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1
BADOUAL, MATHILDE, QUAN ZOU, ANDREW P. DAVISON, MICHAEL RUDOLPH, THIERRY BAL, YVES FRÉGNAC, and ALAIN DESTEXHE. "BIOPHYSICAL AND PHENOMENOLOGICAL MODELS OF MULTIPLE SPIKE INTERACTIONS IN SPIKE-TIMING DEPENDENT PLASTICITY." International Journal of Neural Systems 16, no. 02 (April 2006): 79–97. http://dx.doi.org/10.1142/s0129065706000524.
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Spike-timing dependent plasticity (STDP) is a form of associative synaptic modification which depends on the respective timing of pre- and post-synaptic spikes. The biophysical mechanisms underlying this form of plasticity are currently not known. We present here a biophysical model which captures the characteristics of STDP, such as its frequency dependency, and the effects of spike pair or spike triplet interactions. We also make links with other well-known plasticity rules. A simplified phenomenological model is also derived, which should be useful for fast numerical simulation and analytical investigation of the impact of STDP at the network level.
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Uramoto, Takumi, and Hiroyuki Torikai. "A Calcium-Based Simple Model of Multiple Spike Interactions in Spike-Timing-Dependent Plasticity." Neural Computation 25, no. 7 (July 2013): 1853–69. http://dx.doi.org/10.1162/neco_a_00462.
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Spike-timing-dependent plasticity (STDP) is a form of synaptic modification that depends on the relative timings of presynaptic and postsynaptic spikes. In this letter, we proposed a calcium-based simple STDP model, described by an ordinary differential equation having only three state variables: one represents the density of intracellular calcium, one represents a fraction of open state NMDARs, and one represents the synaptic weight. We shown that in spite of its simplicity, the model can reproduce the properties of the plasticity that have been experimentally measured in various brain areas (e.g., layer 2/3 and 5 visual cortical slices, hippocampal cultures, and layer 2/3 somatosensory cortical slices) with respect to various patterns of presynaptic and postsynaptic spikes. In addition, comparisons with other STDP models are made, and the significance and advantages of the proposed model are discussed.
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Dan, Yang, and Mu-Ming Poo. "Spike Timing-Dependent Plasticity: From Synapse to Perception." Physiological Reviews 86, no. 3 (July 2006): 1033–48. http://dx.doi.org/10.1152/physrev.00030.2005.
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Information in the nervous system may be carried by both the rate and timing of neuronal spikes. Recent findings of spike timing-dependent plasticity (STDP) have fueled the interest in the potential roles of spike timing in processing and storage of information in neural circuits. Induction of long-term potentiation (LTP) and long-term depression (LTD) in a variety of in vitro and in vivo systems has been shown to depend on the temporal order of pre- and postsynaptic spiking. Spike timing-dependent modification of neuronal excitability and dendritic integration was also observed. Such STDP at the synaptic and cellular level is likely to play important roles in activity-induced functional changes in neuronal receptive fields and human perception.
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Echeveste, Rodrigo, and Claudius Gros. "Two-Trace Model for Spike-Timing-Dependent Synaptic Plasticity." Neural Computation 27, no. 3 (March 2015): 672–98. http://dx.doi.org/10.1162/neco_a_00707.
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We present an effective model for timing-dependent synaptic plasticity (STDP) in terms of two interacting traces, corresponding to the fraction of activated NMDA receptors and the [Formula: see text] concentration in the dendritic spine of the postsynaptic neuron. This model intends to bridge the worlds of existing simplistic phenomenological rules and highly detailed models, thus constituting a practical tool for the study of the interplay of neural activity and synaptic plasticity in extended spiking neural networks. For isolated pairs of pre- and postsynaptic spikes, the standard pairwise STDP rule is reproduced, with appropriate parameters determining the respective weights and timescales for the causal and the anticausal contributions. The model contains otherwise only three free parameters, which can be adjusted to reproduce triplet nonlinearities in hippocampal culture and cortical slices. We also investigate the transition from time-dependent to rate-dependent plasticity occurring for both correlated and uncorrelated spike patterns.
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Florian, Răzvan V. "Reinforcement Learning Through Modulation of Spike-Timing-Dependent Synaptic Plasticity." Neural Computation 19, no. 6 (June 2007): 1468–502. http://dx.doi.org/10.1162/neco.2007.19.6.1468.
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The persistent modification of synaptic efficacy as a function of the relative timing of pre- and postsynaptic spikes is a phenomenon known as spike-timing-dependent plasticity (STDP). Here we show that the modulation of STDP by a global reward signal leads to reinforcement learning. We first derive analytically learning rules involving reward-modulated spike-timing-dependent synaptic and intrinsic plasticity, by applying a reinforcement learning algorithm to the stochastic spike response model of spiking neurons. These rules have several features common to plasticity mechanisms experimentally found in the brain. We then demonstrate in simulations of networks of integrate-and-fire neurons the efficacy of two simple learning rules involving modulated STDP. One rule is a direct extension of the standard STDP model (modulated STDP), and the other one involves an eligibility trace stored at each synapse that keeps a decaying memory of the relationships between the recent pairs of pre- and postsynaptic spike pairs (modulated STDP with eligibility trace). This latter rule permits learning even if the reward signal is delayed. The proposed rules are able to solve the XOR problem with both rate coded and temporally coded input and to learn a target output firing-rate pattern. These learning rules are biologically plausible, may be used for training generic artificial spiking neural networks, regardless of the neural model used, and suggest the experimental investigation in animals of the existence of reward-modulated STDP.
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Lightheart, Toby, Steven Grainger, and Tien-Fu Lu. "Spike-Timing-Dependent Construction." Neural Computation 25, no. 10 (October 2013): 2611–45. http://dx.doi.org/10.1162/neco_a_00501.
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Spike-timing-dependent construction (STDC) is the production of new spiking neurons and connections in a simulated neural network in response to neuron activity. Following the discovery of spike-timing-dependent plasticity (STDP), significant effort has gone into the modeling and simulation of adaptation in spiking neural networks (SNNs). Limitations in computational power imposed by network topology, however, constrain learning capabilities through connection weight modification alone. Constructive algorithms produce new neurons and connections, allowing automatic structural responses for applications of unknown complexity and nonstationary solutions. A conceptual analogy is developed and extended to theoretical conditions for modeling synaptic plasticity as network construction. Generalizing past constructive algorithms, we propose a framework for the design of novel constructive SNNs and demonstrate its application in the development of simulations for the validation of developed theory. Potential directions of future research and applications of STDC for biological modeling and machine learning are also discussed.
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Lu, Hui, Hyungju Park, and Mu-Ming Poo. "Spike-timing-dependent BDNF secretion and synaptic plasticity." Philosophical Transactions of the Royal Society B: Biological Sciences 369, no. 1633 (January 5, 2014): 20130132. http://dx.doi.org/10.1098/rstb.2013.0132.
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In acute hippocampal slices, we found that the presence of extracellular brain-derived neurotrophic factor (BDNF) is essential for the induction of spike-timing-dependent long-term potentiation (tLTP). To determine whether BDNF could be secreted from postsynaptic dendrites in a spike-timing-dependent manner, we used a reduced system of dissociated hippocampal neurons in culture. Repetitive pairing of iontophoretically applied glutamate pulses at the dendrite with neuronal spikes could induce persistent alterations of glutamate-induced responses at the same dendritic site in a manner that mimics spike-timing-dependent plasticity (STDP)—the glutamate-induced responses were potentiated and depressed when the glutamate pulses were applied 20 ms before and after neuronal spiking, respectively. By monitoring changes in the green fluorescent protein (GFP) fluorescence at the dendrite of hippocampal neurons expressing GFP-tagged BDNF, we found that pairing of iontophoretic glutamate pulses with neuronal spiking resulted in BDNF secretion from the dendrite at the iontophoretic site only when the glutamate pulses were applied within a time window of approximately 40 ms prior to neuronal spiking, consistent with the timing requirement of synaptic potentiation via STDP. Thus, BDNF is required for tLTP and BDNF secretion could be triggered in a spike-timing-dependent manner from the postsynaptic dendrite.
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Leen, Todd K., and Robert Friel. "Stochastic Perturbation Methods for Spike-Timing-Dependent Plasticity." Neural Computation 24, no. 5 (May 2012): 1109–46. http://dx.doi.org/10.1162/neco_a_00267.
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Online machine learning rules and many biological spike-timing-dependent plasticity (STDP) learning rules generate jump process Markov chains for the synaptic weights. We give a perturbation expansion for the dynamics that, unlike the usual approximation by a Fokker-Planck equation (FPE), is well justified. Our approach extends the related system size expansion by giving an expansion for the probability density as well as its moments. We apply the approach to two observed STDP learning rules and show that in regimes where the FPE breaks down, the new perturbation expansion agrees well with Monte Carlo simulations. The methods are also applicable to the dynamics of stochastic neural activity. Like previous ensemble analyses of STDP, we focus on equilibrium solutions, although the methods can in principle be applied to transients as well.
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Hunzinger, Jason F., Victor H. Chan, and Robert C. Froemke. "Learning complex temporal patterns with resource-dependent spike timing-dependent plasticity." Journal of Neurophysiology 108, no. 2 (July 15, 2012): 551–66. http://dx.doi.org/10.1152/jn.01150.2011.
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Studies of spike timing-dependent plasticity (STDP) have revealed that long-term changes in the strength of a synapse may be modulated substantially by temporal relationships between multiple presynaptic and postsynaptic spikes. Whereas long-term potentiation (LTP) and long-term depression (LTD) of synaptic strength have been modeled as distinct or separate functional mechanisms, here, we propose a new shared resource model. A functional consequence of our model is fast, stable, and diverse unsupervised learning of temporal multispike patterns with a biologically consistent spiking neural network. Due to interdependencies between LTP and LTD, dendritic delays, and proactive homeostatic aspects of the model, neurons are equipped to learn to decode temporally coded information within spike bursts. Moreover, neurons learn spike timing with few exposures in substantial noise and jitter. Surprisingly, despite having only one parameter, the model also accurately predicts in vitro observations of STDP in more complex multispike trains, as well as rate-dependent effects. We discuss candidate commonalities in natural long-term plasticity mechanisms.
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Mendes, Alexandre, Gaetan Vignoud, Sylvie Perez, Elodie Perrin, Jonathan Touboul, and Laurent Venance. "Concurrent Thalamostriatal and Corticostriatal Spike-Timing-Dependent Plasticity and Heterosynaptic Interactions Shape Striatal Plasticity Map." Cerebral Cortex 30, no. 8 (March 7, 2020): 4381–401. http://dx.doi.org/10.1093/cercor/bhaa024.
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Abstract The striatum integrates inputs from the cortex and thalamus, which display concomitant or sequential activity. The striatum assists in forming memory, with acquisition of the behavioral repertoire being associated with corticostriatal (CS) plasticity. The literature has mainly focused on that CS plasticity, and little remains known about thalamostriatal (TS) plasticity rules or CS and TS plasticity interactions. We undertook here the study of these plasticity rules. We found bidirectional Hebbian and anti-Hebbian spike-timing-dependent plasticity (STDP) at the thalamic and cortical inputs, respectively, which were driving concurrent changes at the striatal synapses. Moreover, TS- and CS-STDP induced heterosynaptic plasticity. We developed a calcium-based mathematical model of the coupled TS and CS plasticity, and simulations predict complex changes in the CS and TS plasticity maps depending on the precise cortex–thalamus–striatum engram. These predictions were experimentally validated using triplet-based STDP stimulations, which revealed the significant remodeling of the CS-STDP map upon TS activity, which is notably the induction of the LTD areas in the CS-STDP for specific timing regimes. TS-STDP exerts a greater influence on CS plasticity than CS-STDP on TS plasticity. These findings highlight the major impact of precise timing in cortical and thalamic activity for the memory engram of striatal synapses.
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Morrison, Abigail, Ad Aertsen, and Markus Diesmann. "Spike-Timing-Dependent Plasticity in Balanced Random Networks." Neural Computation 19, no. 6 (June 2007): 1437–67. http://dx.doi.org/10.1162/neco.2007.19.6.1437.
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The balanced random network model attracts considerable interest because it explains the irregular spiking activity at low rates and large membrane potential fluctuations exhibited by cortical neurons in vivo. In this article, we investigate to what extent this model is also compatible with the experimentally observed phenomenon of spike-timing-dependent plasticity (STDP). Confronted with the plethora of theoretical models for STDP available, we reexamine the experimental data. On this basis, we propose a novel STDP update rule, with a multiplicative dependence on the synaptic weight for depression, and a power law dependence for potentiation. We show that this rule, when implemented in large, balanced networks of realistic connectivity and sparseness, is compatible with the asynchronous irregular activity regime. The resultant equilibrium weight distribution is unimodal with fluctuating individual weight trajectories and does not exhibit development of structure. We investigate the robustness of our results with respect to the relative strength of depression. We introduce synchronous stimulation to a group of neurons and demonstrate that the decoupling of this group from the rest of the network is so severe that it cannot effectively control the spiking of other neurons, even those with the highest convergence from this group.
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Legenstein, Robert, Christian Naeger, and Wolfgang Maass. "What Can a Neuron Learn with Spike-Timing-Dependent Plasticity?" Neural Computation 17, no. 11 (November 1, 2005): 2337–82. http://dx.doi.org/10.1162/0899766054796888.
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Spiking neurons are very flexible computational modules, which can implement with different values of their adjustable synaptic parameters an enormous variety of different transformations F from input spike trains to output spike trains. We examine in this letter the question to what extent a spiking neuron with biologically realistic models for dynamic synapses can be taught via spike-timing-dependent plasticity (STDP) to implement a given transformation F. We consider a supervised learning paradigm where during training, the output of the neuron is clamped to the target signal (teacher forcing). The well-known perceptron convergence theorem asserts the convergence of a simple supervised learning algorithm for drastically simplified neuron models (McCulloch-Pitts neurons). We show that in contrast to the perceptron convergence theorem, no theoretical guarantee can be given for the convergence of STDP with teacher forcing that holds for arbitrary input spike patterns. On the other hand, we prove that average case versions of the perceptron convergence theorem hold for STDP in the case of uncorrelated and correlated Poisson input spike trains and simple models for spiking neurons. For a wide class of cross-correlation functions of the input spike trains, the resulting necessary and sufficient condition can be formulated in terms of linear separability, analogously as the well-known condition of learnability by perceptrons. However, the linear separability criterion has to be applied here to the columns of the correlation matrix of the Poisson input. We demonstrate through extensive computer simulations that the theoretically predicted convergence of STDP with teacher forcing also holds for more realistic models for neurons, dynamic synapses, and more general input distributions. In addition, we show through computer simulations that these positive learning results hold not only for the common interpretation of STDP, where STDP changes the weights of synapses, but also for a more realistic interpretation suggested by experimental data where STDP modulates the initial release probability of dynamic synapses.
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Appleby, Peter A., and Terry Elliott. "Stable Competitive Dynamics Emerge from Multispike Interactions in a Stochastic Model of Spike-Timing-Dependent Plasticity." Neural Computation 18, no. 10 (October 2006): 2414–64. http://dx.doi.org/10.1162/neco.2006.18.10.2414.
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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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Farries, Michael A., and Adrienne L. Fairhall. "Reinforcement Learning With Modulated Spike Timing–Dependent Synaptic Plasticity." Journal of Neurophysiology 98, no. 6 (December 2007): 3648–65. http://dx.doi.org/10.1152/jn.00364.2007.
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Spike timing–dependent synaptic plasticity (STDP) has emerged as the preferred framework linking patterns of pre- and postsynaptic activity to changes in synaptic strength. Although synaptic plasticity is widely believed to be a major component of learning, it is unclear how STDP itself could serve as a mechanism for general purpose learning. On the other hand, algorithms for reinforcement learning work on a wide variety of problems, but lack an experimentally established neural implementation. Here, we combine these paradigms in a novel model in which a modified version of STDP achieves reinforcement learning. We build this model in stages, identifying a minimal set of conditions needed to make it work. Using a performance-modulated modification of STDP in a two-layer feedforward network, we can train output neurons to generate arbitrarily selected spike trains or population responses. Furthermore, a given network can learn distinct responses to several different input patterns. We also describe in detail how this model might be implemented biologically. Thus our model offers a novel and biologically plausible implementation of reinforcement learning that is capable of training a neural population to produce a very wide range of possible mappings between synaptic input and spiking output.
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Wang, W., G. Pedretti, V. Milo, R. Carboni, A. Calderoni, N. Ramaswamy, A. S. Spinelli, and D. Ielmini. "Computing of temporal information in spiking neural networks with ReRAM synapses." Faraday Discussions 213 (2019): 453–69. http://dx.doi.org/10.1039/c8fd00097b.
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This work addresses the methodology and implementation of a neuromorphic SNN system to compute the temporal information among neural spikes using ReRAM synapses capable of spike-timing dependent plasticity (STDP).
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Gidon, Albert, and Idan Segev. "Spike-Timing–Dependent Synaptic Plasticity and Synaptic Democracy in Dendrites." Journal of Neurophysiology 101, no. 6 (June 2009): 3226–34. http://dx.doi.org/10.1152/jn.91349.2008.
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We explored in a computational study the effect of dendrites on excitatory synapses undergoing spike-timing–dependent plasticity (STDP), using both cylindrical dendritic models and reconstructed dendritic trees. We show that even if the initial strength, gpeak, of distal synapses is augmented in a location independent manner, the efficacy of distal synapses diminishes following STDP and proximal synapses would eventually dominate. Indeed, proximal synapses always win over distal synapses following linear STDP rule, independent of the initial synaptic strength distribution in the dendritic tree. This effect is more pronounced as the dendritic cable length increases but it does not depend on the dendritic branching structure. Adding a small multiplicative component to the linear STDP rule, whereby already strong synapses tend to be less potentiated than depressed (and vice versa for weak synapses) did partially “save” distal synapses from “dying out.” Another successful strategy for balancing the efficacy of distal and proximal synapses following STDP is to increase the upper bound for the synaptic conductance ( gmax) with distance from the soma. We conclude by discussing an experiment for assessing which of these possible strategies might actually operate in dendrites.
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Tavanaei, Amirhossein, and Anthony Maida. "BP-STDP: Approximating backpropagation using spike timing dependent plasticity." Neurocomputing 330 (February 2019): 39–47. http://dx.doi.org/10.1016/j.neucom.2018.11.014.
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Rubin, Jonathan E., Richard C. Gerkin, Guo-Qiang Bi, and Carson C. Chow. "Calcium Time Course as a Signal for Spike-Timing–Dependent Plasticity." Journal of Neurophysiology 93, no. 5 (May 2005): 2600–2613. http://dx.doi.org/10.1152/jn.00803.2004.
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Calcium has been proposed as a postsynaptic signal underlying synaptic spike-timing–dependent plasticity (STDP). We examine this hypothesis with computational modeling based on experimental results from hippocampal cultures, some of which are presented here, in which pairs and triplets of pre- and postsynaptic spikes induce potentiation and depression in a temporally asymmetric way. Specifically, we present a set of model biochemical detectors, based on plausible molecular pathways, which make direct use of the time course of the calcium signal to reproduce these experimental STDP results. Our model features a modular structure, in which long-term potentiation (LTP) and depression (LTD) components compete to determine final plasticity outcomes; one aspect of this competition is a veto through which appropriate calcium time courses suppress LTD. Simulations of our model are also shown to be consistent with classical LTP and LTD induced by several presynaptic stimulation paradigms. Overall, our results provide computational evidence that, while the postsynaptic calcium time course contains sufficient information to distinguish various experimental long-term plasticity paradigms, small changes in the properties of back-propagation of action potentials or in synaptic dynamics can alter the calcium time course in ways that will significantly affect STDP induction by any detector based exclusively on postsynaptic calcium. This may account for the variability of STDP outcomes seen within hippocampal cultures, under repeated application of a single experimental protocol, as well as for that seen in multiple spike experiments across different systems.
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Nobukawa, Sou, Haruhiko Nishimura, and Teruya Yamanishi. "Pattern Classification by Spiking Neural Networks Combining Self-Organized and Reward-Related Spike-Timing-Dependent Plasticity." Journal of Artificial Intelligence and Soft Computing Research 9, no. 4 (October 1, 2019): 283–91. http://dx.doi.org/10.2478/jaiscr-2019-0009.
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Abstract Many recent studies have applied to spike neural networks with spike-timing-dependent plasticity (STDP) to machine learning problems. The learning abilities of dopamine-modulated STDP (DA-STDP) for reward-related synaptic plasticity have also been gathering attention. Following these studies, we hypothesize that a network structure combining self-organized STDP and reward-related DA-STDP can solve the machine learning problem of pattern classification. Therefore, we studied the ability of a network in which recurrent spiking neural networks are combined with STDP for non-supervised learning, with an output layer joined by DA-STDP for supervised learning, to perform pattern classification. We confirmed that this network could perform pattern classification using the STDP effect for emphasizing features of the input spike pattern and DA-STDP supervised learning. Therefore, our proposed spiking neural network may prove to be a useful approach for machine learning problems.
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Gilson, Matthieu, Moritz Bürck, Anthony N. Burkitt, and J. Leo van Hemmen. "Frequency Selectivity Emerging from Spike-Timing-Dependent Plasticity." Neural Computation 24, no. 9 (September 2012): 2251–79. http://dx.doi.org/10.1162/neco_a_00331.
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Periodic neuronal activity has been observed in various areas of the brain, from lower sensory to higher cortical levels. Specific frequency components contained in this periodic activity can be identified by a neuronal circuit that behaves as a bandpass filter with given preferred frequency, or best modulation frequency (BMF). For BMFs typically ranging from 10 to 200 Hz, a plausible and minimal configuration consists of a single neuron with adjusted excitatory and inhibitory synaptic connections. The emergence, however, of such a neuronal circuitry is still unclear. In this letter, we demonstrate how spike-timing-dependent plasticity (STDP) can give rise to frequency-dependent learning, thus leading to an input selectivity that enables frequency identification. We use an in-depth mathematical analysis of the learning dynamics in a population of plastic inhibitory connections. These provide inhomogeneous postsynaptic responses that depend on their dendritic location. We find that synaptic delays play a crucial role in organizing the weight specialization induced by STDP. Under suitable conditions on the synaptic delays and postsynaptic potentials (PSPs), the BMF of a neuron after learning can match the training frequency. In particular, proximal (distal) synapses with shorter (longer) dendritic delay and somatically measured PSP time constants respond better to higher (lower) frequencies. As a result, the neuron will respond maximally to any stimulating frequency (in a given range) with which it has been trained in an unsupervised manner. The model predicts that synapses responding to a given BMF form clusters on dendritic branches.
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Pool, R. Rossi, and G. Mato. "Spike-Timing-Dependent Plasticity and Reliability Optimization: The Role of Neuron Dynamics." Neural Computation 23, no. 7 (July 2011): 1768–89. http://dx.doi.org/10.1162/neco_a_00140.
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Plastic changes in synaptic efficacy can depend on the time ordering of presynaptic and postsynaptic spikes. This phenomenon is called spike-timing-dependent plasticity (STDP). One of the most striking aspects of this plasticity mechanism is that the STDP windows display a great variety of forms in different parts of the nervous system. We explore this issue from a theoretical point of view. We choose as the optimization principle the minimization of conditional entropy or maximization of reliability in the transmission of information. We apply this principle to two types of postsynaptic dynamics, designated type I and type II. The first is characterized as being an integrator, while the second is a resonator. We find that, depending on the parameters of the models, the optimization principle can give rise to a wide variety of STDP windows, such as antisymmetric Hebbian, predominantly depressing or symmetric with one positive region and two lateral negative regions. We can relate each of these forms to the dynamical behavior of the different models. We also propose experimental tests to assess the validity of the optimization principle.
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Verhoog, Matthijs B., and Huibert D. Mansvelder. "Presynaptic Ionotropic Receptors Controlling and Modulating the Rules for Spike Timing-Dependent Plasticity." Neural Plasticity 2011 (2011): 1–11. http://dx.doi.org/10.1155/2011/870763.
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Throughout life, activity-dependent changes in neuronal connection strength enable the brain to refine neural circuits and learn based on experience. In line with predictions made by Hebb, synapse strength can be modified depending on the millisecond timing of action potential firing (STDP). The sign of synaptic plasticity depends on the spike order of presynaptic and postsynaptic neurons. Ionotropic neurotransmitter receptors, such as NMDA receptors and nicotinic acetylcholine receptors, are intimately involved in setting the rules for synaptic strengthening and weakening. In addition, timing rules for STDP within synapses are not fixed. They can be altered by activation of ionotropic receptors located at, or close to, synapses. Here, we will highlight studies that uncovered how network actions control and modulate timing rules for STDP by activating presynaptic ionotropic receptors. Furthermore, we will discuss how interaction between different types of ionotropic receptors may create “timing” windows during which particular timing rules lead to synaptic changes.
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Robinson, Brian S., Theodore W. Berger, and Dong Song. "Identification of Stable Spike-Timing-Dependent Plasticity from Spiking Activity with Generalized Multilinear Modeling." Neural Computation 28, no. 11 (November 2016): 2320–51. http://dx.doi.org/10.1162/neco_a_00883.
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Characterization of long-term activity-dependent plasticity from behaviorally driven spiking activity is important for understanding the underlying mechanisms of learning and memory. In this letter, we present a computational framework for quantifying spike-timing-dependent plasticity (STDP) during behavior by identifying a functional plasticity rule solely from spiking activity. First, we formulate a flexible point-process spiking neuron model structure with STDP, which includes functions that characterize the stationary and plastic properties of the neuron. The STDP model includes a novel function for prolonged plasticity induction, as well as a more typical function for synaptic weight change based on the relative timing of input-output spike pairs. Consideration for system stability is incorporated with weight-dependent synaptic modification. Next, we formalize an estimation technique using a generalized multilinear model (GMLM) structure with basis function expansion. The weight-dependent synaptic modification adds a nonlinearity to the model, which is addressed with an iterative unconstrained optimization approach. Finally, we demonstrate successful model estimation on simulated spiking data and show that all model functions can be estimated accurately with this method across a variety of simulation parameters, such as number of inputs, output firing rate, input firing type, and simulation time. Since this approach requires only naturally generated spikes, it can be readily applied to behaving animal studies to characterize the underlying mechanisms of learning and memory.
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Guyonneau, Rudy, Rufin VanRullen, and Simon J. Thorpe. "Neurons Tune to the Earliest Spikes Through STDP." Neural Computation 17, no. 4 (April 1, 2005): 859–79. http://dx.doi.org/10.1162/0899766053429390.
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Spike timing-dependent plasticity (STDP) is a learning rule that modifies the strength of a neuron's synapses as a function of the precise temporal relations between input and output spikes. In many brains areas, temporal aspects of spike trains have been found to be highly reproducible. How will STDP affect a neuron's behavior when it is repeatedly presented with the same input spike pattern? We show in this theoretical study that repeated inputs systematically lead to a shaping of the neuron's selectivity, emphasizing its very first input spikes, while steadily decreasing the postsynaptic response latency. This was obtained under various conditions of background noise, and even under conditions where spiking latencies and firing rates, or synchrony, provided conflicting informations. The key role of first spikes demonstrated here provides further support for models using a single wave of spikes to implement rapid neural processing.
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SHIN, CHANG-WOO, and SEUNGHWAN KIM. "HIERARCHICAL MODULARITY OF THE FUNCTIONAL NEURAL NETWORK ORGANIZED BY SPIKE TIMING DEPENDENT SYNAPTIC PLASTICITY." International Journal of Modern Physics B 21, no. 23n24 (September 30, 2007): 4124–29. http://dx.doi.org/10.1142/s021797920704530x.
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We study the emergent functional neural network organized by synaptic reorganization by the spike timing dependent synaptic plasticity (STDP). We show that small-world and scale-free functional structures organized by STDP, in the case of synaptic balance, exhibit hierarchial modularity.
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Ocker, Gabriel Koch, and Brent Doiron. "Training and Spontaneous Reinforcement of Neuronal Assemblies by Spike Timing Plasticity." Cerebral Cortex 29, no. 3 (February 3, 2018): 937–51. http://dx.doi.org/10.1093/cercor/bhy001.
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Abstract The synaptic connectivity of cortex is plastic, with experience shaping the ongoing interactions between neurons. Theoretical studies of spike timing-dependent plasticity (STDP) have focused on either just pairs of neurons or large-scale simulations. A simple analytic account for how fast spike time correlations affect both microscopic and macroscopic network structure is lacking. We develop a low-dimensional mean field theory for STDP in recurrent networks and show the emergence of assemblies of strongly coupled neurons with shared stimulus preferences. After training, this connectivity is actively reinforced by spike train correlations during the spontaneous dynamics. Furthermore, the stimulus coding by cell assemblies is actively maintained by these internally generated spiking correlations, suggesting a new role for noise correlations in neural coding. Assembly formation has often been associated with firing rate-based plasticity schemes; our theory provides an alternative and complementary framework, where fine temporal correlations and STDP form and actively maintain learned structure in cortical networks.
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Elliott, Terry. "A Non-Markovian Random Walk Underlies a Stochastic Model of Spike-Timing-Dependent Plasticity." Neural Computation 22, no. 5 (May 2010): 1180–230. http://dx.doi.org/10.1162/neco.2009.06-09-1038.
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A stochastic model of spike-timing-dependent plasticity (STDP) proposes that spike timing influences the probability but not the amplitude of synaptic strength change at single synapses. The classic, biphasic STDP profile emerges as a spatial average over many synapses presented with a single spike pair or as a temporal average over a single synapse presented with many spike pairs. We have previously shown that the model accounts for a variety of experimental data, including spike triplet results, and has a number of desirable theoretical properties, including being entirely self-stabilizing in all regions of parameter space. Our earlier analyses of the model have employed cumbersome spike-to-spike averaging arguments to derive results. Here, we show that the model can be reformulated as a non-Markovian random walk in synaptic strength, the step sizes being fixed as postulated. This change of perspective greatly simplifies earlier calculations by integrating out the proposed switch mechanism by which changes in strength are driven and instead concentrating on the changes in strength themselves. Moreover, this change of viewpoint is generative, facilitating further calculations that would be intractable, if not impossible, with earlier approaches. We prepare the machinery here for these later calculations but also briefly indicate how this machinery may be used by considering two particular applications.
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Karmarkar, Uma R., and Dean V. Buonomano. "A Model of Spike-Timing Dependent Plasticity: One or Two Coincidence Detectors?" Journal of Neurophysiology 88, no. 1 (July 1, 2002): 507–13. http://dx.doi.org/10.1152/jn.2002.88.1.507.
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In spike-timing dependent plasticity (STDP), synapses exhibit LTD or LTP depending on the order of activity in the presynaptic and postsynaptic cells. LTP occurs when a single presynaptic spike precedes a postsynaptic one (a positive interspike interval, or ISI), while the reverse order of activity (a negative ISI) produces LTD. A fundamental question is whether the “standard model” of plasticity in which moderate increases in Ca2+ influx through the N-methyl-d-aspartate (NMDA) channels induce LTD and large increases induce LTP, can account for the order and interval sensitivity of STDP. To examine this issue we developed a model that captures postsynaptic Ca2+ influx dynamics and the associativity of the NMDA receptors. While this model can generate both LTD and LTP, it predicts that LTD will be observed at both negative and positive ISIs. This is because longer and longer positive ISIs induce monotonically decreasing levels of Ca2+, which eventually fall into the same range that produced LTD at negative ISIs. A second model that incorporated a second coincidence detector in addition to the NMDA receptor generated LTP at positive intervals and LTD only at negative ones. Our findings suggest that a single coincidence detector model based on the standard model of plasticity cannot account for order-specific STDP, and we predict that STDP requires two coincidence detectors.
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Bohte, Sander M., and Michael C. Mozer. "Reducing the Variability of Neural Responses: A Computational Theory of Spike-Timing-Dependent Plasticity." Neural Computation 19, no. 2 (February 2007): 371–403. http://dx.doi.org/10.1162/neco.2007.19.2.371.
Full textAbstract:
Experimental studies have observed synaptic potentiation when a presynaptic neuron fires shortly before a postsynaptic neuron and synaptic depression when the presynaptic neuron fires shortly after. The dependence of synaptic modulation on the precise timing of the two action potentials is known as spike-timing dependent plasticity (STDP). We derive STDP from a simple computational principle: synapses adapt so as to minimize the postsynaptic neuron's response variability to a given presynaptic input, causing the neuron's output to become more reliable in the face of noise. Using an objective function that minimizes response variability and the biophysically realistic spike-response model of Gerstner (2001), we simulate neurophysiological experiments and obtain the characteristic STDP curve along with other phenomena, including the reduction in synaptic plasticity as synaptic efficacy increases. We compare our account to other efforts to derive STDP from computational principles and argue that our account provides the most comprehensive coverage of the phenomena. Thus, reliability of neural response in the face of noise may be a key goal of unsupervised cortical adaptation.
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Shen, Xi, Xiaobin Lin, and Philippe De Wilde. "Oscillations and Spiking Pairs: Behavior of a Neuronal Model with STDP Learning." Neural Computation 20, no. 8 (August 2008): 2037–69. http://dx.doi.org/10.1162/neco.2008.08-06-317.
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In a biologically plausible but computationally simplified integrate-and-fire neuronal population, it is observed that transient synchronized spikes can occur repeatedly. However, groups with different properties exhibit different periods and different patterns of synchrony. We include learning mechanisms in these models. The effects of spike-timing-dependent plasticity have been known to play a distinct role in information processing in the central nervous system for several years. In this letter, neuronal models with dynamical synapses are constructed, and we analyze the effect of STDP on collective network behavior, such as oscillatory activity, weight distribution, and spike timing precision. We comment on how information is encoded by the neuronal signaling, when synchrony groups may appear, and what could contribute to the uncertainty in decision making.
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Zaitsev, Aleksey V., and Roger Anwyl. "Inhibition of the slow afterhyperpolarization restores the classical spike timing-dependent plasticity rule obeyed in layer 2/3 pyramidal cells of the prefrontal cortex." Journal of Neurophysiology 107, no. 1 (January 2012): 205–15. http://dx.doi.org/10.1152/jn.00452.2011.
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The induction of long-term potentiation (LTP) and long-term depression (LTD) of excitatory postsynaptic currents was investigated in proximal synapses of layer 2/3 pyramidal cells of the rat medial prefrontal cortex. The spike timing-dependent plasticity (STDP) induction protocol of negative timing, with postsynaptic leading presynaptic stimulation of action potentials (APs), induced LTD as expected from the classical STDP rule. However, the positive STDP protocol of presynaptic leading postsynaptic stimulation of APs predominantly induced a presynaptically expressed LTD rather than the expected postsynaptically expressed LTP. Thus the induction of plasticity in layer 2/3 pyramidal cells does not obey the classical STDP rule for positive timing. This unusual STDP switched to a classical timing rule if the slow Ca2+-dependent, K+-mediated afterhyperpolarization (sAHP) was inhibited by the selective blocker N-trityl-3-pyridinemethanamine (UCL2077), by the β-adrenergic receptor agonist isoproterenol, or by the cholinergic agonist carbachol. Thus we demonstrate that neuromodulators can affect synaptic plasticity by inhibition of the sAHP. These findings shed light on a fundamental question in the field of memory research regarding how environmental and behavioral stimuli influence LTP, thereby contributing to the modulation of memory.
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Inglebert, Yanis, Johnatan Aljadeff, Nicolas Brunel, and Dominique Debanne. "Synaptic plasticity rules with physiological calcium levels." Proceedings of the National Academy of Sciences 117, no. 52 (December 16, 2020): 33639–48. http://dx.doi.org/10.1073/pnas.2013663117.
Full textAbstract:
Spike-timing–dependent plasticity (STDP) is considered as a primary mechanism underlying formation of new memories during learning. Despite the growing interest in activity-dependent plasticity, it is still unclear whether synaptic plasticity rules inferred from in vitro experiments are correct in physiological conditions. The abnormally high calcium concentration used in in vitro studies of STDP suggests that in vivo plasticity rules may differ significantly from in vitro experiments, especially since STDP depends strongly on calcium for induction. We therefore studied here the influence of extracellular calcium on synaptic plasticity. Using a combination of experimental (patch-clamp recording and Ca2+ imaging at CA3-CA1 synapses) and theoretical approaches, we show here that the classic STDP rule in which pairs of single pre- and postsynaptic action potentials induce synaptic modifications is not valid in the physiological Ca2+ range. Rather, we found that these pairs of single stimuli are unable to induce any synaptic modification in 1.3 and 1.5 mM calcium and lead to depression in 1.8 mM. Plasticity can only be recovered when bursts of postsynaptic spikes are used, or when neurons fire at sufficiently high frequency. In conclusion, the STDP rule is profoundly altered in physiological Ca2+, but specific activity regimes restore a classical STDP profile.
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Câteau, Hideyuki, and Tomoki Fukai. "A Stochastic Method to Predict the Consequence of Arbitrary Forms of Spike-Timing-Dependent Plasticity." Neural Computation 15, no. 3 (March 1, 2003): 597–620. http://dx.doi.org/10.1162/089976603321192095.
Full textAbstract:
Synapses in various neural preparations exhibit spike-timing-dependent plasticity (STDP) with a variety of learning window functions. The window functions determine the magnitude and the polarity of synaptic change according to the time difference of pre- and postsynaptic spikes. Numerical experiments revealed that STDP learning with a single-expo nential window function resulted in a bimodal distribution of synaptic conductances as a consequence of competition between synapses. A slightly modified window function, however, resulted in a unimodal distribution rather than a bimodal distribution. Since various window functions have been observed in neural preparations, we develop a rigorous mathematical method to calculate the conductance distribution for any given window function. Our method is based on the Fokker-Planck equation to determine the conductance distribution and on the Ornstein-Uhlenbeck process to characterize the membrane potential fluctuations. Demonstrating that our method reproduces the known quantitative results of STDP learning, we apply the method to the type of STDP learning found recently in the CA1 region of the rat hippocampus. We find that this learning can result in nearly optimized competition between synapses. Meanwhile, we find that the type of STDP learning found in the cerebellum-like structure of electric fish can result in all-or-none synapses: either all the synaptic conductances are maximized, or none of them becomes significantly large. Our method also determines the window function that optimizes synaptic competition.
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Prokin, Ilya, Ivan Tyukin, and Victor Kazantsev. "Phase Selective Oscillations in Two Noise Driven Synaptically Coupled Spiking Neurons." International Journal of Bifurcation and Chaos 25, no. 07 (June 30, 2015): 1540005. http://dx.doi.org/10.1142/s0218127415400052.
Full textAbstract:
The work investigates the influence of spike-timing dependent plasticity (STDP) mechanisms on the dynamics of two synaptically coupled neurons driven by additive external noise. In this setting, the noise signal models synaptic inputs that the pair receives from other neurons in a larger network. We show that in the absence of STDP feedbacks the pair of neurons exhibit oscillations and intermittent synchronization. When the synapse connecting the neurons is supplied with a phase selective feedback mechanism simulating STDP, induced dynamics of spikes in the coupled system resembles a phase locked mode with time lags between spikes oscillating about a specific value. This value, as we show by extensive numerical simulations, can be set arbitrary within a broad interval by tuning parameters of the STDP feedback.
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Bar Ilan, Lital, Albert Gidon, and Idan Segev. "Interregional synaptic competition in neurons with multiple STDP-inducing signals." Journal of Neurophysiology 105, no. 3 (March 2011): 989–98. http://dx.doi.org/10.1152/jn.00612.2010.
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Neocortical layer 5 (L5) pyramidal cells have at least two spike initiation zones: Na+ spikes are generated near the soma, and Ca2+ spikes at the apical dendritic tuft. These spikes interact with each other and serve as signals for synaptic plasticity. The present computational study explores the implications of having two spike-timing-dependent plasticity (STDP) signals in a neuron, each with its respective regional population of synaptic “pupils.” In a detailed model of an L5 pyramidal neuron, competition emerges between synapses belonging to different regions, on top of the competition among synapses within each region, which characterizes the STDP mechanism. Interregional competition results in strengthening of one group of synapses, which ultimately dominates cell firing, at the expense of weakening synapses in other regions. This novel type of competition is inherent to dendrites with multiple regional signals for Hebbian plasticity. Surprisingly, such interregional competition exists even in a simplified model of two identical coupled compartments. We find that in a model of an L5 pyramidal cell, the different synaptic subpopulations “live in peace” when the induction of Ca2+ spikes requires the back-propagating action potential (BPAP). Thus we suggest a new key role for the BPAP, to maintain the balance between synaptic efficacies throughout the dendritic tree, thereby sustaining the functional integrity of the entire neuron.
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Hosaka, Ryosuke, Osamu Araki, and Tohru Ikeguchi. "STDP Provides the Substrate for Igniting Synfire Chains by Spatiotemporal Input Patterns." Neural Computation 20, no. 2 (February 2008): 415–35. http://dx.doi.org/10.1162/neco.2007.11-05-043.
Full textAbstract:
Spike-timing-dependent synaptic plasticity (STDP), which depends on the temporal difference between pre- and postsynaptic action potentials, is observed in the cortices and hippocampus. Although several theoretical and experimental studies have revealed its fundamental aspects, its functional role remains unclear. To examine how an input spatiotemporal spike pattern is altered by STDP, we observed the output spike patterns of a spiking neural network model with an asymmetrical STDP rule when the input spatiotemporal pattern is repeatedly applied. The spiking neural network comprises excitatory and inhibitory neurons that exhibit local interactions. Numerical experiments show that the spiking neural network generates a single global synchrony whose relative timing depends on the input spatiotemporal pattern and the neural network structure. This result implies that the spiking neural network learns the transformation from spatiotemporal to temporal information. In the literature, the origin of the synfire chain has not been sufficiently focused on. Our results indicate that spiking neural networks with STDP can ignite synfire chains in the cortices.
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Franosch, Jan-Moritz P., Sebastian Urban, and J. Leo van Hemmen. "Supervised Spike-Timing-Dependent Plasticity: A Spatiotemporal Neuronal Learning Rule for Function Approximation and Decisions." Neural Computation 25, no. 12 (December 2013): 3113–30. http://dx.doi.org/10.1162/neco_a_00520.
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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.
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Cameron, K., V. Boonsobhak, A. Murray, and D. Renshaw. "Spike Timing Dependent Plasticity (STDP) can Ameliorate Process Variations in Neuromorphic VLSI." IEEE Transactions on Neural Networks 16, no. 6 (November 2005): 1626–37. http://dx.doi.org/10.1109/tnn.2005.852238.
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Elliott, Terry. "Discrete States of Synaptic Strength in a Stochastic Model of Spike-Timing-Dependent Plasticity." Neural Computation 22, no. 1 (January 2010): 244–72. http://dx.doi.org/10.1162/neco.2009.07-08-814.
Full textAbstract:
A stochastic model of spike-timing-dependent plasticity (STDP) postulates that single synapses presented with a single spike pair exhibit all-or-none quantal jumps in synaptic strength. The amplitudes of the jumps are independent of spiking timing, but their probabilities do depend on spiking timing. By making the amplitudes of both upward and downward transitions equal, synapses then occupy only a discrete set of states of synaptic strength. We explore the impact of a finite, discrete set of strength states on our model, finding three principal results. First, a finite set of strength states limits the capacity of a single synapse to express the standard, exponential STDP curve. We derive the expression for the expected change in synaptic strength in response to a standard, experimental spike pair protocol, finding a deviation from exponential behavior. We fit our prediction to recent data from single dendritic spine heads, finding results that are somewhat better than exponential fits. Second, we show that the fixed-point dynamics of our model regulate the upward and downward transition probabilities so that these are on average equal, leading to a uniform distribution of synaptic strength states. However, third, under long-term potentiation (LTP) and long-term depression (LTD) protocols, these probabilities are unequal, skewing the distribution away from uniformity. If the number of states of strength is at least of order 10, then we find that three effective states of synaptic strength appear, consistent with some experimental data on ternary-strength synapses. On this view, LTP and LTD protocols may therefore be saturating protocols.
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Conde, Virginia, Henning Vollmann, Marco Taubert, Bernhard Sehm, Leonardo G. Cohen, Arno Villringer, and Patrick Ragert. "Reversed timing-dependent associative plasticity in the human brain through interhemispheric interactions." Journal of Neurophysiology 109, no. 9 (May 1, 2013): 2260–71. http://dx.doi.org/10.1152/jn.01004.2012.
Full textAbstract:
Spike timing-dependent plasticity (STDP) has been proposed as one of the key mechanisms underlying learning and memory. Repetitive median nerve stimulation, followed by transcranial magnetic stimulation (TMS) of the contralateral primary motor cortex (M1), defined as paired-associative stimulation (PAS), has been used as an in vivo model of STDP in humans. PAS-induced excitability changes in M1 have been repeatedly shown to be time-dependent in a STDP-like fashion, since synchronous arrival of inputs within M1 induces long-term potentiation-like effects, whereas an asynchronous arrival induces long-term depression (LTD)-like effects. Here, we show that interhemispheric inhibition of the sensorimotor network during PAS, with the peripheral stimulation over the hand ipsilateral to the motor cortex receiving TMS, results in a LTD-like effect, as opposed to the standard STDP-like effect seen for contralateral PAS. Furthermore, we could show that this reversed-associative plasticity critically depends on the timing interval between afferent and cortical stimulation. These results indicate that the outcome of associative stimulation in the human brain depends on functional network interactions (inhibition or facilitation) at a systems level and can either follow standard or reversed STDP-like mechanisms.
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Palmer, John H. C., and Pulin Gong. "Formation and Regulation of Dynamic Patterns in Two-Dimensional Spiking Neural Circuits with Spike-Timing-Dependent Plasticity." Neural Computation 25, no. 11 (November 2013): 2833–57. http://dx.doi.org/10.1162/neco_a_00511.
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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.
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Garrido, Jesús A., Niceto R. Luque, Silvia Tolu, and Egidio D’Angelo. "Oscillation-Driven Spike-Timing Dependent Plasticity Allows Multiple Overlapping Pattern Recognition in Inhibitory Interneuron Networks." International Journal of Neural Systems 26, no. 05 (June 8, 2016): 1650020. http://dx.doi.org/10.1142/s0129065716500209.
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The majority of operations carried out by the brain require learning complex signal patterns for future recognition, retrieval and reuse. Although learning is thought to depend on multiple forms of long-term synaptic plasticity, the way this latter contributes to pattern recognition is still poorly understood. Here, we have used a simple model of afferent excitatory neurons and interneurons with lateral inhibition, reproducing a network topology found in many brain areas from the cerebellum to cortical columns. When endowed with spike-timing dependent plasticity (STDP) at the excitatory input synapses and at the inhibitory interneuron–interneuron synapses, the interneurons rapidly learned complex input patterns. Interestingly, induction of plasticity required that the network be entrained into theta-frequency band oscillations, setting the internal phase-reference required to drive STDP. Inhibitory plasticity effectively distributed multiple patterns among available interneurons, thus allowing the simultaneous detection of multiple overlapping patterns. The addition of plasticity in intrinsic excitability made the system more robust allowing self-adjustment and rescaling in response to a broad range of input patterns. The combination of plasticity in lateral inhibitory connections and homeostatic mechanisms in the inhibitory interneurons optimized mutual information (MI) transfer. The storage of multiple complex patterns in plastic interneuron networks could be critical for the generation of sparse representations of information in excitatory neuron populations falling under their control.
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Hwang, Sungmin, Hyungjin Kim, and Byung-Gook Park. "Quantized Weight Transfer Method Using Spike-Timing-Dependent Plasticity for Hardware Spiking Neural Network." Applied Sciences 11, no. 5 (February 25, 2021): 2059. http://dx.doi.org/10.3390/app11052059.
Full textAbstract:
A hardware-based spiking neural network (SNN) has attracted many researcher’s attention due to its energy-efficiency. When implementing the hardware-based SNN, offline training is most commonly used by which trained weights by a software-based artificial neural network (ANN) are transferred to synaptic devices. However, it is time-consuming to map all the synaptic weights as the scale of the neural network increases. In this paper, we propose a method for quantized weight transfer using spike-timing-dependent plasticity (STDP) for hardware-based SNN. STDP is an online learning algorithm for SNN, but we utilize it as the weight transfer method. Firstly, we train SNN using the Modified National Institute of Standards and Technology (MNIST) dataset and perform weight quantization. Next, the quantized weights are mapped to the synaptic devices using STDP, by which all the synaptic weights connected to a neuron are transferred simultaneously, reducing the number of pulse steps. The performance of the proposed method is confirmed, and it is demonstrated that there is little reduction in the accuracy at more than a certain level of quantization, but the number of pulse steps for weight transfer substantially decreased. In addition, the effect of the device variation is verified.
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Masquelier, Timothée, Rudy Guyonneau, and Simon J. Thorpe. "Competitive STDP-Based Spike Pattern Learning." Neural Computation 21, no. 5 (May 2009): 1259–76. http://dx.doi.org/10.1162/neco.2008.06-08-804.
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Recently it has been shown that a repeating arbitrary spatiotemporal spike pattern hidden in equally dense distracter spike trains can be robustly detected and learned by a single neuron equipped with spike-timing-dependent plasticity (STDP) (Masquelier, Guyonneau, & Thorpe, 2008). To be precise, the neuron becomes selective to successive coincidences of the pattern. Here we extend this scheme to a more realistic scenario with multiple repeating patterns and multiple STDP neurons “listening” to the incoming spike trains. These “listening” neurons are in competition: as soon as one fires, it strongly inhibits the others through lateral connections (one-winner-take-all mechanism). This tends to prevent the neurons from learning the same (parts of the) repeating patterns, as shown in simulations. Instead, the population self-organizes, trying to cover the different patterns or coding one pattern by the successive firings of several neurons, and a powerful distributed coding scheme emerges. Taken together, these results illustrate how the brain could easily encode and decode information in the spike times, a theory referred to as temporal coding, and how STDP could play a key role by detecting repeating patterns and generating selective response to them.
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ZHAO, GANG. "PROBABILITY OF ENTRAINMENT, SYNAPTIC MODIFICATION AND ENTRAINED PHASE ARE PHASE-DEPENDENT IN STDP." Journal of Biological Systems 18, no. 02 (June 2010): 479–93. http://dx.doi.org/10.1142/s0218339010003354.
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Synaptic conductance can be modified in an activity dependent manner, in which the temporal relationship between pre- and post-synaptic spikes plays a major role. This spike timing dependent plasticity (STDP) has profound implications in neural coding, computation and functionality. Because the STDP learning curve is strongly nonlinear, initial state may have great impacts on the eventual state of the system. However, although this feature is intuitively clear, it has not been explored in detail before. This paper presents a preliminary numerical study in this direction. In a model of two pacemaker neurons and a synapse undergoing STDP, it is found that the probability of entrainment, direction of synaptic modification and entrained phase are all influenced by the initial relative phase. Based on these findings, it is reasonable to propose that the initial-state sensitive feature of STDP may contribute to its role of selective response in oscillatory neural networks.
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Fuenzalida, Marco, David Fernández de Sevilla, Alejandro Couve, and Washington Buño. "Role of AMPA and NMDA Receptors and Back-Propagating Action Potentials in Spike Timing–Dependent Plasticity." Journal of Neurophysiology 103, no. 1 (January 2010): 47–54. http://dx.doi.org/10.1152/jn.00416.2009.
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The cellular mechanisms that mediate spike timing–dependent plasticity (STDP) are largely unknown. We studied in vitro in CA1 pyramidal neurons the contribution of AMPA and N-methyl-d-aspartate (NMDA) components of Schaffer collateral (SC) excitatory postsynaptic potentials (EPSPs; EPSPAMPA and EPSPNMDA) and of the back-propagating action potential (BAP) to the long-term potentiation (LTP) induced by a STDP protocol that consisted in pairing an EPSP and a BAP. Transient blockade of EPSPAMPA with 7-nitro-2,3-dioxo-1,4-dihydroquinoxaline-6-carbonitrile (CNQX) during the STDP protocol prevented LTP. Contrastingly LTP was induced under transient inhibition of EPSPAMPA by combining SC stimulation, an imposed EPSPAMPA-like depolarization, and BAP or by coupling the EPSPNMDA evoked under sustained depolarization (approximately −40 mV) and BAP. In Mg2+-free solution EPSPNMDA and BAP also produced LTP. Suppression of EPSPNMDA or BAP always prevented LTP. Thus activation of NMDA receptors and BAPs are needed but not sufficient because AMPA receptor activation is also obligatory for STDP. However, a transient depolarization of another origin that unblocks NMDA receptors and a BAP may also trigger LTP.
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Pedró, Marta, Javier Martín-Martínez, Marcos Maestro-Izquierdo, Rosana Rodríguez, and Montserrat Nafría. "Self-Organizing Neural Networks Based on OxRAM Devices under a Fully Unsupervised Training Scheme." Materials 12, no. 21 (October 24, 2019): 3482. http://dx.doi.org/10.3390/ma12213482.
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A fully-unsupervised learning algorithm for reaching self-organization in neuromorphic architectures is provided in this work. We experimentally demonstrate spike-timing dependent plasticity (STDP) in Oxide-based Resistive Random Access Memory (OxRAM) devices, and propose a set of waveforms in order to induce symmetric conductivity changes. An empirical model is used to describe the observed plasticity. A neuromorphic system based on the tested devices is simulated, where the developed learning algorithm is tested, involving STDP as the local learning rule. The design of the system and learning scheme permits to concatenate multiple neuromorphic layers, where autonomous hierarchical computing can be performed.
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Burkitt, Anthony N., Hamish Meffin, and David B. Grayden. "Spike-Timing-Dependent Plasticity: The Relationship to Rate-Based Learning for Models with Weight Dynamics Determined by a Stable Fixed Point." Neural Computation 16, no. 5 (May 1, 2004): 885–940. http://dx.doi.org/10.1162/089976604773135041.
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Experimental evidence indicates that synaptic modification depends on the timing relationship between the presynaptic inputs and the output spikes that they generate. In this letter, results are presented for models of spike-timing-dependent plasticity (STDP) whose weight dynamics is determined by a stable fixed point. Four classes of STDP are identified on the basis of the time extent of their input-output interactions. The effect on the potentiation of synapses with different rates of input is investigated to elucidate the relationship of STDP with classical studies of long-term potentiation and depression and rate-based Hebbian learning. The selective potentiation of higher-rate synaptic inputs is found only for models where the time extent of the input-output interactions is input restricted (i.e., restricted to time domains delimited by adjacent synaptic inputs) and that have a time-asymmetric learning window with a longer time constant for depression than for potentiation. The analysis provides an account of learning dynamics determined by an input-selective stable fixed point. The effect of suppressive interspike interactions on STDP is also analyzed and shown to modify the synaptic dynamics.
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SAMURA, TOSHIKAZU, and MOTONOBU HATTORI. "HIPPOCAMPAL MEMORY MODIFICATION INDUCED BY PATTERN COMPLETION AND SPIKE-TIMING DEPENDENT SYNAPTIC PLASTICITY." International Journal of Neural Systems 15, no. 01n02 (February 2005): 13–22. http://dx.doi.org/10.1142/s0129065705000025.
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One of the roles of the hippocampus is viewed as modifying episodic memory so that it can contribute to form semantic memory. In this paper, we show that pattern completion ability of the hippocampal CA3 and symmetric spike timing-dependent synaptic plasticity (STDP) induce memory modification so that the hippocampal CA3 can memorize invariable parts of repetitive episodes as essential elements and forget variable parts of them as unnecessary ones.
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Gangarossa, Giuseppe, Sylvie Perez, Yulia Dembitskaya, Ilya Prokin, Hugues Berry, and Laurent Venance. "BDNF Controls Bidirectional Endocannabinoid Plasticity at Corticostriatal Synapses." Cerebral Cortex 30, no. 1 (April 25, 2019): 197–214. http://dx.doi.org/10.1093/cercor/bhz081.
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AbstractThe dorsal striatum exhibits bidirectional corticostriatal synaptic plasticity, NMDAR and endocannabinoids (eCB) mediated, necessary for the encoding of procedural learning. Therefore, characterizing factors controlling corticostriatal plasticity is of crucial importance. Brain-derived neurotrophic factor (BDNF) and its receptor, the tropomyosine receptor kinase-B (TrkB), shape striatal functions, and their dysfunction deeply affects basal ganglia. BDNF/TrkB signaling controls NMDAR plasticity in various brain structures including the striatum. However, despite cross-talk between BDNF and eCBs, the role of BDNF in eCB plasticity remains unknown. Here, we show that BDNF/TrkB signaling promotes eCB-plasticity (LTD and LTP) induced by rate-based (low-frequency stimulation) or spike-timing–based (spike-timing–dependent plasticity, STDP) paradigm in striatum. We show that TrkB activation is required for the expression and the scaling of both eCB-LTD and eCB-LTP. Using 2-photon imaging of dendritic spines combined with patch-clamp recordings, we show that TrkB activation prolongs intracellular calcium transients, thus increasing eCB synthesis and release. We provide a mathematical model for the dynamics of the signaling pathways involved in corticostriatal plasticity. Finally, we show that TrkB activation enlarges the domain of expression of eCB-STDP. Our results reveal a novel role for BDNF/TrkB signaling in governing eCB-plasticity expression in striatum and thus the engram of procedural learning.