A Computational Model of Working Memory Based on Spike-Timing-Dependent Plasticity

Working memory is closely involved in various cognitive activities, but its neural mechanism is still under exploration. The mainstream view has long been that persistent activity is the neural basis of working memory, but recent experiments have observed that activity-silent memory can also be correctly recalled. The underlying mechanism of activity-silent memory is considered to be an alternative scheme that rejects the theory of persistent activity. We propose a working memory model based on spike-timing-dependent plasticity (STDP). Different from models based on spike-rate coding, our model adopts temporal patterns of action potentials to represent information, so it can flexibly encode new memory representation. The model can work in both persistent and silent states, i.e., it is compatible with both of these seemingly conflicting neural mechanisms. We conducted a simulation experiment, and the results are similar to the real experimental results, which suggests that our model is plausible in biology.

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Robust Rhythmogenesis in the Gamma Band via Spike Timing Dependent Plasticity

10.1101/2020.07.23.217026 ◽

2020 ◽

Author(s):

Gabi Socolovsky ◽

Maoz Shamir

Keyword(s):

Mean Field ◽

Rhythmic Activity ◽

Gamma Oscillations ◽

Underlying Mechanism ◽

Spike Timing ◽

Gamma Band ◽

Fine Tuning ◽

Spike Timing Dependent Plasticity ◽

Dependent Plasticity ◽

Wide Range

Rhythmic activity in the gamma band (30-100Hz) has been observed in numerous animal species ranging from insects to humans, and in relation to a wide range of cognitive tasks. Various experimental and theoretical studies have investigated this rhythmic activity. The theoretical efforts have mainly been focused on the neuronal dynamics, under the assumption that network connectivity satisfies certain fine-tuning conditions required to generate gamma oscillations. However, it remains unclear how this fine tuning is achieved.Here we investigated the hypothesis that spike timing dependent plasticity (STDP) can provide the underlying mechanism for tuning synaptic connectivity to generate rhythmic activity in the gamma band. We addressed this question in a modeling study. We examined STDP dynamics in the framework of a network of excitatory and inhibitory neuronal populations that has been suggested to underlie the generation of gamma. Mean field Fokker Planck equations for the synaptic weights dynamics are derived in the limit of slow learning. We drew on this approximation to determine which types of STDP rules drive the system to exhibit gamma oscillations, and demonstrate how the parameters that characterize the plasticity rule govern the rhythmic activity. Finally, we propose a novel mechanism that can ensure the robustness of self-developing processes, in general and for rhythmogenesis in particular.

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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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