Robustness of STDP to spike timing jitter

SYNAPTIC PLASTICITY IN THE INJURED BRAIN DEPENDS ON THE TEMPORAL PATTERN OF STIMULATION

Neurostimulation protocols are increasingly used as therapeutic interventions, including for brain injury. In addition to the direct activation of neurons, these stimulation protocols are also likely to have downstream effects on those neurons' synaptic outputs. It is well known that alterations in the strength of synaptic connections (long-term potentiation, LTP; long-term depression, LTD) are sensitive to the frequency of stimulation used for induction, however little is known about the contribution of the temporal pattern of stimulation to the downstream synaptic plasticity that may be induced by neurostimulation in the injured brain. We explored interactions of the temporal pattern and frequency of neurostimulation in the normal cerebral cortex and after mild traumatic brain injury (mTBI), to inform therapies to strengthen or weaken neural circuits in injured brains, as well as to better understand the role of these factors in normal brain plasticity. Whole-cell (WC) patch-clamp recordings of evoked postsynaptic potentials (PSPs) in individual neurons, as well as field potential (FP) recordings, were made from layer 2/3 of visual cortex in response to stimulation of layer 4, in acute slices from control (naïve), sham operated, and mTBI rats. We compared synaptic plasticity induced by different stimulation protocols, each consisting of a specific frequency (1 Hz, 10 Hz, or 100 Hz), continuity (continuous or discontinuous), and temporal pattern (perfectly regular, slightly irregular, or highly irregular). At the individual neuron level, dramatic differences in plasticity outcome occurred when the highly irregular stimulation protocol was used at 1 Hz or 10 Hz, producing an overall LTD in controls and shams, but a robust overall LTP after mTBI. Consistent with the individual neuron results, the plasticity outcomes for simultaneous FP recordings were similar, indicative of our results generalizing to a larger scale synaptic network than can be sampled by individual WC recordings alone. In addition to the differences in plasticity outcome between control (naïve or sham) and injured brains, the dynamics of the changes in synaptic responses that developed during stimulation were predictive of the final plasticity outcome. Our results demonstrate that the temporal pattern of stimulation plays a role in the polarity and magnitude of synaptic plasticity induced in the cerebral cortex while highlighting differences between normal and injured brain responses. Moreover, these results may be useful for optimization of neurostimulation therapies to treat mTBI and other brain disorders, in addition to providing new insights into downstream plasticity signaling mechanisms in the normal brain.

Anti-Hebbian plasticity drives sequence learning in striatum

Spatio-temporal activity patterns have been observed in a variety of brain areas in spontaneous activity, prior to or during action, or in response to stimuli. Biological mechanisms endowing neurons with the ability to distinguish between different sequences remain largely unknown. Learning sequences of spikes raises multiple challenges, such as maintaining in memory spike history and discriminating partially overlapping sequences. Here, we show that anti-Hebbian spike-timing dependent plasticity (STDP), as observed at cortico-striatal synapses, can naturally lead to learning spike sequences. We design a spiking model of the striatal output neuron receiving spike patterns defined as sequential input from a fixed set of cortical neurons. We use a simple synaptic plasticity rule that combines anti-Hebbian STDP and non-associative potentiation for a subset of the presented patterns called rewarded patterns. We study the ability of striatal output neurons to discriminate rewarded from non-rewarded patterns by firing only after the presentation of a rewarded pattern. In particular, we show that two biological properties of striatal networks, spiking latency and collateral inhibition, contribute to an increase in accuracy, by allowing a better discrimination of partially overlapping sequences. These results suggest that anti-Hebbian STDP may serve as a biological substrate for learning sequences of spikes.

Synaptic plasticity through a naturalistic lens

From the myriad of studies on neuronal plasticity, investigating its underlying molecular mechanisms up to its behavioral relevance, a very complex landscape has emerged. Recent efforts have been achieved toward more naturalistic investigations as an attempt to better capture the synaptic plasticity underpinning of learning and memory, which has been fostered by the development of in vivo electrophysiological and imaging tools. In this review, we examine these naturalistic investigations, by devoting a first part to synaptic plasticity rules issued from naturalistic in vivo-like activity patterns. We next give an overview of the novel tools, which enable an increased spatio-temporal specificity for detecting and manipulating plasticity expressed at individual spines up to neuronal circuit level during behavior. Finally, we put particular emphasis on works considering brain-body communication loops and macroscale contributors to synaptic plasticity, such as body internal states and brain energy metabolism.

Nonlinear slow-timescale mechanisms in synaptic plasticity

Learning and memory rely on synapses changing their strengths in response to neural activity. However, there is a substantial gap between the timescales of neural electrical dynamics (1-100 ms) and organism behaviour during learning (seconds-minutes). What mechanisms bridge this timescale gap? What are the implications for theories of brain learning? Here I first cover experimental evidence for slow-timescale factors in plasticity induction. Then I review possible underlying cellular and synaptic mechanisms, and insights from recent computational models that incorporate such slow-timescale variables. I conclude that future progress in understanding brain learning across timescales will require both experimental and computational modelling studies that map out the nonlinearities implemented by both fast and slow plasticity mechanisms at synapses, and crucially, their joint interactions.

A stochastic model of hippocampal synaptic plasticity with geometrical readout of enzyme dynamics

Discovering the rules of synaptic plasticity is an important step for understanding brain learning. Existing plasticity models are either (1) top-down and interpretable, but not flexible enough to account for experimental data, or (2) bottom-up and biologically realistic, but too intricate to interpret and hard to fit to data. To avoid the shortcomings of these approaches, we present a new plasticity rule based on a geometrical readout mechanism that flexibly maps synaptic enzyme dynamics to predict plasticity outcomes. We apply this readout to a multi-timescale model of hippocampal synaptic plasticity induction that includes electrical dynamics, calcium, CaMKII and calcineurin, and accurate representation of intrinsic noise sources. Using a single set of model parameters, we demonstrate the robustness of this plasticity rule by reproducing nine published ex vivo experiments covering various spike-timing and frequency-dependent plasticity induction protocols, animal ages, and experimental conditions. Our model also predicts that in vivo-like spike timing irregularity strongly shapes plasticity outcome. This geometrical readout modelling approach can be readily applied to other excitatory or inhibitory synapses to discover their synaptic plasticity rules.

Synaptic Plasticity Is Predicted by Spatiotemporal Firing Rate Patterns and Robust to In Vivo-like Variability

Synaptic plasticity, the experience-induced change in connections between neurons, underlies learning and memory in the brain. Most of our understanding of synaptic plasticity derives from in vitro experiments with precisely repeated stimulus patterns; however, neurons exhibit significant variability in vivo during repeated experiences. Further, the spatial pattern of synaptic inputs to the dendritic tree influences synaptic plasticity, yet is not considered in most synaptic plasticity rules. Here, we investigate how spatiotemporal synaptic input patterns produce plasticity with in vivo-like conditions using a data-driven computational model with a plasticity rule based on calcium dynamics. Using in vivo spike train recordings as inputs to different size clusters of spines, we show that plasticity is strongly robust to trial-to-trial variability of spike timing. In addition, we derive general synaptic plasticity rules describing how spatiotemporal patterns of synaptic inputs control the magnitude and direction of plasticity. Synapses that strongly potentiated have greater firing rates and calcium concentration later in the trial, whereas strongly depressing synapses have hiring firing rates early in the trial. The neighboring synaptic activity influences the direction and magnitude of synaptic plasticity, with small clusters of spines producing the greatest increase in synaptic strength. Together, our results reveal that calcium dynamics can unify diverse plasticity rules and reveal how spatiotemporal firing rate patterns control synaptic plasticity.

Er81 Transcription Factor Fine-Tunes Striatal Cholinergic Interneuron Activity and Drives Habit Formation

The molecular mechanisms tuning cholinergic interneuron (CIN) activity, although crucial for striatal function and behavior, remain largely unexplored. Previous studies report that the Etv1/Er81 transcription factor is vital for regulating neuronal maturation and activity. While Er81 is known to be expressed in the striatum during development, its specific role in defining CIN properties and the resulting consequences on striatal function is unknown. We report here that Er81 is expressed in CINs and its specific ablation leads to prominent changes in their molecular, morphologic, and electrophysiological features. In particular, the lack of Er81 amplifies intrinsic delayed-rectifier and hyperpolarization-activated currents, which subsequently alters the tonic and phasic activity of CINs. We further reveal that Er81 expression is required for normal CIN pause and time-locked responses to sensorimotor inputs in awake mice. Overall, this study uncovers a new cell type-specific control of CIN function in the striatum which drives habit formation in adult male mice.SIGNIFICANCE STATEMENT Although previous studies have shown that cholinergic interneurons drive striatal activity and habit formation, the underlying molecular mechanisms controlling their function are unknown. Here we reveal that key cholinergic interneuron physiological properties are controlled by Er81, a transcription factor regulating neuronal activity and development in a cell-specific manner. Moreover, our findings uncover a link between the Er81-dependent molecular control of cholinergic interneuron function and habit formation in mice. These insights will contribute to the future enhancement of our understanding of disorders that involve behavioral inflexibility, such as autism and addiction.

Engrams of Fast Learning

Fast learning designates the behavioral and neuronal mechanisms underlying the acquisition of a long-term memory trace after a unique and brief experience. As such it is opposed to incremental, slower reinforcement or procedural learning requiring repetitive training. This learning process, found in most animal species, exists in a large spectrum of natural behaviors, such as one-shot associative, spatial, or perceptual learning, and is a core principle of human episodic memory. We review here the neuronal and synaptic long-term changes associated with fast learning in mammals and discuss some hypotheses related to their underlying mechanisms. We first describe the variety of behavioral paradigms used to test fast learning memories: those preferentially involve a single and brief (from few hundred milliseconds to few minutes) exposures to salient stimuli, sufficient to trigger a long-lasting memory trace and new adaptive responses. We then focus on neuronal activity patterns observed during fast learning and the emergence of long-term selective responses, before documenting the physiological correlates of fast learning. In the search for the engrams of fast learning, a growing body of evidence highlights long-term changes in gene expression, structural, intrinsic, and synaptic plasticities. Finally, we discuss the potential role of the sparse and bursting nature of neuronal activity observed during the fast learning, especially in the induction plasticity mechanisms leading to the rapid establishment of long-term synaptic modifications. We conclude with more theoretical perspectives on network dynamics that could enable fast learning, with an overview of some theoretical approaches in cognitive neuroscience and artificial intelligence.

Lights on Endocannabinoid-Mediated Synaptic Potentiation

The endocannabinoid (eCB) system is a lipid-based neurotransmitter complex that plays crucial roles in the neural control of learning and memory. The current model of eCB-mediated retrograde signaling is that eCBs released from postsynaptic elements travel retrogradely to presynaptic axon terminals, where they activate cannabinoid type-1 receptors (CB₁Rs) and ultimately decrease neurotransmitter release on a short- or long-term scale. An increasing body of evidence has enlarged this view and shows that eCBs, besides depressing synaptic transmission, are also able to increase neurotransmitter release at multiple synapses of the brain. This indicates that eCBs act as bidirectional regulators of synaptic transmission and plasticity. Recently, studies unveiled links between the expression of eCB-mediated long-term potentiation (eCB-LTP) and learning, and between its dysregulation and several pathologies. In this review article, we first distinguish the various forms of eCB-LTP based on their mechanisms, resulting from homosynaptically or heterosynaptically-mediated processes. Next, we consider the neuromodulation of eCB-LTP, its behavioral impact on learning and memory, and finally, eCB-LTP disruptions in various pathologies and its potential as a therapeutic target in disorders such as stress coping, addiction, Alzheimer’s and Parkinson’s disease, and pain. Cannabis is gaining popularity as a recreational substance as well as a medicine, and multiple eCB-based drugs are under development. In this context, it is critical to understand eCB-mediated signaling in its multi-faceted complexity. Indeed, the bidirectional nature of eCB-based neuromodulation may offer an important key to interpret the functions of the eCB system and how it is impacted by cannabis and other drugs.

Tonic GABAA Conductance Favors Spike-Timing-Dependent over Theta-Burst-Induced Long-Term Potentiation in the Hippocampus

Synaptic plasticity is triggered by different patterns of network activity. Here, we investigated how LTP in CA3-CA1 synapses induced by different stimulation patterns is affected by tonic GABA_A conductances in rat hippocampal slices. Spike-timing-dependent LTP was induced by pairing Schaffer collateral stimulation with antidromic stimulation of CA1 pyramidal neurons. Theta-burst-induced LTP was induced by theta-burst stimulation of Schaffer collaterals. We mimicked increased tonic GABA_A conductance by bath application of 30 μm GABA. Surprisingly, tonic GABA_A conductance selectively suppressed theta-burst-induced LTP but not spike-timing-dependent LTP. We combined whole-cell patch-clamp electrophysiology, two-photon Ca²⁺ imaging, glutamate uncaging, and mathematical modeling to dissect the mechanisms underlying these differential effects of tonic GABA_A conductance. We found that Ca²⁺ transients during pairing of an action potential with an EPSP were less sensitive to tonic GABA_A conductance-induced shunting inhibition than Ca²⁺ transients induced by EPSP burst. Our results may explain how different forms of memory are affected by increasing tonic GABA_A conductances under physiological or pathologic conditions, as well as under the influence of substances that target extrasynaptic GABA_A receptors (e.g., neurosteroids, sedatives, antiepileptic drugs, and alcohol).SIGNIFICANCE STATEMENT Brain activity is associated with neuronal firing and synaptic signaling among neurons. Synaptic plasticity represents a mechanism for learning and memory. However, some neurotransmitters that escape the synaptic cleft or are released by astrocytes can target extrasynaptic receptors. Extrasynaptic GABA_A receptors mediate tonic conductances that reduce the excitability of neurons by shunting. This results in the decreased ability for neurons to fire action potentials, but when action potentials are successfully triggered, tonic conductances are unable to reduce them significantly. As such, tonic GABA_A conductances have minimal effects on spike-timing-dependent synaptic plasticity while strongly attenuating the plasticity evoked by EPSP bursts. Our findings shed light on how changes in tonic conductances can selectively affect different forms of learning and memory.

Concurrent Thalamostriatal and Corticostriatal Spike-Timing-Dependent Plasticity and Heterosynaptic Interactions Shape Striatal Plasticity Map

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.

Environmental enrichment shapes striatal spike-timing-dependent plasticity in vivo

Behavioural experience, such as environmental enrichment (EE), induces long-term effects on learning and memory. Learning can be assessed with the Hebbian paradigm, such as spike-timing-dependent plasticity (STDP), which relies on the timing of neuronal activity on either side of the synapse. Although EE is known to control neuronal excitability and consequently spike timing, whether EE shapes STDP remains unknown. Here, using in vivo long-duration intracellular recordings at the corticostriatal synapses we show that EE promotes asymmetric anti-Hebbian STDP, i.e. spike-timing-dependent-potentiation (tLTP) for post-pre pairings and spike-timing-dependent-depression (tLTD) for pre-post pairings, whereas animals grown in standard housing show mainly tLTD and a high failure rate of plasticity. Indeed, in adult rats grown in standard conditions, we observed unidirectional plasticity (mainly symmetric anti-Hebbian tLTD) within a large temporal window (~200 ms). However, rats grown for two months in EE displayed a bidirectional STDP (tLTP and tLTD depending on spike timing) in a more restricted temporal window (~100 ms) with low failure rate of plasticity. We also found that the effects of EE on STDP characteristics are influenced by the anaesthesia status: the deeper the anaesthesia, the higher the absence of plasticity. These findings establish a central role for EE and the anaesthetic regime in shaping in vivo, a synaptic Hebbian learning rule such as STDP.

BDNF Controls Bidirectional Endocannabinoid Plasticity at Corticostriatal Synapses

The 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.

Low Cost Interconnected Architecture for the Hardware Spiking Neural Networks

A novel low cost interconnected architecture (LCIA) is proposed in this paper, which is an efficient solution for the neuron interconnections for the hardware spiking neural networks (SNNs). It is based on an all-to-all connection that takes each paired input and output nodes of multi-layer SNNs as the source and destination of connections. The aim is to maintain an efficient routing performance under low hardware overhead. A Networks-on-Chip (NoC) router is proposed as the fundamental component of the LCIA, where an effective scheduler is designed to address the traffic challenge due to irregular spikes. The router can find requests rapidly, make the arbitration decision promptly, and provide equal services to different network traffic requests. Experimental results show that the LCIA can manage the intercommunication of the multi-layer neural networks efficiently and have a low hardware overhead which can maintain the scalability of hardware SNNs.

Bridging the gap between striatal plasticity and learning

The striatum, the main input nucleus of the basal ganglia, controls goal-directed behavior and procedural learning. Striatal projection neurons integrate glutamatergic inputs from cortex and thalamus together with neuromodulatory systems, and are subjected to plasticity. Striatal projection neurons exhibit bidirectional plasticity (LTP and LTD) when exposed to Hebbian paradigms. Importantly, correlative and even causal links between procedural learning and striatal plasticity have recently been shown. This short review summarizes the current view on striatal plasticity (with a focus on spike-timing-dependent plasticity), recent studies aiming at bridging in vivo skill acquisition and striatal plasticity, the temporal credit-assignment problem, and the gaps that remain to be filled.

Dopamine-endocannabinoid interactions mediate spike-timing-dependent potentiation in the striatum

Dopamine modulates striatal synaptic plasticity, a key substrate for action selection and procedural learning. Thus, characterizing the repertoire of activity-dependent plasticity in striatum and its dependence on dopamine is of crucial importance. We recently unraveled a striatal spike-timing-dependent long-term potentiation (tLTP) mediated by endocannabinoids (eCBs) and induced with few spikes (~5–15). Whether this eCB-tLTP interacts with the dopaminergic system remains to be investigated. Here, we report that eCB-tLTP is impaired in a rodent model of Parkinson’s disease and rescued by L-DOPA. Dopamine controls eCB-tLTP via dopamine type-2 receptors (D₂R) located presynaptically in cortical terminals. Dopamine–endocannabinoid interactions via D₂R are required for the emergence of tLTP in response to few coincident pre- and post-synaptic spikes and control eCB-plasticity by modulating the long-term potentiation (LTP)/depression (LTD) thresholds. While usually considered as a depressing synaptic function, our results show that eCBs in the presence of dopamine constitute a versatile system underlying bidirectional plasticity implicated in basal ganglia pathophysiology.

Dopamine tightly regulates plasticity at corticostriatal synapses. Here, the authors report that endocannabinoid dependent LTP induced with few spikes in the striatum is impaired in a rodent model of Parkinson’s disease, requires dopamine through presynaptic D2 receptors located on corticostriatal inputs.

Modulation of Spike-Timing Dependent Plasticity: Towards the Inclusion of a Third Factor in Computational Models

In spike-timing dependent plasticity (STDP) change in synaptic strength depends on the timing of pre- vs. postsynaptic spiking activity. Since STDP is in compliance with Hebb's postulate, it is considered one of the major mechanisms of memory storage and recall. STDP comprises a system of two coincidence detectors with N-methyl-D-aspartate receptor (NMDAR) activation often posited as one of the main components. Numerous studies have unveiled a third component of this coincidence detection system, namely neuromodulation and glia activity shaping STDP. Even though dopaminergic control of STDP has most often been reported, acetylcholine, noradrenaline, nitric oxide (NO), brain-derived neurotrophic factor (BDNF) or gamma-aminobutyric acid (GABA) also has been shown to effectively modulate STDP. Furthermore, it has been demonstrated that astrocytes, via the release or uptake of glutamate, gate STDP expression. At the most fundamental level, the timing properties of STDP are expected to depend on the spatiotemporal dynamics of the underlying signaling pathways. However in most cases, due to technical limitations experiments grant only indirect access to these pathways. Computational models carefully constrained by experiments, allow for a better qualitative understanding of the molecular basis of STDP and its regulation by neuromodulators. Recently, computational models of calcium dynamics and signaling pathway molecules have started to explore STDP emergence in ex and in vivo-like conditions. These models are expected to reproduce better at least part of the complex modulation of STDP as an emergent property of the underlying molecular pathways. Elucidation of the mechanisms underlying STDP modulation and its consequences on network dynamics is of critical importance and will allow better understanding of the major mechanisms of memory storage and recall both in health and disease.