Hebbian Spike Timing-dependent Plasticity Research Articles

Spike-timing-dependent plasticity induction reveals dissociable supplementary– and premotor–motor pathways to automatic imitation

Humans tend to spontaneously imitate others' behavior, even when detrimental to the task at hand. The action observation network (AON) is consistently recruited during imitative tasks. However, whether automatic imitation is mediated by cortico-cortical projections from AON regions to the primary motor cortex (M1) remains speculative. Similarly, the potentially dissociable role of AON-to-M1 pathways involving the ventral premotor cortex (PMv) or supplementary motor area (SMA) in automatic imitation is unclear. Here, we used cortico-cortical paired associative stimulation (ccPAS) to enhance or hinder effective connectivity in PMv-to-M1 and SMA-to-M1 pathways via Hebbian spike-timing-dependent plasticity (STDP) to test their functional relevance to automatic and voluntary motor imitation. ccPAS affected behavior under competition between task rules and prepotent visuomotor associations underpinning automatic imitation. Critically, we found dissociable effects of manipulating the strength of the two pathways. While strengthening PMv-to-M1 projections enhanced automatic imitation, weakening them hindered it. On the other hand, strengthening SMA-to-M1 projections reduced automatic imitation but also reduced interference from task-irrelevant cues during voluntary imitation. Our study demonstrates that driving Hebbian STDP in AON-to-M1 projections induces opposite effects on automatic imitation that depend on the targeted pathway. Our results provide direct causal evidence of the functional role of PMv-to-M1 projections for automatic imitation, seemingly involved in spontaneously mirroring observed actions and facilitating the tendency to imitate them. Moreover, our findings support the notion that SMA exerts an opposite gating function, controlling M1 to prevent overt motor behavior when inadequate to the context.

Effects of Mg Doping to a LiCoO2 Channel on the Synaptic Plasticity of Li Ion-Gated Transistors.

Artificial synapses with ideal functionalities are essential in hardware neural networks to allow for energy-efficient analog computing. However, the realization of linear and symmetric weight updates in real synaptic devices has proven challenging and ultimately limits the online training capabilities of neural network systems. Herein, we investigate the effect of Mg doping on a LiCoO2 (LCO) channel in a Li ion-gated synaptic transistor, so as to improve long-term and short-term plasticity. Two transistor structures, based on a lithium phosphorus oxynitride electrolyte, were examined by using undoped LCO and Mg-doped LCO as the channel material between the source and drain electrodes. It was found that Mg doping increased the initial channel conductance by 3 orders of magnitude, which is probably due to the substitution of Co3+ by Mg2+ and the compensation of hole creation. It was further found that the doped channel transistor showed good retention characteristics and better linearity of long-term potentiation and depression when voltage pulses were applied to the gate electrode. The improved retention and linearity are attributed to an extended range of the insulator-to-conductor transition by Mg doping and Li-ion extraction/insertion cooperated in the LCO channel. Using the obtained synaptic weight update, artificial neural network simulations demonstrated that the doped channel transistor shows an image recognition accuracy of ∼80% for handwritten digits, which is higher than ∼65% exhibited by the undoped channel transistor. Mg doping also improved short-term plasticity such as paired-pulse facilitation/depression and Hebbian spike timing-dependent plasticity. These results indicate that elemental doping to the channel of Li ion-gated synaptic transistors could be a useful procedure for realizing robust neuromorphic systems based on analog computing.

Spike timing-dependent plasticity alters electrosensory neuron synaptic strength in vitro but does not consistently predict changes in sensory tuning in vivo.

How do sensory systems optimize detection of behaviorally relevant stimuli when the sensory environment is constantly changing? We addressed the role of spike timing-dependent plasticity (STDP) in driving changes in synaptic strength in a sensory pathway and whether those changes in synaptic strength could alter sensory tuning. It is challenging to precisely control temporal patterns of synaptic activity in vivo and replicate those patterns in vitro in behaviorally relevant ways. This makes it difficult to make connections between STDP-induced changes in synaptic physiology and plasticity in sensory systems. Using the mormyrid species Brevimyrus niger and Brienomyrus brachyistius, which produce electric organ discharges for electrolocation and communication, we can precisely control the timing of synaptic input in vivo and replicate these same temporal patterns of synaptic input in vitro. In central electrosensory neurons in the electric communication pathway, using whole cell intracellular recordings in vitro, we paired presynaptic input with postsynaptic spiking at different delays. Using whole cell intracellular recordings in awake, behaving fish, we paired sensory stimulation with postsynaptic spiking using the same delays. We found that Hebbian STDP predictably alters sensory tuning in vitro and is mediated by NMDA receptors. However, the change in synaptic responses induced by sensory stimulation in vivo did not adhere to the direction predicted by the STDP observed in vitro. Further analysis suggests that this difference is influenced by polysynaptic activity, including inhibitory interneurons. Our findings suggest that STDP rules operating at identified synapses may not drive predictable changes in sensory responses at the circuit level.NEW & NOTEWORTHY We replicated behaviorally relevant temporal patterns of synaptic activity in vitro and used the same patterns during sensory stimulation in vivo. There was a Hebbian spike timing-dependent plasticity (STDP) pattern in vitro, but sensory responses in vivo did not shift according to STDP predictions. Analysis suggests that this disparity is influenced by differences in polysynaptic activity, including inhibitory interneurons. These results suggest that STDP rules at synapses in vitro do not necessarily apply to circuits in vivo.

Implementation of an Advanced Frequency-Based Hebbian Spike Timing Dependent Plasticity.

The brain is provided with an enormous computing capability and exploits neural plasticity to store and elaborate complex information. One of the multiple mechanisms that neural circuits express is the Spike-timing-dependent plasticity (STDP), a form of long-term synaptic plasticity exploiting the time relationship between pre- and post-synaptic action potentials (i.e., neuron spikes). It has been found that in certain cases, for instance at the input stage of the cerebellum, between mossy fibers and granular neurons, the plasticity is not only driven by the timing of the spikes, but also by the oscillation frequency of the inputs. This complex behaviour has been implemented in this work, where we developed a novel form of advanced synaptic plasticity model to be used in a well-established neural network simulator (NEST). The subsequent tests proved the proper functioning of the plasticity and its range of applicability, demonstrating the possibility to adopt it in noisy and variable conditions, similar to the biological settings.

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.

Restickable Oxide Neuromorphic Transistors with Spike‐Timing‐Dependent Plasticity and Pavlovian Associative Learning Activities

AbstractIn neural system, both spike‐timing‐dependent plasticity (STDP) and conditioned reflex are vital synaptic learning mechanisms to regulate advanced neural activities, being associated with temporally coupled stimuli. Thus, realization of STDP and conditioned reflex on a single solid‐state device may open up new opportunities for neuromorphic engineering. Here, restickable chitosan‐gated oxide neuromorphic transistors are fabricated on polyimide tape. Based on protonic electrochemical doping/de‐doping processes at indium‐tin‐oxide/chitosan interfaces, four types of STDP learning rules are successfully demonstrated, including Hebbian STDP, anti‐Hebbian STDP, symmetrical STDP, and visual STDP. Trained with Hebbian STDP, Pavlovian associative learning and extinction behaviors are demonstrated successfully on a single‐oxide neuromorphic transistor. No complex device circuits are needed. It is interesting to note here that the devices can be pasted on different holders with different curvature radii without degrading the transistor performances and STDP learning rules. Moreover, the proposed oxide neuromorphic transistors can be dissolved in deionized water easily. The results here indicate potential applications of the proposed restickable oxide neuromorphic transistors in flexible neuromorphic cognitive platforms.

Endocannabinoid-LTP Mediated by CB1 and TRPV1 Receptors Encodes for Limited Occurrences of Coincident Activity in Neocortex.

Synaptic efficacy changes, long-term potentiation (LTP) and depression (LTD), underlie various forms of learning and memory. Synaptic plasticity is generally assessed under prolonged activation, whereas learning can emerge from few or even a single trial. Here, we investigated the existence of rapid responsiveness of synaptic plasticity in response to a few number of spikes, in neocortex in a synaptic Hebbian learning rule, the spike-timing-dependent plasticity (STDP). We investigated the effect of lowering the number of pairings from 100 to 50, and 10 on STDP expression, using whole-cell recordings from pyramidal cells in rodent somatosensory cortical brain slices. We found that a low number of paired stimulations induces LTP at neocortical layer 4–2/3 synapses. Besides the asymmetric Hebbian STDP reported in the neocortex induced by 100 pairings, we observed a symmetric anti-Hebbian LTD for 50 pairings and unveiled a unidirectional Hebbian spike-timing-dependent LTP (tLTP) induced by 10–15 pairings. This tLTP was not mediated by NMDA receptor activation but requires CB1 receptors and transient receptor potential vanilloid type-1 (TRPV1) activated by endocannabinoids (eCBs). eCBs have been widely described as mediating short- and long-term synaptic depression. Here, the eCB-tLTP reported at neocortical synapses could constitute a substrate operating in the online learning of new associative memories or during the initial stages of learning. In addition, these findings should provide useful insight into the mechanisms underlying eCB-plasticity occurring during marijuana intoxication.

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.

Learning of Precise Spike Times with Homeostatic Membrane Potential Dependent Synaptic Plasticity.

Precise spatio-temporal patterns of neuronal action potentials underly e.g. sensory representations and control of muscle activities. However, it is not known how the synaptic efficacies in the neuronal networks of the brain adapt such that they can reliably generate spikes at specific points in time. Existing activity-dependent plasticity rules like Spike-Timing-Dependent Plasticity are agnostic to the goal of learning spike times. On the other hand, the existing formal and supervised learning algorithms perform a temporally precise comparison of projected activity with the target, but there is no known biologically plausible implementation of this comparison. Here, we propose a simple and local unsupervised synaptic plasticity mechanism that is derived from the requirement of a balanced membrane potential. Since the relevant signal for synaptic change is the postsynaptic voltage rather than spike times, we call the plasticity rule Membrane Potential Dependent Plasticity (MPDP). Combining our plasticity mechanism with spike after-hyperpolarization causes a sensitivity of synaptic change to pre- and postsynaptic spike times which can reproduce Hebbian spike timing dependent plasticity for inhibitory synapses as was found in experiments. In addition, the sensitivity of MPDP to the time course of the voltage when generating a spike allows MPDP to distinguish between weak (spurious) and strong (teacher) spikes, which therefore provides a neuronal basis for the comparison of actual and target activity. For spatio-temporal input spike patterns our conceptually simple plasticity rule achieves a surprisingly high storage capacity for spike associations. The sensitivity of the MPDP to the subthreshold membrane potential during training allows robust memory retrieval after learning even in the presence of activity corrupted by noise. We propose that MPDP represents a biophysically plausible mechanism to learn temporal target activity patterns.

Balancing feed-forward excitation and inhibition via Hebbian inhibitory synaptic plasticity.

It has been suggested that excitatory and inhibitory inputs to cortical cells are balanced, and that this balance is important for the highly irregular firing observed in the cortex. There are two hypotheses as to the origin of this balance. One assumes that it results from a stable solution of the recurrent neuronal dynamics. This model can account for a balance of steady state excitation and inhibition without fine tuning of parameters, but not for transient inputs. The second hypothesis suggests that the feed forward excitatory and inhibitory inputs to a postsynaptic cell are already balanced. This latter hypothesis thus does account for the balance of transient inputs. However, it remains unclear what mechanism underlies the fine tuning required for balancing feed forward excitatory and inhibitory inputs. Here we investigated whether inhibitory synaptic plasticity is responsible for the balance of transient feed forward excitation and inhibition. We address this issue in the framework of a model characterizing the stochastic dynamics of temporally anti-symmetric Hebbian spike timing dependent plasticity of feed forward excitatory and inhibitory synaptic inputs to a single post-synaptic cell. Our analysis shows that inhibitory Hebbian plasticity generates ‘negative feedback’ that balances excitation and inhibition, which contrasts with the ‘positive feedback’ of excitatory Hebbian synaptic plasticity. As a result, this balance may increase the sensitivity of the learning dynamics to the correlation structure of the excitatory inputs.

Distinct coincidence detectors govern the corticostriatal spike timing-dependent plasticity

Corticostriatal projections constitute the main input to the basal ganglia, an ensemble of interconnected subcortical nuclei involved in procedural learning. Thus, long-term plasticity at corticostriatal synapses would provide a basic mechanism for the function of basal ganglia in learning and memory. We had previously reported the existence of a corticostriatal anti-Hebbian spike timing-dependent plasticity (STDP) at synapses onto striatal output neurons, the medium-sized spiny neurons. Here, we show that the blockade of GABAergic transmission reversed the time dependence of corticostriatal STDP. We explored the receptors and signalling mechanisms involved in the corticostriatal STDP. Although classical models for STDP propose NMDA receptors as the unique coincidence detector, the involvement of multiple coincidence detectors has also been demonstrated. Here, we show that corticostriatal STDP depends on distinct coincidence detectors. Specifically, long-term potentiation is dependent on NMDA receptor activation, while long-term depression requires distinct coincidence detectors: the phospholipase Cbeta (PLCbeta) and the inositol-trisphosphate receptor (IP3R)-gated calcium stores. Furthermore, we found that PLCbeta activation is controlled by group-I metabotropic glutamate receptors, type-1 muscarinic receptors and voltage-sensitive calcium channel activities. Activation of PLCbeta and IP3Rs leads to robust retrograde endocannabinoid signalling mediated by 2-arachidonoyl-glycerol and cannabinoid CB1 receptors. Interestingly, the same coincidence detectors govern the corticostriatal anti-Hebbian STDP and the Hebbian STDP reported at cortical synapses. Therefore, LTP and LTD induced by STDP at corticostriatal synapses are mediated by independent signalling mechanisms, each one being controlled by distinct coincidence detectors.

Sparse odor representation and olfactory learning

Sensory systems create neural representations of environmental stimuli and these representations can be associated with other stimuli through learning. Are spike patterns the neural representations that get directly associated with reinforcement during conditioning? In the moth Manduca sexta, we found that odor presentations that support associative conditioning elicited only one or two spikes on the odor's onset (and sometimes offset) in each of a small fraction of Kenyon cells. Using associative conditioning procedures that effectively induced learning and varying the timing of reinforcement relative to spiking in Kenyon cells, we found that odor-elicited spiking in these cells ended well before the reinforcement was delivered. Furthermore, increasing the temporal overlap between spiking in Kenyon cells and reinforcement presentation actually reduced the efficacy of learning. Thus, spikes in Kenyon cells do not constitute the odor representation that coincides with reinforcement, and Hebbian spike timing-dependent plasticity in Kenyon cells alone cannot underlie this learning.

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.

Kinetic models of spike-timing dependent plasticity and their functional consequences in detecting correlations

Spike-timing dependent plasticity (STDP) is a type of synaptic modification found relatively recently, but the underlying biophysical mechanisms are still unclear. Several models of STDP have been proposed, and differ by their implementation, and in particular how synaptic weights saturate to their minimal and maximal values. We analyze here kinetic models of transmitter-receptor interaction and derive a series of STDP models. In general, such kinetic models predict progressive saturation of the weights. Various forms can be obtained depending on the hypotheses made in the kinetic model, and these include a simple linear dependence on the value of the weight ("soft bounds"), mixed soft and abrupt saturation ("hard bound"), or more complex forms. We analyze in more detail simple soft-bound models of Hebbian and anti-Hebbian STDPs, in which nonlinear spike interactions (triplets) are taken into account. We show that Hebbian STDPs can be used to selectively potentiate synapses that are correlated in time, while anti-Hebbian STDPs depress correlated synapses, despite the presence of nonlinear spike interactions. This correlation detection enables neurons to develop a selectivity to correlated inputs. We also examine different versions of kinetics-based STDP models and compare their sensitivity to correlations. We conclude that kinetic models generally predict soft-bound dynamics, and that such models seem ideal for detecting correlations among large numbers of inputs.