Form Of Spike Timing Dependent Plasticity Research Articles

A Hypothetical Model Concerning How Spike-Timing-Dependent Plasticity Contributes to Neural Circuit Formation and Initiation of the Critical Period in Barrel Cortex.

Spike timing is an important factor in the modification of synaptic strength. Various forms of spike timing-dependent plasticity (STDP) occur in the brains of diverse species, from insects to humans. In unimodal STDP, only LTP or LTD occurs at the synapse, regardless of which neuron spikes first; the magnitude of potentiation or depression increases as the time between presynaptic and postsynaptic spikes decreases. This from of STDP may promote developmental strengthening or weakening of early projections. In bidirectional Hebbian STDP, the magnitude and the sign (potentiation or depression) of plasticity depend, respectively, on the timing and the order of presynaptic and postsynaptic spikes. In the rodent barrel cortex, multiple forms of STDP appear sequentially during development, and they contribute to network formation, retraction, or fine-scale functional reorganization. Hebbian STDP appears at L4-L2/3 synapses starting at postnatal day (P) 15; the synapses exhibit unimodal "all-LTP STDP" before that age. The appearance of Hebbian STDP at L4-L2/3 synapses coincides with the maturation of parvalbumin-containing GABA interneurons in L4, which contributes to the generation of L4-before-L2/3 spiking in response to thalamic input by producing fast feedforward suppression of both L4 and L2/3 cells. After P15, L4-L2/3 STDP mediates fine-scale circuit refinement, essential for the critical period in the barrel cortex. In this review, we first briefly describe the relevance of STDP to map plasticity in the barrel cortex, then look over roles of distinct forms of STDP during development. Finally, we propose a hypothesis that explains the transition from network formation to the initiation of the critical period in the barrel cortex.

Interplay of multiple pathways and activity-dependent rules in STDP.

Hebbian plasticity describes a basic mechanism for synaptic plasticity whereby synaptic weights evolve depending on the relative timing of paired activity of the pre- and postsynaptic neurons. Spike-timing-dependent plasticity (STDP) constitutes a central experimental and theoretical synaptic Hebbian learning rule. Various mechanisms, mostly calcium-based, account for the induction and maintenance of STDP. Classically STDP is assumed to gradually emerge in a monotonic way as the number of pairings increases. However, non-monotonic STDP accounting for fast associative learning led us to challenge this monotonicity hypothesis and explore how the existence of multiple plasticity pathways affects the dynamical establishment of plasticity. To account for distinct forms of STDP emerging from increasing numbers of pairings and the variety of signaling pathways involved, we developed a general class of simple mathematical models of plasticity based on calcium transients and accommodating various calcium-based plasticity mechanisms. These mechanisms can either compete or cooperate for the establishment of long-term potentiation (LTP) and depression (LTD), that emerge depending on past calcium activity. Our model reproduces accurately the striatal STDP that involves endocannabinoid and NMDAR signaling pathways. Moreover, we predict how stimulus frequency alters plasticity, and how triplet rules are affected by the number of pairings. We further investigate the general model with an arbitrary number of pathways and show that depending on those pathways and their properties, a variety of plasticities may emerge upon variation of the number and/or the frequency of pairings, even when the outcome after large numbers of pairings is identical. These findings, built upon a biologically realistic example and generalized to other applications, argue that in order to fully describe synaptic plasticity it is not sufficient to record STDP curves at fixed pairing numbers and frequencies. In fact, considering the whole spectrum of activity-dependent parameters could have a great impact on the description of plasticity, and a better understanding of the engram.

Robustness of STDP to spike timing jitter

In Hebbian plasticity, neural circuits adjust their synaptic weights depending on patterned firing. Spike-timing-dependent plasticity (STDP), a synaptic Hebbian learning rule, relies on the order and timing of the paired activities in pre- and postsynaptic neurons. Classically, in ex vivo experiments, STDP is assessed with deterministic (constant) spike timings and time intervals between successive pairings, thus exhibiting a regularity that differs from biological variability. Hence, STDP emergence from noisy inputs as occurring in in vivo-like firing remains unresolved. Here, we used noisy STDP pairings where the spike timing and/or interval between pairings were jittered. We explored with electrophysiology and mathematical modeling, the impact of jitter on three forms of STDP at corticostriatal synapses: NMDAR-LTP, endocannabinoid-LTD and endocannabinoid-LTP. We found that NMDAR-LTP was highly fragile to jitter, whereas endocannabinoid-plasticity appeared more resistant. When the frequency or number of pairings was increased, NMDAR-LTP became more robust and could be expressed despite strong jittering. Our results identify endocannabinoid-plasticity as a robust form of STDP, whereas the sensitivity to jitter of NMDAR-LTP varies with activity frequency. This provides new insights into the mechanisms at play during the different phases of learning and memory and the emergence of Hebbian plasticity in in vivo-like activity.

A model of human motor sequence learning explains facilitation and interference effects based on spike-timing dependent plasticity

The ability to learn sequential behaviors is a fundamental property of our brains. Yet a long stream of studies including recent experiments investigating motor sequence learning in adult human subjects have produced a number of puzzling and seemingly contradictory results. In particular, when subjects have to learn multiple action sequences, learning is sometimes impaired by proactive and retroactive interference effects. In other situations, however, learning is accelerated as reflected in facilitation and transfer effects. At present it is unclear what the underlying neural mechanism are that give rise to these diverse findings. Here we show that a recently developed recurrent neural network model readily reproduces this diverse set of findings. The self-organizing recurrent neural network (SORN) model is a network of recurrently connected threshold units that combines a simplified form of spike-timing dependent plasticity (STDP) with homeostatic plasticity mechanisms ensuring network stability, namely intrinsic plasticity (IP) and synaptic normalization (SN). When trained on sequence learning tasks modeled after recent experiments we find that it reproduces the full range of interference, facilitation, and transfer effects. We show how these effects are rooted in the network’s changing internal representation of the different sequences across learning and how they depend on an interaction of training schedule and task similarity. Furthermore, since learning in the model is based on fundamental neuronal plasticity mechanisms, the model reveals how these plasticity mechanisms are ultimately responsible for the network’s sequence learning abilities. In particular, we find that all three plasticity mechanisms are essential for the network to learn effective internal models of the different training sequences. This ability to form effective internal models is also the basis for the observed interference and facilitation effects. This suggests that STDP, IP, and SN may be the driving forces behind our ability to learn complex action sequences.

Arbitrary Spike Time Dependent Plasticity (STDP) in Memristor by Analog Waveform Engineering

In the literature, various pulse -based programming schemes have been used to mimic typical spike time-dependent plasticity (STDP)-based learning rule observed in biological synapses. In this letter, we demonstrate the capability to generated arbitrary STDP behaviors by using analog programming waveforms inspired by neuronal action potential. First, we propose a simple algorithm to generate any arbitrary form of STDP. Second, we show the feasibility of a range of spike correlation time scales for STDP, e.g., biological (~100 ms) to accelerated ( $\sim 20\mu {s})$ , based on W/Pr0.7Ca0.3MnO3/Pt based memristor. Third, we experimentally demonstrate several forms of STDP behaviors, where the pre- and post-neuronal waveforms are randomly spaced in time, akin to operational conditions. STDP shape corresponds well to waveforms. Thus, we show that artificial synapses can achieve the richness observed in biology as well as a range of STDP timescales for biologically compatible to accelerated neural network applications.

Equilibrium Propagation: Bridging the Gap between Energy-Based Models and Backpropagation.

We introduce Equilibrium Propagation, a learning framework for energy-based models. It involves only one kind of neural computation, performed in both the first phase (when the prediction is made) and the second phase of training (after the target or prediction error is revealed). Although this algorithm computes the gradient of an objective function just like Backpropagation, it does not need a special computation or circuit for the second phase, where errors are implicitly propagated. Equilibrium Propagation shares similarities with Contrastive Hebbian Learning and Contrastive Divergence while solving the theoretical issues of both algorithms: our algorithm computes the gradient of a well-defined objective function. Because the objective function is defined in terms of local perturbations, the second phase of Equilibrium Propagation corresponds to only nudging the prediction (fixed point or stationary distribution) toward a configuration that reduces prediction error. In the case of a recurrent multi-layer supervised network, the output units are slightly nudged toward their target in the second phase, and the perturbation introduced at the output layer propagates backward in the hidden layers. We show that the signal “back-propagated” during this second phase corresponds to the propagation of error derivatives and encodes the gradient of the objective function, when the synaptic update corresponds to a standard form of spike-timing dependent plasticity. This work makes it more plausible that a mechanism similar to Backpropagation could be implemented by brains, since leaky integrator neural computation performs both inference and error back-propagation in our model. The only local difference between the two phases is whether synaptic changes are allowed or not. We also show experimentally that multi-layer recurrently connected networks with 1, 2, and 3 hidden layers can be trained by Equilibrium Propagation on the permutation-invariant MNIST task.

Dopamine Receptors Differentially Control Binge Alcohol Drinking-Mediated Synaptic Plasticity of the Core Nucleus Accumbens Direct and Indirect Pathways.

Binge alcohol drinking, a behavior characterized by rapid repeated alcohol intake, is most prevalent in young adults and is a risk factor for excessive alcohol consumption and alcohol dependence. Although the alteration of synaptic plasticity is thought to contribute to this behavior, there is currently little evidence that this is the case. We used drinking in the dark (DID) as a model of binge alcohol drinking to assess its effects on spike timing-dependent plasticity (STDP) in medium spiny neurons (MSNs) of the core nucleus accumbens (NAc) by combining patch-clamp recordings with calcium imaging and optogenetics. After 2 weeks of daily alcohol binges, synaptic plasticity was profoundly altered. STDP in MSNs expressing dopamine D1 receptors shifted from spike-timing-dependent long-term depression (tLTD), the predominant form of plasticity in naive male mice, to spike-timing-dependent long-term potentiation (tLTP) in DID mice, an effect that was totally reversed in the presence of 4 μm SCH23390, a dopamine D1 receptor antagonist. In MSNs presumably expressing dopamine D2 receptors, tLTP, the main form of plasticity in naive mice, was inhibited in DID mice. Interestingly, 1 μm sulpiride, a D2 receptor antagonist, restored tLTP. Although we observed no alterations of AMPA and NMDA receptor properties, we found that the AMPA/NMDA ratio increased at cortical and amygdaloid inputs but not at hippocampal inputs. Also, DID effects on STDP were accompanied by lower dendritic calcium transients. These data suggest that the role of dopamine in mediating the effects of binge alcohol drinking on synaptic plasticity of NAc MSNs differs markedly whether these neurons belong to the direct or indirect pathways.SIGNIFICANCE STATEMENT We examined the relationship between binge alcohol drinking and spike timing-dependent plasticity in nucleus accumbens (NAc) neurons. We found that repeated drinking bouts modulate differently synaptic plasticity in medium spiny neurons of the accumbens direct and indirect pathways. While timing-dependent long-term depression switches to long-term potentiation (LTP) in the former, timing-dependent LTP is inhibited in the latter. These effects are not accompanied by changes in AMPA and NMDA receptor properties at cortical, amygdaloid, and hippocampal synapses. Interestingly, dopamine D1 and D2 receptor antagonists have opposite effects on plasticity. Our data show that whether core NAc medium spiny neurons belong to the direct or indirect pathways determines the form of spike timing-dependent plasticity (STDP), the manner by which STDP responds to binge alcohol drinking, and its sensitivity to dopamine receptor antagonists.

Hebbian Spike-Timing Dependent Plasticity at the Cerebellar Input Stage.

Spike-timing-dependent plasticity (STDP) is a form of long-term synaptic plasticity exploiting the time relationship between postsynaptic action potentials (APs) and EPSPs. Surprisingly enough, very little was known about STDP in the cerebellum, although it is thought to play a critical role for learning appropriate timing of actions. We speculated that low-frequency oscillations observed in the granular layer may provide a reference for repetitive EPSP/AP phase coupling. Here we show that EPSP-spike pairing at 6 Hz can optimally induce STDP at the mossy fiber-granule cell synapse in rats. Spike timing-dependent long-term potentiation and depression (st-LTP and st-LTD) were confined to a ±25 ms time-window. Because EPSPs led APs in st-LTP while APs led EPSPs in st-LTD, STDP was Hebbian in nature. STDP occurred at 6-10 Hz but vanished >50 Hz or <1 Hz (where only LTP or LTD occurred). STDP disappeared with randomized EPSP/AP pairing or high intracellular Ca2+ buffering, and its sign was inverted by GABA-A receptor activation. Both st-LTP and st-LTD required NMDA receptors, but st-LTP also required reinforcing signals mediated by mGluRs and intracellular calcium stores. Importantly, st-LTP and st-LTD were significantly larger than LTP and LTD obtained by modulating the frequency and duration of mossy fiber bursts, probably because STDP expression involved postsynaptic in addition to presynaptic mechanisms. These results thus show that a Hebbian form of STDP occurs at the cerebellum input stage, providing the substrate for phase-dependent binding of mossy fiber spikes to repetitive theta-frequency cycles of granule cell activity.SIGNIFICANCE STATEMENT Long-term synaptic plasticity is a fundamental property of the brain, causing persistent modifications of neuronal communication thought to provide the cellular basis of learning and memory. The cerebellum is critical for learning the appropriate timing of sensorimotor behaviors, but whether and how appropriate spike patterns could drive long-term synaptic plasticity remained unknown. Here, we show that this can actually occur through a form of spike-timing-dependent plasticity (STDP) at the cerebellar inputs stage. Pairing presynaptic and postsynaptic spikes at 6-10 Hz reliably induced STDP at the mossy fiber-granule cell synapse, with potentiation and depression symmetrically distributed within a ±25 ms time window. Thus, STDP can bind plasticity to the mossy fiber burst phase with high temporal precision.

Concurrently induced plasticity due to convergence of distinct forms of spike timing-dependent plasticity in the developing barrel cortex.

Spike timing-dependent plasticity (STDP) has been demonstrated in a variety of neural circuits. Recent studies reveal that it plays a fundamental role in the formation and remodeling of neuronal circuits. We show here an interaction of two distinct forms of STDP in the mouse barrel cortex causing concurrent, plastic changes, potentially a novel mechanism underlying network remodeling. We previously demonstrated that during the second postnatal week, when layer four (L4) cells are forming synapses onto L2/3 cells, L4-L2/3 synapses exhibit STDP with only long-term potentiation (t-LTP). We also showed that at the same developmental stage, thalamus-L2/3 synapses express functional cannabinoid type 1 receptor (CB1R) and exhibit CB1R-dependent STDP with only long-term depression (t-LTD). Thus, distinct forms of STDP with opposite directions (potentiation vs. depression) converge in the target layer of L2/3 during the second postnatal week. As the canonical target layer of the thalamus is L4 and thalamic cells activate both L4 and L2/3 cells, in principle, thalamic activity could induce t-LTP at L4-L2/3 and t-LTD at thalamus-L2/3 simultaneously. In this study, we tested this possibility. We found that when spike timing stimulation was applied to the thalamus and L2/3 cells, synapses between the thalamus and L2/3 were weakened, whereas synapses between L4 and L2/3 were potentiated; therefore, converging STDP caused the predicted concurrent plasticity. We propose that developmentally transient convergences of STDP may play a role in shaping neural networks by facilitating L4-L2/3 formation and weakening aberrant thalamic innervation to L2/3, both driven by thalamic activity.

Developmental Switch in Spike Timing-Dependent Plasticity and Cannabinoid-Dependent Reorganization of the Thalamocortical Projection in the Barrel Cortex.

The formation and refinement of thalamocortical axons (TCAs) is an activity-dependent process (Katz and Shatz, 1996), but its mechanism and nature of activity are elusive. We studied the role of spike timing-dependent plasticity (STDP) in TCA formation and refinement in mice. At birth (postnatal day 0, P0), TCAs invade the cortical plate, from which layers 4 (L4) and L2/3 differentiate at P3-P4. A portion of TCAs transiently reach toward the pia surface around P2-P4 (Senft and Woolsey, 1991; Rebsam et al., 2002) but are eventually confined below the border between L2/3 and L4. We previously showed that L4-L2/3 synapses exhibit STDP with only potentiation (timing-dependent long-term potentiation [t-LTP]) during synapse formation, then switch to a Hebbian form of STDP. Here we show that TCA-cortical plate synapses exhibit robust t-LTP in neonates, whose magnitude decreased gradually after P4-P5. After L2/3 is differentiated, TCA-L2/3 gradually switched to STDP with only depression (t-LTD) after P7-P8, whereas TCA-L4 lost STDP. t-LTP was dependent on NMDA receptor and PKA, whereas t-LTD was mediated by Type 1 cannabinoid receptors (CB1Rs) probably located at TCA terminals, revealed by global and cortical excitatory cell-specific knock-out of CB1R. Moreover, we found that administration of CB1R agonists, including Δ(9)-tetrahydrocannabinol, caused substantial retraction of TCAs. Consistent with this, individual thalamocortical axons exuberantly innervated L2/3 at P12 in CB1R knock-outs, indicating that endogenous cannabinoid signaling shapes TCA projection. These results suggest that the developmental switch in STDP and associated appearance of CB1R play important roles in the formation and refinement of TCAs. It has been shown that neural activity is required for initial synapse formation of thalamocortical axons with cortical cells, but precisely what sort of activities in presynaptic and postsynaptic cells are required is not yet clear. In addition, how activity is further translated into structural changes is unclear. We show here that the period during which spike timing-dependent long-term potentiation and depression (t-LTP, t-LTD) can be induced closely matches the time course of synapse formation and retraction, respectively, at the thalamocortical synapse. Moreover, administration of cannabinoid agonists, which mimic t-LTD, caused TCA retraction, suggesting that cannabinoids translate physiological changes into morphological consequences.

Learning structure of sensory inputs with synaptic plasticity leads to interference.

Synaptic plasticity is often explored as a form of unsupervised adaptation in cortical microcircuits to learn the structure of complex sensory inputs and thereby improve performance of classification and prediction. The question of whether the specific structure of the input patterns is encoded in the structure of neural networks has been largely neglected. Existing studies that have analyzed input-specific structural adaptation have used simplified, synthetic inputs in contrast to complex and noisy patterns found in real-world sensory data. In this work, input-specific structural changes are analyzed for three empirically derived models of plasticity applied to three temporal sensory classification tasks that include complex, real-world visual and auditory data. Two forms of spike-timing dependent plasticity (STDP) and the Bienenstock-Cooper-Munro (BCM) plasticity rule are used to adapt the recurrent network structure during the training process before performance is tested on the pattern recognition tasks. It is shown that synaptic adaptation is highly sensitive to specific classes of input pattern. However, plasticity does not improve the performance on sensory pattern recognition tasks, partly due to synaptic interference between consecutively presented input samples. The changes in synaptic strength produced by one stimulus are reversed by the presentation of another, thus largely preventing input-specific synaptic changes from being retained in the structure of the network. To solve the problem of interference, we suggest that models of plasticity be extended to restrict neural activity and synaptic modification to a subset of the neural circuit, which is increasingly found to be the case in experimental neuroscience.

Network Self-Organization Explains the Statistics and Dynamics of Synaptic Connection Strengths in Cortex

The information processing abilities of neural circuits arise from their synaptic connection patterns. Understanding the laws governing these connectivity patterns is essential for understanding brain function. The overall distribution of synaptic strengths of local excitatory connections in cortex and hippocampus is long-tailed, exhibiting a small number of synaptic connections of very large efficacy. At the same time, new synaptic connections are constantly being created and individual synaptic connection strengths show substantial fluctuations across time. It remains unclear through what mechanisms these properties of neural circuits arise and how they contribute to learning and memory. In this study we show that fundamental characteristics of excitatory synaptic connections in cortex and hippocampus can be explained as a consequence of self-organization in a recurrent network combining spike-timing-dependent plasticity (STDP), structural plasticity and different forms of homeostatic plasticity. In the network, associative synaptic plasticity in the form of STDP induces a rich-get-richer dynamics among synapses, while homeostatic mechanisms induce competition. Under distinctly different initial conditions, the ensuing self-organization produces long-tailed synaptic strength distributions matching experimental findings. We show that this self-organization can take place with a purely additive STDP mechanism and that multiplicative weight dynamics emerge as a consequence of network interactions. The observed patterns of fluctuation of synaptic strengths, including elimination and generation of synaptic connections and long-term persistence of strong connections, are consistent with the dynamics of dendritic spines found in rat hippocampus. Beyond this, the model predicts an approximately power-law scaling of the lifetimes of newly established synaptic connection strengths during development. Our results suggest that the combined action of multiple forms of neuronal plasticity plays an essential role in the formation and maintenance of cortical circuits.

Promoting endogenous associative plasticity in human primary motor cortex

Promoting endogenous associative plasticity in human primary motor cortex

Interaction of inhibition and triplets of excitatory spikes modulates the NMDA-R-mediated synaptic plasticity in a computational model of spike timing-dependent plasticity

Spike timing-dependent plasticity (STDP) experiments have shown that a synapse is strengthened when a presynaptic spike precedes a postsynaptic one and depressed vice versa. The canonical form of STDP has been shown to have an asymmetric shape with the peak long-term potentiation at +6 ms and the peak long-term depression at -5 ms. Experiments in hippocampal cultures with more complex stimuli such as triplets (one presynaptic spike combined with two postsynaptic spikes or one postsynaptic spike with two presynaptic spikes) have shown that pre-post-pre spike triplets result in no change in synaptic strength, whereas post-pre-post spike triplets lead to significant potentiation. The sign and magnitude of STDP have also been experimentally hypothesized to be modulated by inhibition. Recently, a computational study showed that the asymmetrical form of STDP in the CA1 pyramidal cell dendrite when two spikes interact switches to a symmetrical one in the presence of inhibition under certain conditions. In the present study, I investigate computationally how inhibition modulates STDP in the CA1 pyramidal neuron dendrite when it is driven by triplets. The model uses calcium as the postsynaptic signaling agent for STDP and is shown to be consistent with the experimental triplet observations in the absence of inhibition: simulated pre-post-pre spike triplets result in no change in synaptic strength, whereas simulated post-pre-post spike triplets lead to significant potentiation. When inhibition is bounded by the onset and offset of the triplet stimulation, then the strength of the synapse is decreased as the strength of inhibition increases. When inhibition arrives either few milliseconds before or at the onset of the last spike in the pre-post-pre triplet stimulation, then the synapse is potentiated. Variability in the frequency of inhibition (50 vs. 100 Hz) produces no change in synaptic strength. Finally, a 5% variation in model's calcium parameters (calcium thresholds) proves that the model's performance is robust.

Bursts shape the NMDA-R mediated spike timing dependent plasticity curve: role of burst interspike interval and GABAergic inhibition.

Spike timing dependent plasticity (STDP) is a synaptic learning rule where the relative timing between the presynaptic and postsynaptic action potentials determines the sign and strength of synaptic plasticity. In its basic form STDP has an asymmetric form which incorporates both persistent increases and persistent decreases in synaptic strength. The basic form of STDP, however, is not a fixed property and depends on the dendritic location. An asymmetric curve is observed in the distal dendrites, whereas a symmetrical one is observed in the proximal ones. A recent computational study has shown that the transition from the asymmetry to symmetry is due to inhibition under certain conditions. Synapses have also been observed to be unreliable at generating plasticity when excitatory postsynaptic potentials and single spikes are paired at low frequencies. Bursts of spikes, however, are reliably signaled because transmitter release is facilitated. This article presents a two-compartment model of the CA1 pyramidal cell. The model is neurophysiologically plausible with its dynamics resulting from the interplay of many ionic and synaptic currents. Plasticity is measured by a deterministic Ca(2+) dynamics model which measures the instantaneous calcium level and its time course in the dendrite and change the strength of the synapse accordingly. The model is validated to match the asymmetrical form of STDP from the pairing of a presynaptic (dendritic) and postsynaptic (somatic) spikes as observed experimentally. With the parameter set unchanged the model investigates how pairing of bursts with single spikes and bursts in the presence or absence of inhibition shapes the STDP curve. The model predicts that inhibition strength and frequency are not the only factors of the asymmetry-to-symmetry switch of the STDP curve. Burst interspike interval is another factor. This study is an important first step towards understanding how STDP is affected under natural firing patterns in vivo.

Reconciling the STDP and BCM Models of Synaptic Plasticity in a Spiking Recurrent Neural Network

Rate-coded Hebbian learning, as characterized by the BCM formulation, is an established computational model of synaptic plasticity. Recently it has been demonstrated that changes in the strength of synapses in vivo can also depend explicitly on the relative timing of pre- and postsynaptic firing. Computational modeling of this spike-timing-dependent plasticity (STDP) has demonstrated that it can provide inherent stability or competition based on local synaptic variables. However, it has also been demonstrated that these properties rely on synaptic weights being either depressed or unchanged by an increase in mean stochastic firing rates, which directly contradicts empirical data. Several analytical studies have addressed this apparent dichotomy and identified conditions under which distinct and disparate STDP rules can be reconciled with rate-coded Hebbian learning. The aim of this research is to verify, unify, and expand on these previous findings by manipulating each element of a standard computational STDP model in turn. This allows us to identify the conditions under which this plasticity rule can replicate experimental data obtained using both rate and temporal stimulation protocols in a spiking recurrent neural network. Our results describe how the relative scale of mean synaptic weights and their dependence on stochastic pre- or postsynaptic firing rates can be manipulated by adjusting the exact profile of the asymmetric learning window and temporal restrictions on spike pair interactions respectively. These findings imply that previously disparate models of rate-coded autoassociative learning and temporally coded heteroassociative learning, mediated by symmetric and asymmetric connections respectively, can be implemented in a single network using a single plasticity rule. However, we also demonstrate that forms of STDP that can be reconciled with rate-coded Hebbian learning do not generate inherent synaptic competition, and thus some additional mechanism is required to guarantee long-term input-output selectivity.

Generation of short-term memory for items and order by unsupervised learning

Event Abstract Back to Event Generation of short-term memory for items and order by unsupervised learning In a multiple-item sequential memory task, animals can remember the identity of successive items and their order of appearance [1]. Such ability puts demands on standard models of short-term memory arising from persistent activity of neurons, which typically encode either the first stimulus or the most recent stimulus. In this presentation we show how repeated presentations of stimulus sequences to a network, with an initially limited, transient response to stimuli, can generate the necessary connectivity pattern to solve a two-item sequential memory task. We find that essential plasticity mechanisms within the network are: 1) Hebbian plasticity, which we implement through a well-characterized triplet form of spike-timing dependent plasticity (STDP) [2]. STDP in a sparse network generates an increase in recurrent excitation that is essential to produce persistent activity and memory. 2) Long-term potentiation of inhibition [3] (LTPi), which increases inhibition from active interneurons to inactive pyramidal cells. LTPi produces the cross-inhibition necessary to prevent memory activity from spreading, thus maintaining the specificity of memory. 3) Homeostasis by multiplicative scaling of synaptic strengths [4] prevents a single attractor state from dominating the network, allowing for multiple, approximately equally visited states to be maintained in the network. We produce a simplified, intuitive example of sequential memory as a bump of activity, which requires local excitation to persist following a transient stimulus and which moves toward neurons tuned to subsequent stimuli, provided some longer-range connections exist. Thus we suggest that such a small-world architecture in the pattern of connections between neurons is essential to generate multiple-item sequential memory.

Roles of phospholipase Cβ and NMDA receptor in activity‐dependent endocannabinoid release

Endocannabinoids are released from postsynaptic neurons, activate presynaptic cannabinoid receptors and cause various forms of short-term and long-term synaptic plasticity throughout the brain. Using hippocampal and cerebellar neurons, we have revealed that endocannabinoid release can be induced through two different pathways. One is independent of phospholipase Cbeta (PLCbeta) and driven by Ca(2+) elevation alone (Ca(2+)-driven endocannabinoid release, CaER), and the other is PLCbeta-dependent and driven by activation of G(q/11)-coupled receptors (receptor-driven endocannabinoid release, RER). CaER is induced by activation of either voltage-gated Ca(2+) channels or NMDA receptors. RER is functional even at resting Ca(2+) levels (basal RER), but markedly enhanced by a small Ca(2+) elevation (Ca(2+)-assisted RER). In Ca(2+)-assisted RER, PLCbeta serves as a coincidence detector of receptor activation and Ca(2+) elevation. We have also demonstrated that Ca(2+)-assisted RER is essential for the endocannabinoid release triggered by synaptic activity. Our anatomical data show that a set of receptors and enzymes required for RER are well organized so that the excitatory input can trigger RER effectively. Certain forms of spike-timing-dependent plasticity (STDP) are reported to depend on endocannabinoid signalling. The NMDA receptor and PLCbeta might play key roles in the endocannabinoid-dependent forms of STDP as coincidence detectors with different timing dependences.

Hebbian STDP in mushroom bodies facilitates the synchronous flow of olfactory information in locusts

Odour representations in insects undergo progressive transformations and decorrelation from the receptor array to the presumed site of odour learning, the mushroom body. There, odours are represented by sparse assemblies of Kenyon cells in a large population. Using intracellular recordings in vivo, we examined transmission and plasticity at the synapse made by Kenyon cells onto downstream targets in locusts. We find that these individual synapses are excitatory and undergo hebbian spike-timing dependent plasticity (STDP) on a +/-25 ms timescale. When placed in the context of odour-evoked Kenyon cell activity (a 20-Hz oscillatory population discharge), this form of STDP enhances the synchronization of the Kenyon cells' targets and thus helps preserve the propagation of the odour-specific codes through the olfactory system.

Synaptic equalization by anti-STDP

Experimental evidence has shown that in some neuron types, such as hippocampal CA1 neurons, distally generated excitatory postsynaptic potentials (EPSPs) are scaled up in amplitude compared to more proximally generated EPSPs such that EPSP magnitudes at the soma are approximately equal regardless of the dendritic location of origin. Using an equivalent cable model neuron, we show that a particular form of spike-timing dependent plasticity (STDP) called anti-STDP is capable of achieving this synaptic equalization. Anti-STDP equalizes synapses through the equalization of synaptic efficacies. We show this result for both a passive dendritic cable and for a cable containing a dendritic spike-initiation zone.