scholarly journals Synaptic Plasticity in Memristive Artificial Synapses and Their Robustness Against Noisy Inputs

2021 ◽  
Vol 15 ◽  
Author(s):  
Nan Du ◽  
Xianyue Zhao ◽  
Ziang Chen ◽  
Bhaskar Choubey ◽  
Massimiliano Di Ventra ◽  
...  
Keyword(s):  
Pulse Width ◽  
Spike Timing ◽  
Pulse Interval ◽  
Plastic Behavior ◽  
Cycle Number ◽  
Dependent Plasticity ◽  
Noisy Input ◽  
Artificial Synapse ◽  
Wide Range ◽  
The Impact

Emerging brain-inspired neuromorphic computing paradigms require devices that can emulate the complete functionality of biological synapses upon different neuronal activities in order to process big data flows in an efficient and cognitive manner while being robust against any noisy input. The memristive device has been proposed as a promising candidate for emulating artificial synapses due to their complex multilevel and dynamical plastic behaviors. In this work, we exploit ultrastable analog BiFeO3 (BFO)-based memristive devices for experimentally demonstrating that BFO artificial synapses support various long-term plastic functions, i.e., spike timing-dependent plasticity (STDP), cycle number-dependent plasticity (CNDP), and spiking rate-dependent plasticity (SRDP). The study on the impact of electrical stimuli in terms of pulse width and amplitude on STDP behaviors shows that their learning windows possess a wide range of timescale configurability, which can be a function of applied waveform. Moreover, beyond SRDP, the systematical and comparative study on generalized frequency-dependent plasticity (FDP) is carried out, which reveals for the first time that the ratio modulation between pulse width and pulse interval time within one spike cycle can result in both synaptic potentiation and depression effect within the same firing frequency. The impact of intrinsic neuronal noise on the STDP function of a single BFO artificial synapse can be neglected because thermal noise is two orders of magnitude smaller than the writing voltage and because the cycle-to-cycle variation of the current–voltage characteristics of a single BFO artificial synapses is small. However, extrinsic voltage fluctuations, e.g., in neural networks, cause a noisy input into the artificial synapses of the neural network. Here, the impact of extrinsic neuronal noise on the STDP function of a single BFO artificial synapse is analyzed in order to understand the robustness of plastic behavior in memristive artificial synapses against extrinsic noisy input.

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.

scholarly journals Neurodegeneration exposes firing rate dependent effects on oscillation dynamics in computational neural networks

2019 ◽  
Author(s):  
D. Gabrieli ◽  
Samantha N. Schumm ◽  
B. Parvesse ◽  
D.F. Meaney
Keyword(s):  
Neural Networks ◽  
Firing Rate ◽  
Spike Timing ◽  
Network Activity ◽  
Spike Timing Dependent Plasticity ◽  
Dependent Plasticity ◽  
Inhibitory Circuitry ◽  
Excitatory Neurons ◽  
Oscillation Dynamics ◽  
The Impact

AbstractTraumatic brain injury (TBI) can lead to neurodegeneration in the injured circuitry, either through primary structural damage to the neuron or secondary effects that disrupt key cellular processes. Moreover, traumatic injuries can preferentially impact subpopulations of neurons, but the functional network effects of these targeted degeneration profiles remain unclear. Although isolating the consequences of complex injury dynamics and long-term recovery of the circuit can be difficult to control experimentally, computational networks can be a powerful tool to analyze the consequences of injury. Here, we use the Izhikevich spiking neuron model to create networks representative of cortical tissue. After an initial settling period with spike-timing-dependent plasticity (STDP), networks developed rhythmic oscillations similar to those seenin vivo. As neurons were sequentially removed from the network, population activity rate and oscillation dynamics were significantly reduced. In a successive period of network restructuring with STDP, network activity levels were returned to baseline for some injury levels and oscillation dynamics significantly improved. We next explored the role that specific neurons have in the creation and termination of oscillation dynamics. We determined that oscillations initiate from activation of low firing rate neurons with limited structural inputs. To terminate oscillations, high activity excitatory neurons with strong input connectivity activate downstream inhibitory circuitry. Finally, we confirm the excitatory neuron population role through targeted neurodegeneration. These results suggest targeted neurodegeneration can play a key role in the oscillation dynamics after injury.Author SummaryIn this study, we study the impact of neuronal degeneration – a process that commonly occurs after traumatic injury and neurodegenerative disease – on the neuronal dynamics in a cortical network. We create computational models of neural networks and include spike timing plasticity to alter the synaptic strength among connections as networks remodel after simulated injury. We find that spike-timing dependent plasticity helps recover the neural dynamics of an injured microcircuit, but it frequently cannot recover the original oscillation dynamics in an uninjured network. In addition, we find that selectively injuring excitatory neurons with the highest firing rate reduced the neuronal oscillations in a circuit much more than either random deletion or the removing neurons with the lowest firing rate. In all, these data suggest (a) plasticity reduces the consequences of neurodegeneration and (b) losing the most active neurons in the network has the most adverse effect on neural oscillations.

scholarly journals Multiplexing rhythmic information by spike timing dependent plasticity

2019 ◽  
Author(s):  
Nimrod Sherf ◽  
Maoz Shamir
Keyword(s):  
Mean Field ◽  
Sensory Information ◽  
Rhythmic Activity ◽  
Spike Timing ◽  
Spike Timing Dependent Plasticity ◽  
Independent Manner ◽  
Dependent Plasticity ◽  
Wide Range ◽  
Shared Component ◽  
Post Synaptic Neuron

Rhythmic activity has been associated with a wide range of cognitive processes including the encoding of sensory information, navigation, the transfer of emotional information and others. Previous studies have shown that spike-timing-dependent plasticity (STDP) can facilitate the transfer of rhythmic activity downstream the information processing pathway. However, STDP has also been known to generate strong winner-take-all like competitions between subgroups of correlated synaptic inputs. Consequently, one might expect that STDP would induce strong competition between different rhythmicity channels thus preventing the multiplexing of information across different frequency channels. This study explored whether STDP facilitates the multiplexing of information across multiple frequency channels, and if so, under what conditions. We investigated the STDP dynamics in the framework of a model consisting of two competing sub-populations of neurons that synapse in a feedforward manner onto a single post-synaptic neuron. Each sub-population was assumed to oscillate in an independent manner and in a different frequency band. To investigate the STDP dynamics, a mean field Fokker-Planck theory was developed in the limit of the slow learning rate. Surprisingly, our theory predicted limited interactions between the different sub-groups. Our analysis further revealed that the interaction between these channels was mainly mediated by the shared component of the mean activity. Next, we generalized these results beyond the simplistic model using numerical simulations. We found that for a wide range of parameters, the system converged to a solution in which the post-synaptic neuron responded to both rhythms. Nevertheless, all the synaptic weights remained dynamic and did not converge to a fixed point. These findings imply that STDP can support the multiplexing of rhythmic information, and demonstrate how functionality (multiplexing of information) can be retained in the face of continuous remodeling of all the synaptic weights.

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 Impairment of Spike-Timing-Dependent Plasticity at Schaffer Collateral-CA1 Synapses in Adult APP/PS1 Mice Depends on Proximity of Aβ Plaques

2021 ◽  
Vol 22 (3) ◽  
pp. 1378
Author(s):  
Machhindra Garad ◽  
Elke Edelmann ◽  
Volkmar Leßmann
Keyword(s):  
Neurodegenerative Disorder ◽  
Hippocampal Slices ◽  
Long Term Potentiation ◽  
Spike Timing ◽  
Dependent Manner ◽  
Spike Timing Dependent Plasticity ◽  
Schaffer Collateral ◽  
Ca1 Neurons ◽  
Dependent Plasticity ◽  
The Impact

Alzheimer’s disease (AD) is a multifaceted neurodegenerative disorder characterized by progressive and irreversible cognitive decline, with no disease-modifying therapy until today. Spike timing-dependent plasticity (STDP) is a Hebbian form of synaptic plasticity, and a strong candidate to underlie learning and memory at the single neuron level. Although several studies reported impaired long-term potentiation (LTP) in the hippocampus in AD mouse models, the impact of amyloid-β (Aβ) pathology on STDP in the hippocampus is not known. Using whole cell patch clamp recordings in CA1 pyramidal neurons of acute transversal hippocampal slices, we investigated timing-dependent (t-) LTP induced by STDP paradigms at Schaffer collateral (SC)-CA1 synapses in slices of 6-month-old adult APP/PS1 AD model mice. Our results show that t-LTP can be induced even in fully developed adult mice with different and even low repeat STDP paradigms. Further, adult APP/PS1 mice displayed intact t-LTP induced by 1 presynaptic EPSP paired with 4 postsynaptic APs (6× 1:4) or 1 presynaptic EPSP paired with 1 postsynaptic AP (100× 1:1) STDP paradigms when the position of Aβ plaques relative to recorded CA1 neurons in the slice were not considered. However, when Aβ plaques were live stained with the fluorescent dye methoxy-X04, we observed that in CA1 neurons with their somata <200 µm away from the border of the nearest Aβ plaque, t-LTP induced by 6× 1:4 stimulation was significantly impaired, while t-LTP was unaltered in CA1 neurons >200 µm away from plaques. Treatment of APP/PS1 mice with the anti-inflammatory drug fingolimod that we previously showed to alleviate synaptic deficits in this AD mouse model did not rescue the impaired t-LTP. Our data reveal that overexpression of APP and PS1 mutations in AD model mice disrupts t-LTP in an Aβ plaque distance-dependent manner, but cannot be improved by fingolimod (FTY720) that has been shown to rescue conventional LTP in CA1 of APP/PS1 mice.

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.

Synaptic Democracy in Active Dendrites

2006 ◽  
Vol 96 (5) ◽  
pp. 2307-2318 ◽  
Author(s):  
Clifton C. Rumsey ◽  
L. F. Abbott
Keyword(s):  
Action Potentials ◽  
Dendritic Tree ◽  
Pyramidal Cells ◽  
Spike Timing ◽  
Dominant Factor ◽  
Dependent Plasticity ◽  
Ca1 Pyramidal Cells ◽  
Active Dendrites ◽  
Hebbian Plasticity ◽  
The Impact

Given the extensive attenuation that can occur along dendritic cables, location within the dendritic tree might appear to be a dominant factor in determining the impact of a synapse on the postsynaptic response. By this reasoning, distal synapses should have a smaller effect than proximal ones. However, experimental evidence from several types of neurons, such as CA1 pyramidal cells, indicates that a compensatory strengthening of synapses counteracts the effect of location on synaptic efficacy. A form of spike-timing-dependent plasticity (STDP), called anti-STDP, combined with non-Hebbian activity-dependent plasticity can account for the equalization of synaptic efficacies. This result, obtained originally in models with unbranched passive cables, also arises in multi-compartment models with branched and active dendrites that feature backpropagating action potentials, including models with CA1 pyramidal morphologies. Additionally, when dendrites support the local generation of action potentials, anti-STDP prevents runaway dendritic spiking and locally balances the numbers of dendritic and backpropagating action potentials. Thus in multiple ways, anti-STDP eliminates the location dependence of synapses and allows Hebbian plasticity to operate in a more “democratic” manner.

scholarly journals Effect of Interpopulation Spike-Timing-Dependent Plasticity on Synchronized Rhythms in Neuronal Networks with Inhibitory and Excitatory Populations

2019 ◽  
Author(s):  
Sang-Yoon Kim ◽  
Woochang Lim
Keyword(s):  
Pyramidal Cells ◽  
Long Term Potentiation ◽  
Spike Timing ◽  
Spike Timing Dependent Plasticity ◽  
Dependent Plasticity ◽  
Term Potentiation ◽  
Wide Range ◽  
Raster Plot ◽  
Average Fraction

We consider a two-population network consisting of both inhibitory (I) interneurons and excitatory (E) pyramidal cells. This I-E neuronal network has adaptive dynamic I to E and E to I interpopulation synaptic strengths, governed by interpopulation spike-timing-dependent plasticity (STDP). In previous works without STDPs, fast sparsely synchronized rhythms, related to diverse cognitive functions, were found to appear in a range of noise intensity D for static synaptic strengths. Here, by varying D, we investigate the effect of interpopulation STDPs on fast sparsely synchronized rhythms that emerge in both the I- and the E-populations. Depending on values of D, long-term potentiation (LTP) and long-term depression (LTD) for population-averaged values of saturated interpopulation synaptic strengths are found to occur. Then, the degree of fast sparse synchronization varies due to effects of LTP and LTD. In a broad region of intermediate D, the degree of good synchronization (with higher synchronization degree) becomes decreased, while in a region of large D, the degree of bad synchronization (with lower synchronization degree) gets increased. Consequently, in each I- or E-population, the synchronization degree becomes nearly the same in a wide range of D (including both the intermediate and the large D regions). This kind of “equalization effect” is found to occur via cooperative interplay between the average occupation and pacing degrees of spikes (i.e., the average fraction of firing neurons and the average degree of phase coherence between spikes in each synchronized stripe of spikes in the raster plot of spikes) in fast sparsely synchronized rhythms. Finally, emergences of LTP and LTD of interpopulation synaptic strengths (leading to occurrence of equalization effect) are intensively investigated via a microscopic method based on the distributions of time delays between the pre- and the post-synaptic spike times.PACS numbers87.19.lw, 87.19.lm, 87.19.lc

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.

Sign in / Sign up

Export Citation Format

Share Document