Synaptic plasticity: taming the beast

Abbott, L. F.; Nelson, Sacha B.

doi:10.1038/81453

Cited by 1,937 publications

(1,550 citation statements)

References 46 publications

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“…For the last 10 years, solutions were proposed for emulating classic learning rules in SNNs [24,30,4], by means of drastic simplifications that often resulted in losing precious features of firing time-based computing. As an alternative, various researchers have proposed different ways to exploit recent advances in neuroscience about synaptic plasticity [1], especially IP 2 [10,9] or STDP 3 [28,19], that is usually presented as the Hebb rule, revisited in the context of temporal coding. A current trend is to propose computational justifications for plasticity-based learning rules, in terms of entropy minimization [5] as well as log-likelihood [35] or mutual information maximization [8,46,7].…”

Section: Spiking Neuron Networkmentioning

confidence: 99%

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Delay learning and polychronization for reservoir computing

Paugam-Moisy¹,

Martinez²,

Bengio³

2008

Neurocomputing

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We propose a multi-timescale learning rule for spiking neuron networks, in the line of the recently emerging field of reservoir computing. The reservoir is a network model of spiking neurons, with random topology and driven by STDP (spike-time-dependent plasticity), a temporal Hebbian unsupervised learning mode, biologically observed. The model is further driven by a supervised learning algorithm, based on a margin criterion, that affects the synaptic delays linking the network to the readout neurons, with classification as a goal task. The network processing and the resulting performance can be explained by the concept of polychronization, proposed by Izhikevich [Polychronization: computation with spikes, Neural Comput. 18(2) (2006) 245-282], on physiological grounds. The model emphasizes that polychronization can be used as a tool for exploiting the computational power of synaptic delays and for monitoring the topology and activity of a spiking neuron network. r

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Section: Spiking Neuron Networkmentioning

confidence: 99%

“…linear units, sigmoid neurons, threshold gates or spiking neurons (e.g. LIF 1 ), as far as the internal network behaves like a nonlinear dynamical system. In most models, simple learning rules, such as linear regression or recursive least mean squares, are applied to readout neurons only.…”

Section: Reservoir Computingmentioning

confidence: 99%

Delay learning and polychronization for reservoir computing

Paugam-Moisy¹,

Martinez²,

Bengio³

2008

Neurocomputing

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“…This type of homeostatic regulation has been demonstrated in various experimental settings (Turrigiano et al 1998;Turrigiano 2007) and its theoretical properties have been studied in computational models (Abbott & Nelson 2000). The precise biological signalling mechanisms underlying synaptic scaling remain poorly understood, however.…”

Section: Introductionmentioning

confidence: 99%

Epileptogenesis due to glia-mediated synaptic scaling

Savin

Triesch

Meyer‐Hermann

2008

J. R. Soc. Interface.

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Homeostatic regulation of neuronal activity is fundamental for the stable functioning of the cerebral cortex. One form of homeostatic synaptic scaling has been recently shown to be mediated by glial cells that interact with neurons through the diffusible messenger tumour necrosis factor-a (TNF-a). Interestingly, TNF-a is also used by the immune system as a proinflammatory messenger, suggesting potential interactions between immune system signalling and the homeostatic regulation of neuronal activity. We present the first computational model of neuron-glia interaction in TNF-a-mediated synaptic scaling. The model shows how under normal conditions the homeostatic mechanism is effective in balancing network activity. After chronic immune activation or TNF-a overexpression by glia, however, the network develops seizure-like activity patterns. This may explain why under certain conditions brain inflammation increases the risk of seizures. Additionally, the model shows that TNF-a diffusion may be responsible for epileptogenesis after localized brain lesions.

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“…However, it is also clear that the formation of these ensembles is mediated through rapid anatomical/physiological changes. It has been established that the temporally ordered co-activation of neural populations leads to rapid synaptic changes via spike-timing-dependent synaptic modification (spike-timingdependent plasticity, STDP) processes (Bi & Poo 1998;Abbott & Nelson 2000;Song et al 2000;Song & Abbott 2001). Since these synaptic modifications are directional, one would also expect changes in directional relationships between the firing patterns of neurons.…”

Section: Introductionmentioning

confidence: 99%

Memory formation: from network structure to neural dynamics

Feldt

Wang²,

Hetrick

et al. 2010

Phil. Trans. R. Soc. A.

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Understanding the neural correlates of brain function is an extremely challenging task, since any cognitive process is distributed over a complex and evolving network of neurons that comprise the brain. In order to quantify observed changes in neuronal dynamics during hippocampal memory formation, we present metrics designed to detect directional interactions and the formation of functional neuronal ensembles. We apply these metrics to both experimental and model-derived data in an attempt to link anatomical network changes with observed changes in neuronal dynamics during hippocampal memory formation processes. We show that the developed model provides a consistent explanation of the anatomical network modifications that underlie the activity changes observed in the experimental data.

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Synaptic plasticity: taming the beast

Cited by 1,937 publications

References 46 publications

Delay learning and polychronization for reservoir computing

Delay learning and polychronization for reservoir computing

Epileptogenesis due to glia-mediated synaptic scaling

Memory formation: from network structure to neural dynamics

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