; Solinas et al, 2010 ; Kennedy et al, 2014 ). Each neuron model is carefully reconstructed through repeated validation steps at different levels: at present, accurate models of the GrCs, GoCs, UBCs, PCs, DCN neurons and IOs neurons are available (De Schutter and Bower, 1994a , b ; D’Angelo et al, 2001 ; D’Angelo et al, 2016 ; Nieus et al, 2006 , 2014 ; Solinas et al, 2007a , b ; Vervaeke et al, 2010 ; Luthman et al, 2011 ; Steuber et al, 2011 ; De Gruijl et al, 2012 ; Subramaniyam et al, 2014 ; Masoli et al, 2015 ). Clearly, realistic models have the intrinsic capacity to resolve the still poorly understood issue of brain dynamics, an issue critical to understand how the cerebellum operates (for e.g., see Llinás, 2014 ).…”

Section: Introductionmentioning

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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Single-Neuron and Network Computation in Realistic Models of the Cerebellar Cortex

Cited by 2 publications

References 107 publications

Single Neuron Optimization as a Basis for Accurate Biophysical Modeling: The Case of Cerebellar Granule Cells

Single Neuron Optimization as a Basis for Accurate Biophysical Modeling: The Case of Cerebellar Granule Cells

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

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