Effect Of Spike-timing-dependent Plasticity Research Articles

Chimera states induced by spike timing-dependent plasticity in a regular neuronal network

Chimera states are spatiotemporal patterns in which distinct dynamics coexist, such as synchronous and asynchronous patterns. In this work, we study the effect of spike timing-dependent plasticity (STDP) on the emergence of chimera states. We consider a regular network of coupled adaptive exponential integrate-and-fire neurons, where all connections initially have the same strength value. The STDP alters the strength value as a function of the timing between the pre and postsynaptic action potentials over time. We verify that the range of parameters displaying chimera states is larger in the network with plasticity than in the absence of plasticity. Our simulations show that the chimera lifetime increases when the plasticity actuates in the neuronal network. We also observe an increase in neuronal spike frequency when the neurons are submitted to a constant positive current. In the parameter space, the changes in synaptic weights increase the appearance of chimera states.

STDP and the distribution of preferred phases in the 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.

Spike-Timing Dependent Plasticity Effect on the Temporal Patterning of Neural Synchronization.

Neural synchrony in the brain at rest is usually variable and intermittent, thus intervals of predominantly synchronized activity are interrupted by intervals of desynchronized activity. Prior studies suggested that this temporal structure of the weakly synchronous activity might be functionally significant: many short desynchronizations may be functionally different from few long desynchronizations even if the average synchrony level is the same. In this study, we used computational neuroscience methods to investigate the effects of spike-timing dependent plasticity (STDP) on the temporal patterns of synchronization in a simple model. We employed a small network of conductance-based model neurons that were connected via excitatory plastic synapses. The dynamics of this network was subjected to the time-series analysis methods used in prior experimental studies. We found that STDP could alter the synchronized dynamics in the network in several ways, depending on the time scale that plasticity acts on. However, in general, the action of STDP in the simple network considered here is to promote dynamics with short desynchronizations (i.e., dynamics reminiscent of that observed in experimental studies). Complex interplay of the cellular and synaptic dynamics may lead to the activity-dependent adjustment of synaptic strength in such a way as to facilitate experimentally observed short desynchronizations in the intermittently synchronized neural activity.

Stochastic spike synchronization in a small-world neural network with spike-timing-dependent plasticity

We consider the Watts–Strogatz small-world network (SWN) consisting of subthreshold neurons which exhibit noise-induced spikings. This neuronal network has adaptive dynamic synaptic strengths governed by the spike-timing-dependent plasticity (STDP). In previous works without STDP, stochastic spike synchronization (SSS) between noise-induced spikings of subthreshold neurons was found to occur in a range of intermediate noise intensities. Here, we investigate the effect of additive STDP on the SSS by varying the noise intensity. Occurrence of a “Matthew” effect in synaptic plasticity is found due to a positive feedback process. As a result, good synchronization gets better via long-term potentiation of synaptic strengths, while bad synchronization gets worse via long-term depression. Emergences of long-term potentiation and long-term depression of synaptic strengths are intensively investigated via microscopic studies based on the pair-correlations between the pre- and the post-synaptic IISRs (instantaneous individual spike rates) as well as the distributions of time delays between the pre- and the post-synaptic spike times. Furthermore, the effects of multiplicative STDP (which depends on states) on the SSS are studied and discussed in comparison with the case of additive STDP (independent of states). These effects of STDP on the SSS in the SWN are also compared with those in the regular lattice and the random graph.

Effects of the spike timing-dependent plasticity on the synchronisation in a random Hodgkin–Huxley neuronal network

In this paper, we study the effects of spike timing-dependent plasticity on synchronisation in a network of Hodgkin–Huxley neurons. Neuron plasticity is a flexible property of a neuron and its network to change temporarily or permanently their biochemical, physiological, and morphological characteristics, in order to adapt to the environment. Regarding the plasticity, we consider Hebbian rules, specifically for spike timing-dependent plasticity (STDP), and with regard to network, we consider that the connections are randomly distributed. We analyse the synchronisation and desynchronisation according to an input level and probability of connections. Moreover, we verify that the transition for synchronisation depends on the neuronal network architecture, and the external perturbation level.

Formation and Regulation of Dynamic Patterns in Two-Dimensional Spiking Neural Circuits with Spike-Timing-Dependent Plasticity

Spike-timing-dependent plasticity (STDP) is an important synaptic dynamics that is capable of shaping the complex spatiotemporal activity of neural circuits. In this study, we examine the effects of STDP on the spatiotemporal patterns of a spatially extended, two-dimensional spiking neural circuit. We show that STDP can promote the formation of multiple, localized spiking wave patterns or multiple spike timing sequences in a broad parameter space of the neural circuit. Furthermore, we illustrate that the formation of these dynamic patterns is due to the interaction between the dynamics of ongoing patterns in the neural circuit and STDP. This interaction is analyzed by developing a simple model able to capture its essential dynamics, which give rise to symmetry breaking. This occurs in a fundamentally self-organizing manner, without fine-tuning of the system parameters. Moreover, we find that STDP provides a synaptic mechanism to learn the paths taken by spiking waves and modulate the dynamics of their interactions, enabling them to be regulated. This regulation mechanism has error-correcting properties. Our results therefore highlight the important roles played by STDP in facilitating the formation and regulation of spiking wave patterns that may have crucial functional roles in brain information processing.

STDP induced synchrony in inhibitory neural networks: theory and experiments

Gamma rhythms have been the focus of significant research interest within the neuroscience community in recent years [1]. Earlier theoretical studies focused on the question: how can a network of GABA containing neurons generate gamma oscillations? The findings suggest that for non-instantaneous synaptic events, inhibition rather than excitation is a better synchronizing mechanism for the generation of gamma rhythms [2]. Synchrony in inhibitory neural networks however is extremely sensitive to intrinsic heterogeneity in the network [3]. Based on the evidence for spike timing dependent plasticity (STDP) in inhibitory neurons [4,5], we hypothesize that STDP of inhibitory synapses can promote robust neural synchrony in inhibitory neuronal networks in the presence of heterogeneity. We investigate our hypothesis in a computational (Figure ​(Figure1a)1a) and a hybrid (Figure ​(Figure1b)1b) uni-directionally coupled network (UCI) of two fast-spiking inhibitory neurons. In the computational model, each neuron is modeled using the Hodgkin-Huxley (HH) framework [4]. Heterogeneity (H) is modeled by the difference in the intrinsic firing activity of the coupled neurons resulting from different DC current input. In the presence of STDP, the strength of synaptic coupling between the coupled neurons is modeled using a linear additive rule as: gs(t) = gs(t – 1) + ΔgSTDP(Δt), where Δt = tA – tB is the time interval between successive spikes of the post-synaptic and the pre-synaptic neurons and ΔgSTDP is the STDP rule adapted from [4]. In Figure ​Figure1a,1a, we demonstrate the effect of STDP on synchrony between coupled neurons in the presence of and the absence of STDP for network heterogeneity H=12%. Theoretical analysis using the concept of spike time response curves and Arnold tongue revealed that STDP induced 1:1 synchrony in the UCI network is (i) robust against a large range of heterogeneity in the intrinsic firing activity of coupled neurons, and (ii) in-phase i.e., Δt ≈ 0. Furthermore, the time to in-phase synchronization increases with increasing heterogeneity. Figure 1 a) Theoretical model of UCI. Autaptic synapse is introduced to mimic spike frequency adaptation in neuronal firing activity. b) Hybrid UCI network. Dynamic clamp experimental results for experimental protocol with fixed static-synaptic strength gs(0)=0.5 ... We used the dynamic clamp technique to construct a hybrid UCI network consisting of an HH based model of a inhibitory neuron coupled to a living inhibitory neuron in the stratum oriens of area CA1 of the hippocampus. Whole cell patch clamp recordings were obtained from the live cell under the following experimental protocols: (i) Fix H, vary the static synapse coupling strength gs(0) and (ii) Fix static synapse coupling strength gs(0) and vary H. In Figure ​Figure1b,1b, we present results from the second experimental protocol. We see that as predicted by our theoretical model, the time to in phase neural synchrony in the hybrid UCI network increases with increasing heterogeneity. We conclude that STDP of inhibitory synapses is a putative mechanism for robust neural synchrony in inhibitory neuronal networks.

Effects of spike-timing dependent plasticity on network dynamics in a primary visual cortex model

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Spike timing-dependent plasticity is affected by the interplay of intrinsic and network oscillations

Spike timing-dependent plasticity (STDP) is a form of Hebbian learning which is thought to underlie structure formation during development, and learning and memory in later life. In this paper we show that the intrinsic properties of the postsynaptic neuron might have a deep influence on STDP dynamics by shaping the causal correlation between the pre- and the postsynaptic spike trains. The cell-specific effect of STDP is particularly evident in the presence of an oscillatory component in a cell input. In this case, the cell-specific phase response to an oscillatory modulation biases the oscillating afferents towards potentiation or depression, depending upon the intrinsic dynamics of the postsynaptic neuron and the period of the modulation.

Formation of synchronous activity through STDP in recurrent neural networks with heterogenous delays

The Spike-Timing Dependent Plasticity (STDP) rule originates from biological evidence [1] showing that an excitatory synapse is potentiated if the EPSP (Excitatory PostSynaptic Potential) is shortly followed by the emission of an action potential at the soma, and depressed in the opposite case (when the AP is followed by the EPSP). We study the effects of STDP on random recurrent network of spiking neurons of randomly coupled elements, with random axonal transmission delays, and no external input or additional noise. The initial activity is highly irregular (Figure 1, left). After applying Spike-Timing Dependent Plasticity for a long enough time (15–50 s), the activity before periodic and synchronized (Figure 1, right).

Formation of feedforward networks and frequency synchrony by spike-timing-dependent plasticity

Spike-timing-dependent plasticity (STDP) with asymmetric learning windows is commonly found in the brain and useful for a variety of spike-based computations such as input filtering and associative memory. A natural consequence of STDP is establishment of causality in the sense that a neuron learns to fire with a lag after specific presynaptic neurons have fired. The effect of STDP on synchrony is elusive because spike synchrony implies unitary spike events of different neurons rather than a causal delayed relationship between neurons. We explore how synchrony can be facilitated by STDP in oscillator networks with a pacemaker. We show that STDP with asymmetric learning windows leads to self-organization of feedforward networks starting from the pacemaker. As a result, STDP drastically facilitates frequency synchrony. Even though differences in spike times are lessened as a result of synaptic plasticity, the finite time lag remains so that perfect spike synchrony is not realized. In contrast to traditional mechanisms of large-scale synchrony based on mutual interaction of coupled neurons, the route to synchrony discovered here is enslavement of downstream neurons by upstream ones. Facilitation of such feedforward synchrony does not occur for STDP with symmetric learning windows.

StdpC: A modern dynamic clamp

With the advancement of computer technology many novel uses of dynamic clamp have become possible. We have added new features to our dynamic clamp software StdpC (“Spike timing-dependent plasticity Clamp”) allowing such new applications while conserving the ease of use and installation of the popular earlier Dynclamp 2/4 package. Here, we introduce the new features of a waveform generator, freely programmable Hodgkin–Huxley conductances, learning synapses, graphic data displays, and a powerful scripting mechanism and discuss examples of experiments using these features. In the first example we built and ‘voltage clamped’ a conductance based model cell from a passive resistor–capacitor (RC) circuit using the dynamic clamp software to generate the voltage-dependent currents. In the second example we coupled our new spike generator through a burst detection/burst generation mechanism in a phase-dependent way to a neuron in a central pattern generator and dissected the subtle interaction between neurons, which seems to implement an information transfer through intraburst spike patterns. In the third example, making use of the new plasticity mechanism for simulated synapses, we analyzed the effect of spike timing-dependent plasticity (STDP) on synchronization revealing considerable enhancement of the entrainment of a post-synaptic neuron by a periodic spike train. These examples illustrate that with modern dynamic clamp software like StdpC, the dynamic clamp has developed beyond the mere introduction of artificial synapses or ionic conductances into neurons to a universal research tool, which might well become a standard instrument of modern electrophysiology.