Symmetric Spike-timing Dependent Plasticity Research Articles

Mean-Field Approximations With Adaptive Coupling for Networks With Spike-Timing-Dependent Plasticity.

Understanding the effect of spike-timing-dependent plasticity (STDP) is key to elucidating how neural networks change over long timescales and to design interventions aimed at modulating such networks in neurological disorders. However, progress is restricted by the significant computational cost associated with simulating neural network models with STDP and by the lack of low-dimensional description that could provide analytical insights. Phase-difference-dependent plasticity (PDDP) rules approximate STDP in phase oscillator networks, which prescribe synaptic changes based on phase differences of neuron pairs rather than differences in spike timing. Here we construct mean-field approximations for phase oscillator networks with STDP to describe part of the phase space for this very high-dimensional system. We first show that single-harmonic PDDP rules can approximate a simple form of symmetric STDP, while multiharmonic rules are required to accurately approximate causal STDP. We then derive exact expressions for the evolution of the average PDDP coupling weight in terms of network synchrony. For adaptive networks of Kuramoto oscillators that form clusters, we formulate a family of low-dimensional descriptions based on the mean-field dynamics of each cluster and average coupling weights between and within clusters. Finally, we show that such a two-cluster mean-field model can be fitted to synthetic data to provide a low-dimensional approximation of a full adaptive network with symmetric STDP. Our framework represents a step toward a low-dimensional description of adaptive networks with STDP, and could for example inform the development of new therapies aimed at maximizing the long-lasting effects of brain stimulation.

Amorphous Ga–Sn–O thin-film crosspoint-type spike-timing-dependent-plasticity device

An amorphous Ga–Sn–O (α-GTO) thin-film crosspoint-type spike-timing-dependent- plasticity (STDP) device has been developed. The α-GTO thin-film consists of omnipresent elements, can be easily deposited, and is therefore inexpensive. The STDP is a promising learning rule for neuromorphic systems. In this study, first, an α-GTO thin-film crosspoint-type STDP device is fabricated. Next, simple spiking pulses are applied as pre- and post-synapse signals. Finally, a symmetric STDP characteristic is observed, which is due to whether the electric field becomes high during the pulse application. From the potential feasibilities, we would like you to investigate and apply it as a basic technology for AI electronics.

Solution-processed oxide semiconductor-based artificial optoelectronic synapse array for spatiotemporal synaptic integration

Optoelectronic neuromorphic systems mimicking the structure and the signal processing of biological neural systems have gained significant interest due to their potential advantages such as high computing speed, high bandwidth, parallel processing, and low power requirements. Here, we demonstrate an optoelectronic synapse array consisting of solution-processed indium-gallium-zinc-oxide (IGZO) synapse devices emulating complex neural functions such as the spatiotemporal synaptic integration for neuromorphic implementation. Particularly, we adopted a vertically stacked metal-insulator-semiconductor-metal structure for IGZO synapse device to enable efficient light-induced conductance update and a low-voltage operation typically below 3 V. Using light as the stimulation, versatile synaptic functions including short-term memory/long-term memory, symmetric spike-timing dependent plasticity, and spike-number dependent plasticity were mimicked. In addition, we obtained a high recognition accuracy of handwritten digit images up to 89.4% by implementing ADAM training algorithm. Furthermore, using a crossbar structure 3 × 3 IGZO synapse array, the emulation of spatiotemporal summation was successfully carried out which is believed to be responsible for the neural encoding and the auditory recognition processes occur in the brain.

A biologically plausible supervised learning method for spiking neural networks using the symmetric STDP rule

Spiking neural networks (SNNs) possess energy-efficient potential due to event-based computation. However, supervised training of SNNs remains a challenge as spike activities are non-differentiable. Previous SNNs training methods can be generally categorized into two basic classes, i.e., backpropagation-like training methods and plasticity-based learning methods. The former methods are dependent on energy-inefficient real-valued computation and non-local transmission, as also required in artificial neural networks (ANNs), whereas the latter are either considered to be biologically implausible or exhibit poor performance. Hence, biologically plausible (bio-plausible) high-performance supervised learning (SL) methods for SNNs remain deficient. In this paper, we proposed a novel bio-plausible SNN model for SL based on the symmetric spike-timing dependent plasticity (sym-STDP) rule found in neuroscience. By combining the sym-STDP rule with bio-plausible synaptic scaling and intrinsic plasticity of the dynamic threshold, our SNN model implemented SL well and achieved good performance in the benchmark recognition task (MNIST dataset). To reveal the underlying mechanism of our SL model, we visualized both layer-based activities and synaptic weights using the t-distributed stochastic neighbor embedding (t-SNE) method after training and found that they were well clustered, thereby demonstrating excellent classification ability. Furthermore, to verify the robustness of our model, we trained it on another more realistic dataset (Fashion-MNIST), which also showed good performance. As the learning rules were bio-plausible and based purely on local spike events, our model could be easily applied to neuromorphic hardware for online training and may be helpful for understanding SL information processing at the synaptic level in biological neural systems.

Recurrent network model for learning goal-directed sequences through reverse replay.

Reverse replay of hippocampal place cells occurs frequently at rewarded locations, suggesting its contribution to goal-directed path learning. Symmetric spike-timing dependent plasticity (STDP) in CA3 likely potentiates recurrent synapses for both forward (start to goal) and reverse (goal to start) replays during sequential activation of place cells. However, how reverse replay selectively strengthens forward synaptic pathway is unclear. Here, we show computationally that firing sequences bias synaptic transmissions to the opposite direction of propagation under symmetric STDP in the co-presence of short-term synaptic depression or afterdepolarization. We demonstrate that significant biases are created in biologically realistic simulation settings, and this bias enables reverse replay to enhance goal-directed spatial memory on a W-maze. Further, we show that essentially the same mechanism works in a two-dimensional open field. Our model for the first time provides the mechanistic account for the way reverse replay contributes to hippocampal sequence learning for reward-seeking spatial navigation.

Robust path planning by propagating rhythmic spiking activity in a hippocampal network model

It is believed that humans and animals like rodents and bats navigate in a familiar environment using a cognitive map. Yet, how maps are previously learned when exploring a novel environment and then used to plan routes to specific goals remains unclear. We propose here a biologically inspired method, called Multiwave, with two main ingredients: (i) a symmetric spike-timing dependent plasticity rule that compels the connectivity to map the environment, and (ii) periodic wavefronts propagating through the network to direct the agent towards the goal and increase robustness to noise. The modeling involves a detailed neural network for biological plausibility and a simplified equivalent network, better suited for robotic implementation. Using both simulations and robotic experiments, we show the efficiency of Multiwave for path planning. Multiwave is relevant not only to neuroscience, as the detailed model is biologically grounded, but also to robotic applications, as the simplified model is suitable for analog implementation on neuromorphic hardware.

Symmetric spike timing-dependent plasticity at CA3-CA3 synapses optimizes storage and recall in autoassociative networks.

CA3–CA3 recurrent excitatory synapses are thought to play a key role in memory storage and pattern completion. Whether the plasticity properties of these synapses are consistent with their proposed network functions remains unclear. Here, we examine the properties of spike timing-dependent plasticity (STDP) at CA3–CA3 synapses. Low-frequency pairing of excitatory postsynaptic potentials (EPSPs) and action potentials (APs) induces long-term potentiation (LTP), independent of temporal order. The STDP curve is symmetric and broad (half-width ∼150 ms). Consistent with these STDP induction properties, AP–EPSP sequences lead to supralinear summation of spine [Ca2+] transients. Furthermore, afterdepolarizations (ADPs) following APs efficiently propagate into dendrites of CA3 pyramidal neurons, and EPSPs summate with dendritic ADPs. In autoassociative network models, storage and recall are more robust with symmetric than with asymmetric STDP rules. Thus, a specialized STDP induction rule allows reliable storage and recall of information in the hippocampal CA3 network.

Effects of adaptation and synaptic plasticity on synchronization of coupled oscillating neurons

Synchronized oscillating activity in cortical circuits is a prevalent phenomenon and considered to be fundamental to cognitive function, selective attention and consciousness. In the present study we describe how spike-frequency adaptation (SFA) affects the synchronization properties of coupled neurons driven to repetitive firing, in dependence of axonal conduction delays and spike-timing-dependent plasticity (STDP). We analyze networks of adaptive exponential integrate-and-fire (aEIF) neurons by applying phase reduction theory based on the assumption of weak coupling [1]. We calculate infinitesimal phase response curves (PRCs) and combine these with conductance based AMPA- and GABA-like synapses, in order to obtain interaction functions which determine the dynamics of the reduced phase network. This allows for the identification of stable synchronous and out-of-phase locked states of coupled pairs. The aEIF model involves a subthreshold as well as a spike-triggered adaptation parameter which can be related to the M-current and the after hyperpolarization current, respectively, both producing SFA. The former causes a transition of the PRC from type I (only advancing) to type II (advancing and delaying) whereas the latter determines its skewness. We find that SFA in coupled excitatory pairs synchronizes oscillations as long as conduction delays are negligible, in agreement with [2]. When the delays are increased, the stable states shift from in-phase towards anti-phase locking (see Figure ​Figure1,1, middle). Inhibitory pairs show stable synchrony independent of the conduction delay, as well as anti-phase locking for short delays. Our analysis reveals that excitatory-inhibitory pairs do not phase lock if both neurons have type I PRCs. Type II pairs on the other hand have stable phase locked states even for diverging synaptic strengths (see Figure ​Figure1,1, bottom). When the coupling strengths are modified under the influence of a symmetric STDP rule, such pairs phase lock at a phase difference slightly above the conduction delay, for small delays. We complement these results on pairs by numerical simulations of phase as well as spiking aEIF networks, respectively, demonstrating that STDP promotes pairwise phase locking (clustering) in networks of excitatory neurons with low spike-triggered adaptation, where the number of clusters depends on the conduction delay. Figure 1 Top: Infinitesimal PRCs with low SFA (A), large subthreshold adaptation (B), large spike-triggered adaptation component (C). Middle: Stable (dark blue) and unstable (light blue) phase locked states of excitatory pairs in dependence of the conduction delay. ...

GABA inhibition modulates NMDA-R mediated spike timing dependent plasticity (STDP) in a biophysical model

Spike timing dependent plasticity (STDP) has been demonstrated in various neural systems of many animals. It has been shown that STDP depends on the target and the location of the synapse and is dynamically regulated by the activity of adjacent synapses, the presence of postsynaptic calcium, presynaptic GABA inhibition or the action of neuromodulators. Recent experimental evidence has reported that the profile of STDP in the CA1 pyramidal neuron can be classified into two types depending on its dendritic location: (1) A symmetric STDP profile in the proximal to the soma dendrites, and (2) an asymmetric one in the distal dendrites. Bicuculline application revealed that GABA(A) is responsible for the symmetry of the STDP curve. We investigate via computer simulations how GABA(A) shapes the STDP profile in the CA1 pyramidal neuron dendrites when it is driven by excitatory spike pairs (doublets). The model constructed uses calcium as the postsynaptic signaling agent for STDP and is shown to be consistent with classical long-term potentiation (LTP) and long-term depression (LTD) induced by several doublet stimulation paradigms in the absence of inhibition. Overall, simulation results provide computational evidence for the first time that the switch between the symmetrical and the asymmetrical STDP operational modes is indeed due to GABA inhibition. Furthermore, gamma frequency inhibition and not theta one is responsible for the transition from asymmetry-to-symmetry. The resulted symmetrical STDP profile is centered at +10 ms with two distinct LTD tails at -10 and +40 ms. Finally, the asymmetry-to-symmetry transition is strongly dependent on the strength (conductance) of inhibition and its relative onset with respect to pre- and postsynaptic spike stimulation.