scholarly journals Emergence of oscillations via spike timing dependent plasticity

2018 ◽  
Author(s):  
Sarit Soloduchin ◽  
Maoz Shamir
Keyword(s):  
Phase Diagram ◽  
Neuronal Network ◽  
Learning Process ◽  
Learning Rule ◽  
Spike Timing ◽  
Oscillatory Activity ◽  
Spike Timing Dependent Plasticity ◽  
Dependent Plasticity ◽  
Wide Range ◽  
Stdp Rule

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.

scholarly journals STDP and the distribution of preferred phases in the whisker system

2021 ◽  
Vol 17 (9) ◽  
pp. e1009353
Author(s):  
Nimrod Sherf ◽  
Maoz Shamir
Keyword(s):  
Drift Velocity ◽  
Spike Timing ◽  
Neural Population ◽  
Spike Timing Dependent Plasticity ◽  
Dependent Plasticity ◽  
Wide Range ◽  
Stdp Rule ◽  
Natural Test ◽  
Rats And Mice ◽  
Whisker System

Rats and mice use their whiskers to probe the environment. By rhythmically swiping their whiskers back and forth they can detect the existence of an object, locate it, and identify its texture. Localization can be accomplished by inferring the whisker’s position. Rhythmic neurons that track the phase of the whisking cycle encode information about the azimuthal location of the whisker. These neurons are characterized by preferred phases of firing that are narrowly distributed. Consequently, pooling the rhythmic signal from several upstream neurons is expected to result in a much narrower distribution of preferred phases in the downstream population, which however has not been observed empirically. Here, we show how spike timing dependent plasticity (STDP) can provide a solution to this conundrum. We investigated the effect of STDP on the utility of a neural population to transmit rhythmic information downstream using the framework of a modeling study. We found that under a wide range of parameters, STDP facilitated the transfer of rhythmic information despite the fact that all the synaptic weights remained dynamic. As a result, the preferred phase of the downstream neuron was not fixed, but rather drifted in time at a drift velocity that depended on the preferred phase, thus inducing a distribution of preferred phases. We further analyzed how the STDP rule governs the distribution of preferred phases in the downstream population. This link between the STDP rule and the distribution of preferred phases constitutes a natural test for our theory.

scholarly journals STDP and the distribution of preferred phases in the whisker system

2021 ◽  
Author(s):  
Nimrod Sherf ◽  
Maoz Shamir
Keyword(s):  
Drift Velocity ◽  
Spike Timing ◽  
Neural Population ◽  
Spike Timing Dependent Plasticity ◽  
Dependent Plasticity ◽  
Wide Range ◽  
Stdp Rule ◽  
Natural Test ◽  
Rats And Mice ◽  
Whisker System

Rats and mice use their whiskers to probe the environment. By rhythmically swiping their whiskers back and forth they can detect the existence of an object, locate it, and identify its texture. Localization can be accomplished by inferring the position of the whisker. Rhythmic neurons that track the phase of the whisking cycle encode information about the azimuthal location of the whisker. These neurons are characterized by preferred phases of firing that are narrowly distributed. Consequently, pooling the rhythmic signal from several upstream neurons is expected to result in a much narrower distribution of preferred phases in the downstream population, which however has not been observed empirically. Here, we show how spike timing dependent plasticity (STDP) can provide a solution to this conundrum. We investigated the effect of STDP on the utility of a neural population to transmit rhythmic information downstream using the framework of a modeling study. We found that under a wide range of parameters, STDP facilitated the transfer of rhythmic information despite the fact that all the synaptic weights remained dynamic. As a result, the preferred phase of the downstream neuron was not fixed, but rather drifted in time at a drift velocity that depended on the preferred phase, thus inducing a distribution of preferred phases. We further analyzed how the STDP rule governs the distribution of preferred phases in the downstream population. This link between the STDP rule and the distribution of preferred phases constitutes a natural test for our theory.

scholarly journals Robust Rhythmogenesis in the Gamma Band via Spike Timing Dependent Plasticity

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.

Supervised Spike-Timing-Dependent Plasticity: A Spatiotemporal Neuronal Learning Rule for Function Approximation and Decisions

2013 ◽  
Vol 25 (12) ◽  
pp. 3113-3130 ◽  
Author(s):  
Jan-Moritz P. Franosch ◽  
Sebastian Urban ◽  
J. Leo van Hemmen
Keyword(s):  
Function Approximation ◽  
Sensory Input ◽  
Learning Rule ◽  
Animal Learn ◽  
Spike Timing ◽  
Time Dependent ◽  
Stable Configuration ◽  
Spike Timing Dependent Plasticity ◽  
Dependent Plasticity ◽  
Tactile System

How can an animal learn from experience? How can it train sensors, such as the auditory or tactile system, based on other sensory input such as the visual system? Supervised spike-timing-dependent plasticity (supervised STDP) is a possible answer. Supervised STDP trains one modality using input from another one as “supervisor.” Quite complex time-dependent relationships between the senses can be learned. Here we prove that under very general conditions, supervised STDP converges to a stable configuration of synaptic weights leading to a reconstruction of primary sensory input.

scholarly journals Spike-timing-dependent plasticity in a recurrently connected neuronal network with spontaneous oscillations

2009 ◽  
Vol 10 (Suppl 1) ◽  
pp. P212
Author(s):  
Matthieu Gilson ◽  
Anthony N Burkitt ◽  
David B Grayden ◽  
Doreen A Thomas ◽  
J Leo van Hemmen
Keyword(s):  
Neuronal Network ◽  
Spike Timing ◽  
Spike Timing Dependent Plasticity ◽  
Dependent Plasticity ◽  
Spontaneous Oscillations
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