Single Neuron Dynamics in Epilepsy

We studied single neuron dynamics during anoxic depolarizations, which are often observed in cases of neuronal energy depletion. Anoxic and similar depolarizations play an important role in several pathologies, notably stroke, migraine, and epilepsy. One of the effects of energy depletion was experimentally simulated in slices of rat cortex by blocking the sodium-potassium pumps with ouabain. The membrane voltage of pyramidal cells was measured. Five different kinds of dynamical behavior of the membrane voltage were observed during the resulting depolarizations. Using bifurcation analysis of a single cell model, we show that these voltage dynamics all are responses of the same cell, with normally functioning ion channels, to particular courses of the intra- and extracellular concentrations of sodium and potassium.

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Single Neuron Dynamics: an Introduction

Neural Modeling and Neural Networks ◽

10.1016/b978-0-08-042277-0.50009-5 ◽

1994 ◽

pp. 57-78 ◽

Cited By ~ 3

Author(s):

LARRY F. ABBOTT

Keyword(s):

Single Neuron ◽

Neuron Dynamics

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Author response: Interplay between population firing stability and single neuron dynamics in hippocampal networks

10.7554/elife.04378.027 ◽

2014 ◽

Author(s):

Edden Slomowitz ◽

Boaz Styr ◽

Irena Vertkin ◽

Hila Milshtein-Parush ◽

Israel Nelken ◽

...

Keyword(s):

Single Neuron ◽

Author Response ◽

Hippocampal Networks ◽

Neuron Dynamics

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Modeling Single-Neuron Dynamics and Computations: A Balance of Detail and Abstraction

Science ◽

10.1126/science.1127240 ◽

2006 ◽

Vol 314 (5796) ◽

pp. 80-85 ◽

Cited By ~ 260

Author(s):

A. V. M. Herz ◽

T. Gollisch ◽

C. K. Machens ◽

D. Jaeger

Keyword(s):

Single Neuron ◽

Neuron Dynamics

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Reconstruction of neuronal input through modeling single-neuron dynamics and computations

Chaos An Interdisciplinary Journal of Nonlinear Science ◽

10.1063/1.4954270 ◽

2016 ◽

Vol 26 (6) ◽

pp. 063121 ◽

Cited By ~ 2

Author(s):

Qing Qin ◽

Jiang Wang ◽

Haitao Yu ◽

Bin Deng ◽

Wai-lok Chan

Keyword(s):

Single Neuron ◽

Neuron Dynamics

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New Efficient Multi-Spike Learning for Fast Processing and Robust Learning

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v34i04.5896 ◽

2020 ◽

Vol 34 (04) ◽

pp. 4650-4657

Author(s):

Shenglan Li ◽

Qiang Yu

Keyword(s):

Neural Networks ◽

Single Neuron ◽

Learning Methods ◽

Processing Efficiency ◽

Robust Learning ◽

Learning Rules ◽

Challenging Tasks ◽

Fast Processing ◽

Classification Tasks ◽

Neuron Dynamics

Spiking neural networks (SNNs) are considered to be more biologically plausible and lower power consuming than traditional artificial neural networks (ANNs). SNNs use discrete spikes as input and output, but how to process and learn these discrete spikes efficiently and accurately still remains a challenging task. Moreover, most existing learning methods are inefficient with complicated neuron dynamics and learning procedures being involved. In this paper, we propose efficient alternatives by firstly introducing a simplified and efficient neuron model. Based on it, we develop two new multi-spike learning rules together with an event-driven scheme being presented to improve the processing efficiency. We show that, with the as-proposed rules, a single neuron can be trained to successfully perform challenging tasks such as multi-category classification and feature extraction. Our learning methods demonstrate a significant robustness against various strong noises. Moreover, experimental results on some real-world classification tasks show that our approaches yield higher efficiency with less requirement on computation resource, highlighting the advantages and potential of spike-based processing and driving more efforts towards neuromorphic computing.

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