Closed-loop control of a minimal central pattern generator network

Elices, Irene; Varona, Pablo

doi:10.1016/j.neucom.2015.04.097

Cited by 10 publications

(1 citation statement)

References 31 publications

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“…Most CPGs are typically studied in regular regimes where the activity of individual neurons can be directly related to specific rhythmic motor functions, such as walking, breathing, chewing, etc. However, irregular rhythms can also be observed in control or induced conditions (e.g., see Bartos et al, 1999 ; Thuma and Hooper, 2003 ; Nadim et al, 2011 ; Elices and Varona, 2015 ; Hooper et al, 2015 ). Central and sensory feedback transiently alter the CPG pattern which also results in observed irregularity.…”

Section: Introductionmentioning

confidence: 99%

Asymmetry Factors Shaping Regular and Irregular Bursting Rhythms in Central Pattern Generators

Elices

Varona

2017

Front. Comput. Neurosci.

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Central Pattern Generator (CPG) circuits are neural networks that generate rhythmic motor patterns. These circuits are typically built of half-center oscillator subcircuits with reciprocally inhibitory connections. Another common property in many CPGs is the remarkable rich spiking-bursting dynamics of their constituent cells, which balance robustness and flexibility to generate their joint coordinated rhythms. In this paper, we use conductance-based models and realistic connection topologies inspired by the crustacean pyloric CPG to address the study of asymmetry factors shaping CPG bursting rhythms. In particular, we assess the role of asymmetric maximal synaptic conductances, time constants and gap-junction connectivity to establish the regularity of half-center oscillator based CPGs. We map and characterize the synaptic parameter space that lead to regular and irregular bursting activity in these networks. The analysis indicates that asymmetric configurations display robust regular rhythms and that large regions of both regular and irregular but coordinated rhythms exist as a function of the asymmetry in the circuit. Our results show that asymmetry both in the maximal conductances and in the temporal dynamics of mutually inhibitory neurons can synergistically contribute to shape wide regimes of regular spiking-bursting activity in CPGs. Finally, we discuss how a closed-loop protocol driven by a regularity goal can be used to find and characterize regular regimes when there is not time to perform an exhaustive search, as in most experimental studies.

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Section: Introductionmentioning

confidence: 99%

Asymmetry Factors Shaping Regular and Irregular Bursting Rhythms in Central Pattern Generators

Elices

Varona

2017

Front. Comput. Neurosci.

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Automatic Adaptation of Model Neurons and Connections to Build Hybrid Circuits with Living Networks

et al. 2020

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Hybrid circuits built by creating mono-or bi-directional interactions among living cells and model neurons and synapses are an effective way to study neuron, synaptic and neural network dynamics. However, hybrid circuit technology has been largely underused in the context of neuroscience studies mainly because of the inherent difficulty in implementing and tuning this type of interactions. In this paper, we present a set of algorithms for the automatic adaptation of model neurons and connections in the creation of hybrid circuits with living neural networks. The algorithms perform model time and amplitude scaling, drift compensation, goal-driven synaptic and model tuning/calibration and also automatic parameter mapping. These algorithms have been implemented in RTHybrid, an open-source library that works with hard real-time constraints. We provide validation examples by building hybrid circuits in a central pattern generator. The results of the validation experiments show that the proposed dynamic adaptation facilitates building hybrid circuits and closedloop communication among living and artificial model neurons and connections. Furthermore contributes to characterize system dynamics, achieve control, automate experimental protocols and extend the lifespan of the preparations.

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Predictive control for spike pattern modulation of a two-compartment neuron model

Wang

et al. 2016

Neurocomputing

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Closed-loop control of a minimal central pattern generator network

Cited by 10 publications

References 31 publications

Asymmetry Factors Shaping Regular and Irregular Bursting Rhythms in Central Pattern Generators

Asymmetry Factors Shaping Regular and Irregular Bursting Rhythms in Central Pattern Generators

Automatic Adaptation of Model Neurons and Connections to Build Hybrid Circuits with Living Networks

Predictive control for spike pattern modulation of a two-compartment neuron model

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