Extending the Functional Subnetwork Approach to a Generalized Linear Integrate-and-Fire Neuron Model

Szczecinski, Nicholas S.; Quinn, Roger D.; Hunt, Alexander J.

doi:10.3389/fnbot.2020.577804

Cited by 10 publications

(7 citation statements)

References 42 publications

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“…Spiking neurons in SNS-Toolbox are represented as expanded leaky integrate-and-fire neurons [ 53 ], with the membrane depolarization dynamics described in Equation ( 1 ) and an additional dynamical variable for a firing threshold

[ 10 ],

where

is a threshold time constant, and

is the initial threshold voltage. m is a proportionality constant which describes how changes in V affect the behavior of

, with

causing

to always equal

.…”

Section: Methodsmentioning

confidence: 99%

“…However, the connections’ behavior can be extended to defining connections between populations of neurons. Following the model presented in [ 10 ], in the simplest form of population-to-population connection all neurons become fully connected and the synaptic conductance is automatically scaled such that the total conductance into each postsynaptic neuron is the same as the original synapse.…”

Section: Methodsmentioning

confidence: 99%

“…One approach is to design Synthetic Nervous Systems (SNS), networks of conductance-based neurons and synapses which can be used to model animal nervous systems [ 4 , 5 ] and control robots [ 6 , 7 , 8 ]. Some strengths of SNS networks include that they can be tuned using analytic design rules [ 9 , 10 ] and that results obtained controlling robotic hardware can propose neurobiological hypotheses [ 11 , 12 ].…”

Section: Introductionmentioning

confidence: 99%

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SNS-Toolbox: An Open Source Tool for Designing Synthetic Nervous Systems and Interfacing Them with Cyber–Physical Systems

et al. 2023

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One developing approach for robotic control is the use of networks of dynamic neurons connected with conductance-based synapses, also known as Synthetic Nervous Systems (SNS). These networks are often developed using cyclic topologies and heterogeneous mixtures of spiking and non-spiking neurons, which is a difficult proposition for existing neural simulation software. Most solutions apply to either one of two extremes, the detailed multi-compartment neural models in small networks, and the large-scale networks of greatly simplified neural models. In this work, we present our open-source Python package SNS-Toolbox, which is capable of simulating hundreds to thousands of spiking and non-spiking neurons in real-time or faster on consumer-grade computer hardware. We describe the neural and synaptic models supported by SNS-Toolbox, and provide performance on multiple software and hardware backends, including GPUs and embedded computing platforms. We also showcase two examples using the software, one for controlling a simulated limb with muscles in the physics simulator Mujoco, and another for a mobile robot using ROS. We hope that the availability of this software will reduce the barrier to entry when designing SNS networks, and will increase the prevalence of SNS networks in the field of robotic control.

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[ 10 ],

where

is a threshold time constant, and

is the initial threshold voltage. m is a proportionality constant which describes how changes in V affect the behavior of

, with

causing

to always equal

.…”

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

SNS-Toolbox: An Open Source Tool for Designing Synthetic Nervous Systems and Interfacing Them with Cyber–Physical Systems

et al. 2023

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“…All neurons in the SNS are non-spiking neurons, where each neuron represents the mean activity of a population of spiking neurons [ 36 , 37 ]. Each node in the network functions as a leaky integrator [ 29 ].…”

Section: Methodsmentioning

confidence: 99%

“…Each node in the network functions as a leaky integrator [ 29 ]. Neglecting action potentials increases computational efficiency and greatly reduces runtime [ 37 ]. Our model explores how signals propagate through the network and how groups of neurons activate, deactivate, and contribute to network behavior.…”

Section: Methodsmentioning

confidence: 99%

Biomechanical and Sensory Feedback Regularize the Behavior of Different Locomotor Central Pattern Generators

et al. 2022

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This work presents an in-depth numerical investigation into a hypothesized two-layer central pattern generator (CPG) that controls mammalian walking and how different parameter choices might affect the stepping of a simulated neuromechanical model. Particular attention is paid to the functional role of features that have not received a great deal of attention in previous work: the weak cross-excitatory connectivity within the rhythm generator and the synapse strength between the two layers. Sensitivity evaluations of deafferented CPG models and the combined neuromechanical model are performed. Locomotion frequency is increased in two different ways for both models to investigate whether the model’s stability can be predicted by trends in the CPG’s phase response curves (PRCs). Our results show that the weak cross-excitatory connection can make the CPG more sensitive to perturbations and that increasing the synaptic strength between the two layers results in a trade-off between forced phase locking and the amount of phase delay that can exist between the two layers. Additionally, although the models exhibit these differences in behavior when disconnected from the biomechanical model, these differences seem to disappear with the full neuromechanical model and result in similar behavior despite a variety of parameter combinations. This indicates that the neural variables do not have to be fixed precisely for stable walking; the biomechanical entrainment and sensory feedback may cancel out the strengths of excitatory connectivity in the neural circuit and play a critical role in shaping locomotor behavior. Our results support the importance of including biomechanical models in the development of computational neuroscience models that control mammalian locomotion.

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A Synthetic Nervous System with Coupled Oscillators Controls Peristaltic Locomotion

Riddle

Nourse

et al. 2022

Biomimetic and Biohybrid Systems

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Extending the Functional Subnetwork Approach to a Generalized Linear Integrate-and-Fire Neuron Model

Cited by 10 publications

References 42 publications

SNS-Toolbox: An Open Source Tool for Designing Synthetic Nervous Systems and Interfacing Them with Cyber–Physical Systems

SNS-Toolbox: An Open Source Tool for Designing Synthetic Nervous Systems and Interfacing Them with Cyber–Physical Systems

Biomechanical and Sensory Feedback Regularize the Behavior of Different Locomotor Central Pattern Generators

A Synthetic Nervous System with Coupled Oscillators Controls Peristaltic Locomotion

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