ID 403 – Fronto-parietal changes in oscillatory activity reflects spike-timing-dependent plasticity

AbstractNeuronal oscillatory activity has been reported in relation to a wide range of cognitive processes. In certain cases changes in oscillatory activity has been associated with pathological states. Although the specific role of these oscillations has yet to be determined, it is clear that neuronal oscillations are abundant in the central nervous system. These observations raise the question of the origin of these oscillations; and specifically whether the mechanisms responsible for the generation and stabilization of these oscillations are genetically hard-wired or whether they can be acquired via a learning process.Here we focus on spike timing dependent plasticity (STDP) to investigate whether oscillatory activity can emerge in a neuronal network via an unsupervised learning process of STDP dynamics, and if so, what features of the STDP learning rule govern and stabilize the resultant oscillatory activity?Here, the STDP dynamics of the effective coupling between two competing neuronal populations with reciprocal inhibitory connections was analyzed using the phase-diagram of the system that depicts the possible dynamical states of the network as a function of the effective inhibitory couplings. This phase diagram yields a rich repertoire of possible dynamical behaviors including regions of different fixed point solutions, bi-stability and a region in which the system exhibits oscillatory activity. STDP introduces dynamics for the inhibitory couplings themselves and hence induces a flow in the phase diagram. We investigate the conditions for the flow to converge to an oscillatory state of the neuronal network and then characterize how the features of the STDP rule govern and stabilize these oscillations.

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Spike timing-dependent plasticity in sparse recurrent neural networks

IEICE Proceeding Series ◽

10.15248/proc.1.485 ◽

2014 ◽

Vol 1 ◽

pp. 485-488

Author(s):

Hideyuki Kato ◽

Tohru Ikeguchi

Keyword(s):

Neural Networks ◽

Recurrent Neural Networks ◽

Spike Timing ◽

Spike Timing Dependent Plasticity ◽

Dependent Plasticity

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Faculty Opinions recommendation of Postsynaptic depolarization requirements for LTP and LTD: a critique of spike timing-dependent plasticity.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.1028219.335094 ◽

2005 ◽

Author(s):

Dan Johnston

Keyword(s):

Spike Timing ◽

Spike Timing Dependent Plasticity ◽

Dependent Plasticity

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Stable Competitive Dynamics Emerge from Multispike Interactions in a Stochastic Model of Spike-Timing-Dependent Plasticity

Neural Computation ◽

10.1162/neco.2006.18.10.2414 ◽

2006 ◽

Vol 18 (10) ◽

pp. 2414-2464 ◽

Cited By ~ 19

Author(s):

Peter A. Appleby ◽

Terry Elliott

Keyword(s):

Synaptic Plasticity ◽

Stochastic Model ◽

Higher Order ◽

Spike Timing ◽

Competitive Dynamics ◽

Spike Timing Dependent Plasticity ◽

Interaction Function ◽

Dependent Plasticity ◽

Order Interaction ◽

Synaptic Dynamics

In earlier work we presented a stochastic model of spike-timing-dependent plasticity (STDP) in which STDP emerges only at the level of temporal or spatial synaptic ensembles. We derived the two-spike interaction function from this model and showed that it exhibits an STDP-like form. Here, we extend this work by examining the general n-spike interaction functions that may be derived from the model. A comparison between the two-spike interaction function and the higher-order interaction functions reveals profound differences. In particular, we show that the two-spike interaction function cannot support stable, competitive synaptic plasticity, such as that seen during neuronal development, without including modifications designed specifically to stabilize its behavior. In contrast, we show that all the higher-order interaction functions exhibit a fixed-point structure consistent with the presence of competitive synaptic dynamics. This difference originates in the unification of our proposed “switch” mechanism for synaptic plasticity, coupling synaptic depression and synaptic potentiation processes together. While three or more spikes are required to probe this coupling, two spikes can never do so. We conclude that this coupling is critical to the presence of competitive dynamics and that multispike interactions are therefore vital to understanding synaptic competition.

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