Spike-timing Dependent Synaptic Plasticity Research Articles

IP3 mobilization and diffusion determine the timing window of Ca2+ release by synaptic stimulation and a spike in rat CA1 pyramidal cells

Synaptically activated calcium release from internal stores in CA1 pyramidal neurons is generated via metabotropic glutamate receptors by mobilizing IP(3). Ca(2+) release spreads as a large amplitude wave in a restricted region of the apical dendrites of these cells. These Ca(2+) waves have been shown to induce certain forms of synaptic potentiation and have been hypothesized to affect other forms of plasticity. Pairing a single backpropagating action potential (bAP) with repetitive synaptic stimulation evokes Ca(2+) release when synaptic stimulation alone is subthreshold for generating release. We examined the timing window for this synergistic effect under conditions favoring Ca(2+) release. The window, measured from the end of the train, lasted 250-500 ms depending on the duration of stimulation tetanus. The window appears to correspond to the time when both IP(3) concentration and [Ca(2+)](i) are elevated at the site of the IP(3) receptor. Detailed analysis of the mechanisms determining the duration of the window, including experiments using different forms of caged IP(3) instead of synaptic stimulation, suggest that the most significant processes are the time for IP(3) to diffuse away from the site of generation and the time course of IP(3) production initiated by activation of mGluRs. IP(3) breakdown, desensitization of the IP(3) receptor, and the kinetics of IP(3) unbinding from the receptor may affect the duration of the window but are less significant. The timing window is short but does not appear to be short enough to suggest that this form of coincidence detection contributes to conventional spike timing-dependent synaptic plasticity in these cells.

LTD windows of the STDP learning rule and synaptic connections having a large transmission delay enable robust sequence learning amid background noise

Spike-timing-dependent synaptic plasticity (STDP) is a simple and effective learning rule for sequence learning. However, synapses being subject to STDP rules are readily influenced in noisy circumstances because synaptic conductances are modified by pre- and postsynaptic spikes elicited within a few tens of milliseconds, regardless of whether those spikes convey information or not. Noisy firing existing everywhere in the brain may induce irrelevant enhancement of synaptic connections through STDP rules and would result in uncertain memory encoding and obscure memory patterns. We will here show that the LTD windows of the STDP rules enable robust sequence learning amid background noise in cooperation with a large signal transmission delay between neurons and a theta rhythm, using a network model of the entorhinal cortex layer II with entorhinal-hippocampal loop connections. The important element of the present model for robust sequence learning amid background noise is the symmetric STDP rule having LTD windows on both sides of the LTP window, in addition to the loop connections having a large signal transmission delay and the theta rhythm pacing activities of stellate cells. Above all, the LTD window in the range of positive spike-timing is important to prevent influences of noise with the progress of sequence learning.

A CMOS Spiking Neural Network Circuit with Symmetric/Asymmetric STDP Function

In this paper, we propose an analog CMOS circuit which achieves spiking neural networks with spike-timing dependent synaptic plasticity (STDP). In particular, we propose a STDP circuit with symmetric function for the first time, and also we demonstrate associative memory operation in a Hopfield-type feedback network with STDP learning. In our spiking neuron model, analog information expressing processing results is given by the relative timing of spike firing events. It is well known that a biological neuron changes its synaptic weights by STDP, which provides learning rules depending on relative timing between asynchronous spikes. Therefore, STDP can be used for spiking neural systems with learning function. The measurement results of fabricated chips using TSMC 0.25µm CMOS process technology demonstrate that our spiking neuron circuit can construct feedback networks and update synaptic weights based on relative timing between asynchronous spikes by a symmetric or an asymmetric STDP circuits.

Multiple topological representation self-organized by spike-timing-dependent synaptic learning rule

Position-and-scale-free representations of shapes are acquired by neurons in the inferior temporal (IT) cortex. So each neuron receives information from the whole visual field. Familiar shapes are extremely restricted from all the possible shapes on the whole visual field. So they must be clustered in the shape space to have mixed structure of continuity and discreteness. We demonstrate that multiple representation can be acquired in a spike-based model for topological maps based on the spike-timing-dependent synaptic plasticity (STDP), subjected to a set of inputs on multiple rings, which is a simple example of mixed structure. In this representation, the position on each ring is represented by a center of active neurons and the difference of rings is represented by a detailed pattern of active neurons. Neurons in the same region exhibit high activities for an input on the other ring. The result is consistent with the fact observed in IT cortex that neighboring neurons exhibit different preferences while the region of active neurons is continuously shifted for continuous changes of object.

A neural circuit model of emotional learning using two pathways with different granularity and speed of information processing

We propose a neural circuit model of emotional learning using two pathways with different granularity and speed of information processing. In order to derive a precise time process, we utilized a spiking model neuron proposed by Izhikevich and spike-timing-dependent synaptic plasticity (STDP) of both excitatory and inhibitory synapses. We conducted computer simulations to evaluate the proposed model. We demonstrate some aspects of emotional learning from the perspective of the time process. The agreement of the results with the previous behavioral experiments suggests that the structure and learning process of the proposed model are appropriate.

EMERGENCE OF PREFERRED FIRING SEQUENCES IN LARGE SPIKING NEURAL NETWORKS DURING SIMULATED NEURONAL DEVELOPMENT

Two main processes concurrently refine the nervous system over the course of development: cell death and selective synaptic pruning. We simulated large spiking neural networks (100 x 100 neurons "at birth") characterized by an early developmental phase with cell death due to excessive firing rate, followed by the onset of spike timing dependent synaptic plasticity (STDP), driven by spatiotemporal patterns of stimulation. The cell death affected the inhibitory units more than the excitatory units during the early developmental phase. The network activity showed the appearance of recurrent spatiotemporal firing patterns along the STDP phase, thus suggesting the emergence of cell assemblies from the initially randomly connected networks. Some of these patterns were detected throughout the simulation despite the activity-driven network modifications while others disappeared.

Synaptic symmetry breaking by spike timing dependent synaptic plasticity

Recent experimental studies have shown that in various brain regions, such as the hippocampus and the neocortex, both the sign and the magnitude of synaptic modification depend on the precise temporal relation of spike timing of two neurons, which is called spike timing dependent synaptic plasticity (STDP). Due to the asymmetry of the critical window of STDP, STDP tends to lead morphologically reciprocal synaptic connections between two neurons into a functionally uni-directional synaptic connection. We study the spontaneous symmetry breaking of bi-directional synaptic connections between two neurons, which leads the synaptic competition in a larger network. We show that STDP is optimized to the symmetry breaking for the experimentally observed time scale range of the critical window. from Seventeenth Annual Computational Neuroscience Meeting: CNS*2008 Portland, OR, USA. 19–24 July 2008

Induction mechanisms and modulation of bidirectional burst stimulation-induced synaptic plasticity in the hippocampus

Spike bursting is an important physiological mode of the hippocampus. Whereas the rules of spike timing-dependent synaptic plasticity are well defined for pairs of single action potentials (APs) and excitatory postsynaptic potentials (EPSPs), long-term modification of synaptic responses is much less understood for more complex pre- and postsynaptic spike patterns. We induced a burst stimulation (BS)-associated form of synaptic plasticity in rat CA1 hippocampal slices by repeatedly pairing three EPSPs with a burst of APs induced by postsynaptic current injection. In distinct groups of cells, this induction paradigm resulted in long-term potentiation (LTP), long-term depression (LTD) or no change in synaptic strength. LTP was N-methyl-d-aspartate receptor-dependent, whereas LTD could be blocked by a metabotropic glutamate receptor antagonist or inhibition of Ca(2+) influx through voltage-activated Ca(2+) channels. LTP was predicted by a more depolarized membrane potential and a higher initial AP frequency. LTD was facilitated by a larger time interval between the last EPSP and its preceding AP. We conclude from these findings that associative BS induces a bidirectional form of long-term synaptic plasticity that cannot be fully explained by spike timing rules. Postsynaptic membrane potential and Ca(2+) influx further influence the sign and magnitude of synaptic modification. LTP and LTD have distinct mechanisms and can be selectively modulated. This supports the concept of two independent coincidence detectors for LTP and LTD, and extends the physiological options to modulate synaptic plasticity and maintain a putative balance between potentiation and depression in synaptic networks.

Requirement of an Allosteric Kinetics of NMDA Receptors for Spike Timing-Dependent Plasticity

Spike timing-dependent synaptic plasticity (STDP) plays an important role in neural development and information processing in the brain; however, the mechanism by which spike timing information is encoded into STDP remains unclear. Here, we show that a novel allosteric kinetics of NMDA receptors (NMDARs) is required for STDP. We developed a detailed biophysical model of STDP and found that the model required spike timing-dependent distinct suppression of NMDARs by Ca(2+)-calmodulin. This led us to predict an allosteric kinetics of NMDARs: a slow and rapid suppression of NMDARs by Ca(2+)-calmodulin with prespiking --> postspiking and postspiking --> prespiking, respectively. We found that the allosteric kinetics, but not the conventional kinetics, is consistent with specific features of amplitudes and peak time of NMDAR-mediated EPSPs in experiments. We found that the allosteric kinetics of NMDARs was also valid for synaptic plasticity induced by more complex spike trains in layer II/III of visual cortex. We extracted an essential synaptic learning rule by reduction of the allosteric STDP model and found that spike timing-dependent bidirectional role of postspiking in synaptic modification, which depends on the allosteric kinetics, is the essential principle in STDP. Thus, we propose a simple hypothesis of the allosteric kinetics of NMDARs that can coherently explain critical features of spike timing-dependent NMDAR-mediated EPSPs and synaptic plasticity.

STDP Provides the Substrate for Igniting Synfire Chains by Spatiotemporal Input Patterns

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.

Systems biology of Spike-Timing Dependent synaptic Plasticity

Systems biology of Spike-Timing Dependent synaptic Plasticity

Reinforcement Learning With Modulated Spike Timing–Dependent Synaptic Plasticity

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.

HIERARCHICAL MODULARITY OF THE FUNCTIONAL NEURAL NETWORK ORGANIZED BY SPIKE TIMING DEPENDENT SYNAPTIC PLASTICITY

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.

DOES SPIKE TIMING-DEPENDENT SYNAPTIC PLASTICITY UNDERLIE MEMORY FORMATION?

1. Synaptic plasticity is thought to underlie learning and memory formation in the brain. However, how synaptic plasticity is induced during these processes remains controversial. An attractive candidate mechanism for learning at the neuronal level is spike timing-dependent synaptic plasticity (STDP), which depends on the precise (msec) timing of the synaptic input and the post-synaptic action potential. This temporal relationship resembles typical features of associative learning. Here, we review recent evidence suggesting that STDP is likely to underlie certain forms of learning. 2. First, we discuss the cellular mechanisms of STDP elucidated by in vitro experiments. A special focus is put onto aspects known to differ between in vitro preparations and the in vivo situation. 3. Second, we review the experimental induction of STDP in vivo, in various systems ranging from Xenopus tectum to human motor cortex. 4. The last part of the review addresses the question whether STDP can be induced by activity patterns occurring during normal behaviour. 5. We conclude that STDP is a robust phenomenon in vivo and a likely mechanism underlying sensory map plasticity in the neocortex. Further experimental evidence is required to determine whether STDP also has a role in more complex forms of learning.

Development of Neural Circuitry for Precise Temporal Sequences through Spontaneous Activity, Axon Remodeling, and Synaptic Plasticity

Temporally precise sequences of neuronal spikes that span hundreds of milliseconds are observed in many brain areas, including songbird premotor nucleus, cat visual cortex, and primary motor cortex. Synfire chains—networks in which groups of neurons are connected via excitatory synapses into a unidirectional chain—are thought to underlie the generation of such sequences. It is unknown, however, how synfire chains can form in local neural circuits, especially for long chains. Here, we show through computer simulation that long synfire chains can develop through spike-time dependent synaptic plasticity and axon remodeling—the pruning of prolific weak connections that follows the emergence of a finite number of strong connections. The formation process begins with a random network. A subset of neurons, called training neurons, intermittently receive superthreshold external input. Gradually, a synfire chain emerges through a recruiting process, in which neurons within the network connect to the tail of the chain started by the training neurons. The model is robust to varying parameters, as well as natural events like neuronal turnover and massive lesions. Our model suggests that long synfire chain can form during the development through self-organization, and axon remodeling, ubiquitous in developing neural circuits, is essential in the process.

Emergent functional neural networks organized by spike timing dependent synaptic plasticity

The synchronization of neural activities plays very important roles in the information processing in the brain. Recent studies on complex systems have shown that the synchronization of oscillators, including neuronal ones, is faster, stronger, and more efficient in small-world networks than in regular or random networks, and many studies are based on the assumption that the brain may utilize the small-world and scale-free network structure. The collective dynamical response and the functional neural network structure depend on each other due to synaptic plasticities, and this feedback process is believed to be closely linked to the mechanisms for learning and memory in the brain. Recent experimental studies have shown that in various brain regions, such as the hippocampus and the neocortex, both the sign and the magnitude of synaptic modification depend on the precise temporal relation of spike timing of two neurons, which is called the spike timing dependent synaptic plasticity (STDP). Here, we study the emergent functional neural networks organized by STDP. We show that STDP can lead a neural oscillator network into a functional structure which has both the small-world behaviors and the scale-free properties with hierarchical modularity. The STDP network has small average shortest path length between the neurons and high clustering coefficient. The degree distributions and the clustering coefficient depending on the degree follow power-low decays. We also show that the balance between the maximal excitatory and the inhibitory synaptic inputs is critical in the formation of the nontrivial functional structure, which is found to lie in a self-organized critical state.

Coincident excitatory and inhibitory spike-timing dependent plasticity potentiates pyramidal neurons

Spike-timing dependent plasticity (STDP) has been demonstrated separately at both glutamatergic and GABAergic synapses, however the result of both of these synapses undergoing STDP simultaneously has not been examined. Here we investigate how simultaneous STDP of excitatory and inhibitory synapses onto CA1 pyramidal cells alters their probability and timing of spike generation, thus regulating the output of the hippocampus. Using a multi-compartment model of a CA1 pyramidal neuron with excitatory and inhibitory synapses modeled onto the proximal dendrites, we demonstrate that when these synapses are modified by positive coincident (+10 ms) spike-timing dependent rules there is an increase in both the probability of generating an action potential and a decrease in the latency from synaptic input to spiking. Modifying both excitatory and inhibitory synapses with a negative coincident (-10 ms) spike-timing rules decreases pyramidal cell spiking less than if excitatory synapses were modified alone. When excitatory and inhibitory synapses undergo positive coincident spike-timing dependent synaptic plasticity, in the presence of a theta rhythm, there is a decrease in the interval between synaptic input and spiking which advances the spike forward on the theta cycle. Thus simultaneous modification of excitatory and inhibitory synapses alters the probability of spike generation and the precision of spike-timing within the hippocampus.

A synaptic circuit of a pulse-type hardware neuron model with STDP

A number of recent studies of neural networks have been conducted with the purpose of applying engineering to the brain. We have been attempting to construct artificial neural networks. In the present study, we focus on STDP (spike timing dependent synaptic plasticity) and construct a neural network from a pulse-type hardware neuron model (P-HNM) with STDP. In addition, we propose a simple synaptic circuit of the P-HNM with STDP, which is composed of MOSFETs, and construct a simple neural network in order to verify the robustness with respect to phase fluctuation. We show that a pulse-type hardware neural network with STDP synapses has a learning function that has robustness with respect to phase fluctuation.

A CMOS circuit for STDP with a symmetric time window

In some spiking neuron models, analog information is expressed by the timing of neuronal spike firing events, and synaptic weights change depending on the relative timing between asynchronous spikes, which is called spike-timing dependent synaptic plasticity (STDP). In this paper, we propose an analog CMOS circuit for STDP with a symmetric time window and a Hopfield-type VLSI neural network using the synapse circuit. We have confirmed by circuit simulations that the network can perform autocorrelation learning from input spike patterns.

Spatiotemporal learning in analog neural networks using spike-timing-dependent synaptic plasticity

Incorporating the spike-timing-dependent synaptic plasticity (STDP) into a learning rule, we study spatiotemporal learning in analog neural networks. First, we study learning of a finite number of periodic spatiotemporal patterns by deriving the dynamics of the order parameters. When a pattern is retrieved successfully, the order parameters exhibit periodic oscillation. Analyzing this oscillation of the order parameters, we elucidate the relation of the STDP time window to the properties of the retrieval state; the phase of the Fourier transform of the STDP time window determines the retrieval frequency and the time average of the STDP time window crucially affects the storage capacity. We also evaluate the stability of the order parameter oscillation and identify the retrieval state that is stable in single-pattern learning but unstable in multiple-pattern learning even when the retrieval state is independent of a pattern number. To examine the further applicability of the STDP-based learning rule, we also study learning of nonperiodic spatiotemporal Poisson patterns. Our numerical simulations demonstrate that the Poisson patterns are memorized successfully not only in analog neural networks but also in spiking neural networks.