Conductance-Based Adaptive Exponential Integrate-and-Fire Model

Górski, Tomasz; Depannemaecker, Damien; Destexhe, Alain

doi:10.1162/neco_a_01342

Cited by 34 publications

(35 citation statements)

References 15 publications

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“…B. To examine the changes in the activity of SDH cells taking into account the increased baseline, we modeled the SDH network utilizing a conductance-based adaptive exponential (CadEx, Górski et al, 2021) integrate-and-fire model (Figure 5A). This model allows performing large network simulations with simplified models reproducing neuronal firing properties (Górski et al, 2021).…”

Section: Resultsmentioning

confidence: 99%

“…To examine the changes in the activity of SDH cells taking into account the increased baseline, we modeled the SDH network utilizing a conductance-based adaptive exponential (CadEx, Górski et al, 2021) integrate-and-fire model (Figure 5A). This model allows performing large network simulations with simplified models reproducing neuronal firing properties (Górski et al, 2021). We constructed a biologically constrained model of a superficial spinal dorsal horn Laminae I-II, composed of 70% excitatory and 30% inhibitory spiking neurons (Polgár et al, 2013;Prescott and Ratté, 2012;Todd, 2010) connected to each other (Figure 5A, B).…”

Section: Resultsmentioning

confidence: 99%

“…The computational model is composed of 1000 neurons, with 300 inhibitory (Ni, 30%) and 700 excitatory (Ne, 70%) Conductance-based Adaptive Exponential (CAdEx) integrate-and-fire neurons (Górski et al, 2021;Peirs et al, 2020;Polgár et al, 2013). 100% of the inhibitory neurons had a tonic firing pattern.…”

Section: Computational Modelmentioning

confidence: 99%

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Encoding of inflammatory hyperalgesia in mice spinal cord

Barkai

Butterman

Katz

et al. 2021

Preprint

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Inflammation modifies the input-output properties of peripheral nociceptive neurons, thus leading to hyperalgesia, i.e., changes in the perception of noxious heat stimuli such that the same stimulus produces enhanced pain. The increased nociceptive output enters the superficial dorsal spinal cord (SDH), which comprises the first CNS network integrating the noxious information. Here we used in vivo calcium imaging and a computational approach to investigate how the SDH network in mice encodes the injury-mediated abnormal input from peripheral nociceptive neurons. We show the application of noxious heat stimuli to the mice hind paw in naive conditions before induction of injury affects the activity of 70% of recorded neurons by either increasing or suppressing it. Application of the same noxious heat stimuli to hyperalgesic skin leads to activation of previously non-responded cells and de-suppression of the "suppressed" neurons. We demonstrate that reduction in synaptic inhibition mimics the response to the noxious stimuli in the hyperalgesic conditions. Using a computational model of SDH network, we predict that inflammation-induced "disinhibition"-like changes result in increased activity of excitatory projection neurons. Our model also predicts that the observed "disinhibitory" effect of hyperalgesic stimuli does not require changes in the inhibitory transmission and may result from the SDH networks' intrinsic cytoarchitecture.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

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Encoding of inflammatory hyperalgesia in mice spinal cord

Barkai

Butterman

Katz

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Although the models we have studied are phenomenological, there exists more biophysically plausible versions of such AIF models, in particular conductance-based versions of them. In a recent work [20], the CAdEx model was extensively studied as a single unit and also within networks. Most of the dynamics uncovered in this biophysical version can be obtained with the models we studied in the present work; in particular the delayed bursting showcased in [20] clearly correspond to the bursting cycles with canard segments that we have investigated.…”

Section: Discussionmentioning

confidence: 99%

“…Nevertheless the subsequent decay is still replaced by reset conditions. The AdEx family of IF models has recently been enlarged with the conductance-based AdEx (CAdEx) version [20], more suited to reproduce realistic time series of neuron's membrane potential. Both the Izhikevich and the AdEx/CAdExp models can reproduce bursting activity [22].…”

Section: Introductionmentioning

confidence: 99%

Spike-adding and reset-induced canard cycles in adaptive integrate and fire models

2021

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We study a class of planar integrate and fire (IF) models called adaptive integrate and fire (AIF) models, which possesses an adaptation variable on top of membrane potential, and whose subthreshold dynamics is piecewise linear (PWL). These AIF models therefore have two reset conditions, which enable bursting dynamics to emerge for suitable parameter values. Such models can be thought of as hybrid dynamical systems. We consider a particular slow dynamics within AIF models and prove the existence of bursting cycles with N resets, for any integer N . Furthermore, we study the transition between N -and (N + 1)-reset cycles upon vanishingly small parameter variations and prove (for N = 2) that such transitions are organised by canard cycles. Finally, using numerical continuation we compute branches of bursting cycles, including canard-explosive branches, in these AIF models, by suitably recasting the periodic problem as a two-point boundary-value problem.

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Electrochemical‐Memristor‐Based Artificial Neurons and Synapses—Fundamentals, Applications, and Challenges

et al. 2023

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Artificial neurons and synapses are considered essential for the progress of the future brain‐inspired computing, based on beyond von Neumann architectures. Here, a discussion on the common electrochemical fundamentals of biological and artificial cells is provided, focusing on their similarities with the redox‐based memristive devices. The driving forces behind the functionalities and the ways to control them by an electrochemical‐materials approach are presented. Factors such as the chemical symmetry of the electrodes, doping of the solid electrolyte, concentration gradients, and excess surface energy are discussed as essential to understand, predict, and design artificial neurons and synapses. A variety of two‐ and three‐terminal memristive devices and memristive architectures are presented and their application for solving various problems is shown. The work provides an overview of the current understandings on the complex processes of neural signal generation and transmission in both biological and artificial cells and presents the state‐of‐the‐art applications, including signal transmission between biological and artificial cells. This example is showcasing the possibility for creating bioelectronic interfaces and integrating artificial circuits in biological systems. Prospectives and challenges of the modern technology toward low‐power, high‐information‐density circuits are highlighted.

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Conductance-Based Adaptive Exponential Integrate-and-Fire Model

Cited by 34 publications

References 15 publications

Encoding of inflammatory hyperalgesia in mice spinal cord

Encoding of inflammatory hyperalgesia in mice spinal cord

Spike-adding and reset-induced canard cycles in adaptive integrate and fire models

Electrochemical‐Memristor‐Based Artificial Neurons and Synapses—Fundamentals, Applications, and Challenges

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