Gain in sensitivity and loss in temporal contrast of STDP by dopaminergic modulation at hippocampal synapses

Distinct catecholaminergic pathways projecting to hippocampal CA1 transmit contrasting signals during navigation in familiar and novel environments

Neuromodulatory inputs to the hippocampus play pivotal roles in modulating synaptic plasticity, shaping neuronal activity, and influencing learning and memory. Recently it has been shown that the main sources of catecholamines to the hippocampus, ventral tegmental area (VTA) and locus coeruleus (LC), may have overlapping release of neurotransmitters and effects on the hippocampus. Therefore, to dissect the impacts of both VTA and LC circuits on hippocampal function, a thorough examination of how these pathways might differentially operate during behavior and learning is necessary. We therefore utilized 2-photon microscopy to functionally image the activity of VTA and LC axons within the CA1 region of the dorsal hippocampus in head-fixed male mice navigating linear paths within virtual reality (VR) environments. We found that within familiar environments some VTA axons and the vast majority of LC axons showed a correlation with the animals' running speed. However, as mice approached previously learned rewarded locations, a large majority of VTA axons exhibited a gradual ramping-up of activity, peaking at the reward location. In contrast, LC axons displayed a pre-movement signal predictive of the animal's transition from immobility to movement. Interestingly, a marked divergence emerged following a switch from the familiar to novel VR environments. Many LC axons showed large increases in activity that remained elevated for over a minute, while the previously observed VTA axon ramping-to-reward dynamics disappeared during the same period. In conclusion, these findings highlight distinct roles of VTA and LC catecholaminergic inputs in the dorsal CA1 hippocampal region. These inputs encode unique information, with reward information in VTA inputs and novelty and kinematic information in LC inputs, likely contributing to differential modulation of hippocampal activity during behavior and learning.

Ventral tegmental area dopamine projections to the hippocampus trigger long-term potentiation and contextual learning

In most models of neuronal plasticity and memory, dopamine is thought to promote the long-term maintenance of Long-Term Potentiation (LTP) underlying memory processes, but not the initiation of plasticity or new information storage. Here, we used optogenetic manipulation of midbrain dopamine neurons in male DAT::Cre mice, and discovered that stimulating the Schaffer collaterals - the glutamatergic axons connecting CA3 and CA1 regions - of the dorsal hippocampus concomitantly with midbrain dopamine terminals within a 200 millisecond time-window triggers LTP at glutamatergic synapses. Moreover, we showed that the stimulation of this dopaminergic pathway facilitates contextual learning in awake behaving mice, while its inhibition hinders it. Thus, activation of midbrain dopamine can operate as a teaching signal that triggers NeoHebbian LTP and promotes supervised learning.

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 brain-inspired algorithm that mitigates catastrophic forgetting of artificial and spiking neural networks with low computational cost

Neuromodulators in the brain act globally at many forms of synaptic plasticity, represented as metaplasticity, which is rarely considered by existing spiking (SNNs) and nonspiking artificial neural networks (ANNs). Here, we report an efficient brain-inspired computing algorithm for SNNs and ANNs, referred to here as neuromodulation-assisted credit assignment (NACA), which uses expectation signals to induce defined levels of neuromodulators to selective synapses, whereby the long-term synaptic potentiation and depression are modified in a nonlinear manner depending on the neuromodulator level. The NACA algorithm achieved high recognition accuracy with substantially reduced computational cost in learning spatial and temporal classification tasks. Notably, NACA was also verified as efficient for learning five different class continuous learning tasks with varying degrees of complexity, exhibiting a markedly mitigated catastrophic forgetting at low computational cost. Mapping synaptic weight changes showed that these benefits could be explained by the sparse and targeted synaptic modifications attributed to expectation-based global neuromodulation.

Adaptive STDP-based on-chip spike pattern detection

A spiking neural network (SNN) is a bottom-up tool used to describe information processing in brain microcircuits. It is becoming a crucial neuromorphic computational model. Spike-timing-dependent plasticity (STDP) is an unsupervised brain-like learning rule implemented in many SNNs and neuromorphic chips. However, a significant performance gap exists between ideal model simulation and neuromorphic implementation. The performance of STDP learning in neuromorphic chips deteriorates because the resolution of synaptic efficacy in such chips is generally restricted to 6 bits or less, whereas simulations employ the entire 64-bit floating-point precision available on digital computers. Previously, we introduced a bio-inspired learning rule named adaptive STDP and demonstrated via numerical simulation that adaptive STDP (using only 4-bit fixed-point synaptic efficacy) performs similarly to STDP learning (using 64-bit floating-point precision) in a noisy spike pattern detection model. Herein, we present the experimental results demonstrating the performance of adaptive STDP learning. To the best of our knowledge, this is the first study that demonstrates unsupervised noisy spatiotemporal spike pattern detection to perform well and maintain the simulation performance on a mixed-signal CMOS neuromorphic chip with low-resolution synaptic efficacy. The chip was designed in Taiwan Semiconductor Manufacturing Company (TSMC) 250 nm CMOS technology node and comprises a soma circuit and 256 synapse circuits along with their learning circuitry.

Meta-SpikePropamine: learning to learn with synaptic plasticity in spiking neural networks

We propose that in order to harness our understanding of neuroscience toward machine learning, we must first have powerful tools for training brain-like models of learning. Although substantial progress has been made toward understanding the dynamics of learning in the brain, neuroscience-derived models of learning have yet to demonstrate the same performance capabilities as methods in deep learning such as gradient descent. Inspired by the successes of machine learning using gradient descent, we introduce a bi-level optimization framework that seeks to both solve online learning tasks and improve the ability to learn online using models of plasticity from neuroscience. We demonstrate that models of three-factor learning with synaptic plasticity taken from the neuroscience literature can be trained in Spiking Neural Networks (SNNs) with gradient descent via a framework of learning-to-learn to address challenging online learning problems. This framework opens a new path toward developing neuroscience inspired online learning algorithms.

Dendrites help mitigate the plasticity-stability dilemma

With Hebbian learning 'who fires together wires together', well-known problems arise. Hebbian plasticity can cause unstable network dynamics and overwrite stored memories. Because the known homeostatic plasticity mechanisms tend to be too slow to combat unstable dynamics, it has been proposed that plasticity must be highly gated and synaptic strengths limited. While solving the issue of stability, gating and limiting plasticity does not solve the stability-plasticity dilemma. We propose that dendrites enable both stable network dynamics and considerable synaptic changes, as they allow the gating of plasticity in a compartment-specific manner. We investigate how gating plasticity influences network stability in plastic balanced spiking networks of neurons with dendrites. We compare how different ways to gate plasticity, namely via modulating excitability, learning rate, and inhibition increase stability. We investigate how dendritic versus perisomatic gating allows for different amounts of weight changes in stable networks. We suggest that the compartmentalisation of pyramidal cells enables dendritic synaptic changes while maintaining stability. We show that the coupling between dendrite and soma is critical for the plasticity-stability trade-off. Finally, we show that spatially restricted plasticity additionally improves stability.

Spike timing-dependent plasticity and memory

Spike timing-dependent plasticity (STDP) is a bidirectional form of synaptic plasticity discovered about 30 years ago and based on the relative timing of pre- and post-synaptic spiking activity with a millisecond precision. STDP is thought to be involved in the formation of memory but the millisecond-precision spike-timing required for STDP is difficult to reconcile with the much slower timescales of behavioral learning. This review therefore aims to expose and discuss recent findings about i) the multiple STDP learning rules at both excitatory and inhibitory synapses in vitro, ii) the contribution of STDP-like synaptic plasticity in the formation of memory in vivo and iii) the implementation of STDP rules in artificial neural networks and memristive devices.

Place cells are nonrandomly clustered by field location in CA1 hippocampus

A challenge in both modern and historic neuroscience has been achieving an understanding of neuron circuits, and determining the computational and organizational principles that underlie these circuits. Deeper understanding of the organization of brain circuits and cell types, including in the hippocampus, is required for advances in behavioral and cognitive neuroscience, as well as for understanding principles governing brain development and evolution. In this manuscript, we pioneer a new method to analyze the spatial clustering of active neurons in the hippocampus. We use calcium imaging and a rewarded navigation task to record from 100 s of place cells in the CA1 of freely moving rats. We then use statistical techniques developed for and in widespread use in geographic mapping studies, global Moran's I, and local Moran's I to demonstrate that cells that code for similar spatial locations tend to form small spatial clusters. We present evidence that this clustering is not the result of artifacts from calcium imaging, and show that these clusters are primarily formed by cells that have place fields around previously rewarded locations. We go on to show that, although cells with similar place fields tend to form clusters, there is no obvious topographic mapping of environmental location onto the hippocampus, such as seen in the visual cortex. Insights into hippocampal organization, as in this study, can elucidate mechanisms underlying motivational behaviors, spatial navigation, and memory formation.

Dopaminergic regulation of hippocampal plasticity, learning, and memory

The hippocampus is responsible for encoding behavioral episodes into short-term and long-term memory. The circuits that mediate these processes are subject to neuromodulation, which involves regulation of synaptic plasticity and local neuronal excitability. In this review, we present evidence to demonstrate the influence of dopaminergic neuromodulation on hippocampus-dependent memory, and we address the controversy surrounding the source of dopamine innervation. First, we summarize historical and recent retrograde and anterograde anatomical tracing studies of direct dopaminergic projections from the ventral tegmental area and discuss dopamine release from the adrenergic locus coeruleus. Then, we present evidence of dopaminergic modulation of synaptic plasticity in the hippocampus. Plasticity mechanisms are examined in brain slices and in recordings from in vivo neuronal populations in freely moving rodents. Finally, we review pharmacological, genetic, and circuitry research that demonstrates the importance of dopamine release for learning and memory tasks while dissociating anatomically distinct populations of direct dopaminergic inputs.

Long-term memory, synaptic plasticity and dopamine in rodent medial prefrontal cortex: Role in executive functions

Mnemonic functions, supporting rodent behavior in complex tasks, include both long-term and (short-term) working memory components. While working memory is thought to rely on persistent activity states in an active neural network, long-term memory and synaptic plasticity contribute to the formation of the underlying synaptic structure, determining the range of possible states. Whereas, the implication of working memory in executive functions, mediated by the prefrontal cortex (PFC) in primates and rodents, has been extensively studied, the contribution of long-term memory component to these tasks received little attention. This review summarizes available experimental data and theoretical work concerning cellular mechanisms of synaptic plasticity in the medial region of rodent PFC and the link between plasticity, memory and behavior in PFC-dependent tasks. A special attention is devoted to unique properties of dopaminergic modulation of prefrontal synaptic plasticity and its contribution to executive functions.

Adaptive control of synaptic plasticity integrates micro- and macroscopic network function

Synaptic plasticity configures interactions between neurons and is therefore likely to be a primary driver of behavioral learning and development. How this microscopic-macroscopic interaction occurs is poorly understood, as researchers frequently examine models within particular ranges of abstraction and scale. Computational neuroscience and machine learning models offer theoretically powerful analyses of plasticity in neural networks, but results are often siloed and only coarsely linked to biology. In this review, we examine connections between these areas, asking how network computations change as a function of diverse features of plasticity and vice versa. We review how plasticity can be controlled at synapses by calcium dynamics and neuromodulatory signals, the manifestation of these changes in networks, and their impacts in specialized circuits. We conclude that metaplasticity-defined broadly as the adaptive control of plasticity-forges connections across scales by governing what groups of synapses can and can't learn about, when, and to what ends. The metaplasticity we discuss acts by co-opting Hebbian mechanisms, shifting network properties, and routing activity within and across brain systems. Asking how these operations can go awry should also be useful for understanding pathology, which we address in the context of autism, schizophrenia and Parkinson's disease.

Neuromodulator-dependent synaptic tagging and capture retroactively controls neural coding in spiking neural networks

Events that are important to an individual's life trigger neuromodulator release in brain areas responsible for cognitive and behavioral function. While it is well known that the presence of neuromodulators such as dopamine and norepinephrine is required for memory consolidation, the impact of neuromodulator concentration is, however, less understood. In a recurrent spiking neural network model featuring neuromodulator-dependent synaptic tagging and capture, we study how synaptic memory consolidation depends on the amount of neuromodulator present in the minutes to hours after learning. We find that the storage of rate-based and spike timing-based information is controlled by the level of neuromodulation. Specifically, we find better recall of temporal information for high levels of neuromodulation, while we find better recall of rate-coded spatial patterns for lower neuromodulation, mediated by the selection of different groups of synapses for consolidation. Hence, our results indicate that in minutes to hours after learning, the level of neuromodulation may alter the process of synaptic consolidation to ultimately control which type of information becomes consolidated in the recurrent neural network.

Contingent Amygdala Inputs Trigger Heterosynaptic LTP at Hippocampus-To-Accumbens Synapses

The nucleus accumbens shell (NAcSh) is a key brain region where environmental cues acquire incentive salience to reinforce motivated behaviors. Principal medium spiny neurons (MSNs) in the NAcSh receive extensive glutamatergic projections from limbic regions, among which, the ventral hippocampus (vH) transmits information enriched in contextual cues, and the basolateral amygdala (BLA) encodes real-time arousing states. The vH and BLA project convergently to NAcSh MSNs, both activated in a time-locked manner on a cue-conditioned motivational action. In brain slices prepared from male and female mice, we show that co-activation of the two projections induces long-term potentiation (LTP) at vH-to-NAcSh synapses without affecting BLA-to-NAcSh synapses, revealing a heterosynaptic mechanism through which BLA signals persistently increase the temporally contingent vH-to-NAcSh transmission. Furthermore, this LTP is more prominent in dopamine D1 receptor-expressing (D1) MSNs than D2 MSNs and can be prevented by inhibition of either D1 receptors or dopaminergic terminals in NAcSh. This heterosynaptic LTP may provide a dopamine-guided mechanism through which vH-encoded cue inputs that are contingent to BLA activation acquire increased circuit representation to reinforce behavior.SIGNIFICANCE STATEMENT In motivated behaviors, environmental cues associated with arousing stimuli acquire increased incentive salience, processes mediated in part by the nucleus accumbens (NAc). NAc principal neurons receive glutamatergic projections from the ventral hippocampus (vH) and basolateral amygdala (BLA), which transmit information encoding contextual cues and affective states, respectively. Our results show that co-activation of the two projections induces long-term potentiation (LTP) at vH-to-NAc synapses without affecting BLA-to-NAc synapses, revealing a heterosynaptic mechanism through which BLA signals potentiate the temporally contingent vH-to-NAc transmission. Furthermore, this LTP is prevented by inhibition of either D1 receptors or dopaminergic axons. This heterosynaptic LTP may provide a dopamine-guided mechanism through which vH-encoded cue inputs that are contingent to BLA activation acquire increased circuit representation to reinforce behavior.

Modulation of hippocampal plasticity in learning and memory

Synaptic plasticity plays a central role in the study of neural mechanisms of learning and memory. Plasticity rules are not invariant over time but are under neuromodulatory control, enabling behavioral states to influence memory formation. Neuromodulation controls synaptic plasticity at network level by directing information flow, at circuit level through changes in excitation/inhibition balance, and at synaptic level through modulation of intracellular signaling cascades. Although most research has focused on modulation of principal neurons, recent progress has uncovered important roles for interneurons in not only routing information, but also setting conditions for synaptic plasticity. Moreover, astrocytes have been shown to both gate and mediate plasticity. These additional mechanisms must be considered for a comprehensive mechanistic understanding of learning and memory.

The Origin of Abnormal Beta Oscillations in the Parkinsonian Corticobasal Ganglia Circuits

Parkinson's disease (PD) is a neurodegenerative brain disorder associated with motor and nonmotor symptoms. Exaggerated beta band (15–30 Hz) neuronal oscillations are widely observed in corticobasal ganglia (BG) circuits during parkinsonism. Abnormal beta oscillations have been linked to motor symptoms of PD, but their exact relationship is poorly understood. Nevertheless, reduction of beta oscillations can induce therapeutic effects in PD patients. While it is widely believed that the external globus pallidus (GPe) and subthalamic nucleus (STN) are jointly responsible for abnormal rhythmogenesis in the parkinsonian BG, the role of other cortico-BG circuits cannot be ignored. To shed light on the origin of abnormal beta oscillations in PD, here we review changes of neuronal activity observed in experimental PD models and discuss how the cortex and different BG nuclei cooperate to generate and stabilize abnormal beta oscillations during parkinsonism. This may provide further insights into the complex relationship between abnormal beta oscillations and motor dysfunction in PD, which is crucial for potential target-specific therapeutic interventions in PD patients.

Spike-timing-dependent plasticity rewards synchrony rather than causality

Spike-timing-dependent plasticity (STDP) is a candidate mechanism for information storage in the brain, but the whole-cell recordings required for the experimental induction of STDP are typically limited to 1 h. This mismatch of time scales is a long-standing weakness in synaptic theories of memory. Here we use spectrally separated optogenetic stimulation to fire precisely timed action potentials (spikes) in CA3 and CA1 pyramidal cells. Twenty minutes after optogenetic induction of STDP (oSTDP), we observed timing-dependent depression (tLTD) and timing-dependent potentiation (tLTP), depending on the sequence of spiking. As oSTDP does not require electrodes, we could also assess the strength of these paired connections three days later. At this late time point, late tLTP was observed for both causal (CA3 before CA1) and anticausal (CA1 before CA3) timing, but not for asynchronous activity patterns (Δt = 50 ms). Blocking activity after induction of oSTDP prevented stable potentiation. Our results confirm that neurons wire together if they fire together, but suggest that synaptic depression after anticausal activation (tLTD) is a transient phenomenon.

Canonical neural networks perform active inference

This work considers a class of canonical neural networks comprising rate coding models, wherein neural activity and plasticity minimise a common cost function-and plasticity is modulated with a certain delay. We show that such neural networks implicitly perform active inference and learning to minimise the risk associated with future outcomes. Mathematical analyses demonstrate that this biological optimisation can be cast as maximisation of model evidence, or equivalently minimisation of variational free energy, under the well-known form of a partially observed Markov decision process model. This equivalence indicates that the delayed modulation of Hebbian plasticity-accompanied with adaptation of firing thresholds-is a sufficient neuronal substrate to attain Bayes optimal inference and control. We corroborated this proposition using numerical analyses of maze tasks. This theory offers a universal characterisation of canonical neural networks in terms of Bayesian belief updating and provides insight into the neuronal mechanisms underlying planning and adaptive behavioural control.

Spike-Timing-Dependent Plasticity Mediated by Dopamine and its Role in Parkinson's Disease Pathophysiology

Parkinson's disease (PD) is a multi-systemic neurodegenerative brain disorder. Motor symptoms of PD are linked to the significant dopamine (DA) loss in substantia nigra pars compacta (SNc) followed by basal ganglia (BG) circuit dysfunction. Increasing experimental and computational evidence indicates that (synaptic) plasticity plays a key role in the emergence of PD-related pathological changes following DA loss. Spike-timing-dependent plasticity (STDP) mediated by DA provides a mechanistic model for synaptic plasticity to modify synaptic connections within the BG according to the neuronal activity. To shed light on how DA-mediated STDP can shape neuronal activity and synaptic connectivity in the PD condition, we reviewed experimental and computational findings addressing the modulatory effect of DA on STDP as well as other plasticity mechanisms and discussed their potential role in PD pathophysiology and related network dynamics and connectivity. In particular, reshaping of STDP profiles together with other plasticity-mediated processes following DA loss may abnormally modify synaptic connections in competing pathways of the BG. The cascade of plasticity-induced maladaptive or compensatory changes can impair the excitation-inhibition balance towards the BG output nuclei, leading to the emergence of pathological activity-connectivity patterns in PD. Pre-clinical, clinical as well as computational studies reviewed here provide an understanding of the impact of synaptic plasticity and other plasticity mechanisms on PD pathophysiology, especially PD-related network activity and connectivity, after DA loss. This review may provide further insights into the abnormal structure-function relationship within the BG contributing to the emergence of pathological states in PD. Specifically, this review is intended to provide detailed information for the development of computational network models for PD, serving as testbeds for the development and optimization of invasive and non-invasive brain stimulation techniques. Computationally derived hypotheses may accelerate the development of therapeutic stimulation techniques and potentially reduce the number of related animal experiments.

Active inference leads to Bayesian neurophysiology

The neuronal substrates that implement the free-energy principle and ensuing active inference at the neuron and synapse level have not been fully elucidated. This Review considers possible neuronal substrates underlying the principle. First, the foundations of the free-energy principle are introduced, and then its ability to empirically explain various brain functions and psychological and biological phenomena in terms of Bayesian inference is described. Mathematically, the dynamics of neural activity and plasticity that minimise a cost function can be cast as performing Bayesian inference that minimises variational free energy. This equivalence licenses the adoption of the free-energy principle as a universal characterisation of neural networks. Further, the neural network structure itself represents a generative model under which an agent operates. A virtue of this perspective is that it enables the formal association of neural network properties with prior beliefs that regulate inference and learning. The possible neuronal substrates that implement prior beliefs and how to empirically examine the theory are discussed. This perspective renders brain activity explainable, leading to a deeper understanding of the neuronal mechanisms underlying basic psychology and psychiatric disorders in terms of an implicit generative model.

Distributed Phase Oscillatory Excitation Efficiently Produces Attractors Using Spike-Timing-Dependent Plasticity

The brain is thought to represent information in the form of activity in distributed groups of neurons known as attractors. We show here that in a randomly connected network of simulated spiking neurons, periodic stimulation of neurons with distributed phase offsets, along with standard spike-timing-dependent plasticity (STDP), efficiently creates distributed attractors. These attractors may have a consistent ordered firing pattern or become irregular, depending on the conditions. We also show that when two such attractors are stimulated in sequence, the same STDP mechanism can create a directed association between them, forming the basis of an associative network. We find that for an STDP time constant of 20 ms, the dependence of the efficiency of attractor creation on the driving frequency has a broad peak centered around 8 Hz. Upon restimulation, the attractors self-oscillate, but with an oscillation frequency that is higher than the driving frequency, ranging from 10 to 100 Hz.

Calcium and Spike Timing-Dependent Plasticity

Since its discovery, spike timing-dependent synaptic plasticity (STDP) has been thought to be a primary mechanism underlying the brain's ability to learn and to form new memories. However, despite the enormous interest in both the experimental and theoretical neuroscience communities in activity-dependent plasticity, it is still unclear whether plasticity rules inferred from in vitro experiments apply to in vivo conditions. Among the multiple reasons why plasticity rules in vivo might differ significantly from in vitro studies is that extracellular calcium concentration use in most studies is higher than concentrations estimated in vivo. STDP, like many forms of long-term synaptic plasticity, strongly depends on intracellular calcium influx for its induction. Here, we discuss the importance of considering physiological levels of extracellular calcium concentration to study functional plasticity.

Calcium-Permeable AMPA Receptors Mediate Timing-Dependent LTP Elicited by Low Repeat Coincident Pre- and Postsynaptic Activity at Schaffer Collateral-CA1 Synapses

High-frequency stimulation induced long-term potentiation (LTP) and low-frequency stimulation induced LTD are considered as cellular models of memory formation. Interestingly, spike timing-dependent plasticity (STDP) can induce equally robust timing-dependent LTP (t-LTP) and t-LTD in response to low frequency repeats of coincident action potential (AP) firing in presynaptic and postsynaptic cells. Commonly, STDP paradigms relying on 25-100 repeats of coincident AP firing are used to elicit t-LTP or t-LTD, but the minimum number of repeats required for successful STDP is barely explored. However, systematic investigation of physiologically relevant low repeat STDP paradigms is of utmost importance to explain learning mechanisms in vivo. Here, we examined low repeat STDP at Schaffer collateral-CA1 synapses by pairing one presynaptic AP with either one postsynaptic AP (1:1 t-LTP), or a burst of 4 APs (1:4 t-LTP) and found 3-6 repeats to be sufficient to elicit t-LTP. 6× 1:1 t-LTP required postsynaptic Ca2+ influx via NMDARs and L-type VGCCs and was mediated by increased presynaptic glutamate release. In contrast, 1:4 t-LTP depended on postsynaptic metabotropic GluRs and ryanodine receptor signaling and was mediated by postsynaptic insertion of AMPA receptors. Unexpectedly, both 6× t-LTP variants were strictly dependent on activation of postsynaptic Ca2+-permeable AMPARs but were differentially regulated by dopamine receptor signaling. Our data show that synaptic changes induced by only 3-6 repeats of mild STDP stimulation occurring in ≤10 s can take place on time scales observed also during single trial learning.

NMDA receptor-BK channel coupling regulates synaptic plasticity in the barrel cortex

N-methyl-D-aspartate (NMDA) receptors are critical triggers for neuronal plasticity. We show that large-conductance Ca²⁺- and voltage-gated K⁺ (BK) channels serve as feedback regulators of NMDA receptor–mediated calcium influx to shape NMDA receptor–mediated synaptic potentials and consequently elevate the threshold for triggering plasticity at a subset of synapses.

Postsynaptic N-methyl-D-aspartate receptors (NMDARs) are crucial mediators of synaptic plasticity due to their ability to act as coincidence detectors of presynaptic and postsynaptic neuronal activity. However, NMDARs exist within the molecular context of a variety of postsynaptic signaling proteins, which can fine-tune their function. Here, we describe a form of NMDAR suppression by large-conductance Ca²⁺- and voltage-gated K⁺ (BK) channels in the basal dendrites of a subset of barrel cortex layer 5 pyramidal neurons. We show that NMDAR activation increases intracellular Ca²⁺ in the vicinity of BK channels, thus activating K⁺ efflux and strong negative feedback inhibition. We further show that neurons exhibiting such NMDAR–BK coupling serve as high-pass filters for incoming synaptic inputs, precluding the induction of spike timing–dependent plasticity. Together, these data suggest that NMDAR-localized BK channels regulate synaptic integration and provide input-specific synaptic diversity to a thalamocortical circuit.

From synapse to network: models of information storage and retrieval in neural circuits

The mechanisms of information storage and retrieval in brain circuits are still the subject of debate. It is widely believed that information is stored at least in part through changes in synaptic connectivity in networks that encode this information and that these changes lead in turn to modifications of network dynamics, such that the stored information can be retrieved at a later time. Here, we review recent progress in deriving synaptic plasticity rules from experimental data and in understanding how plasticity rules affect the dynamics of recurrent networks. We show that the dynamics generated by such networks exhibit a large degree of diversity, depending on parameters, similar to experimental observations in vivo during delayed response tasks.

The functional role of sequentially neuromodulated synaptic plasticity in behavioural learning

To survive, animals have to quickly modify their behaviour when the reward changes. The internal representations responsible for this are updated through synaptic weight changes, mediated by certain neuromodulators conveying feedback from the environment. In previous experiments, we discovered a form of hippocampal Spike-Timing-Dependent-Plasticity (STDP) that is sequentially modulated by acetylcholine and dopamine. Acetylcholine facilitates synaptic depression, while dopamine retroactively converts the depression into potentiation. When these experimental findings were implemented as a learning rule in a computational model, our simulations showed that cholinergic-facilitated depression is important for reversal learning. In the present study, we tested the model's prediction by optogenetically inactivating cholinergic neurons in mice during a hippocampus-dependent spatial learning task with changing rewards. We found that reversal learning, but not initial place learning, was impaired, verifying our computational prediction that acetylcholine-modulated plasticity promotes the unlearning of old reward locations. Further, differences in neuromodulator concentrations in the model captured mouse-by-mouse performance variability in the optogenetic experiments. Our line of work sheds light on how neuromodulators enable the learning of new contingencies.

Dopaminergic Neuromodulation of Spike Timing Dependent Plasticity in Mature Adult Rodent and Human Cortical Neurons

Synapses in the cerebral cortex constantly change and this dynamic property regulated by the action of neuromodulators such as dopamine (DA), is essential for reward learning and memory. DA modulates spike-timing-dependent plasticity (STDP), a cellular model of learning and memory, in juvenile rodent cortical neurons. However, it is unknown whether this neuromodulation also occurs at excitatory synapses of cortical neurons in mature adult mice or in humans. Cortical layer V pyramidal neurons were recorded with whole cell patch clamp electrophysiology and an extracellular stimulating electrode was used to induce STDP. DA was either bath-applied or optogenetically released in slices from mice. Classical STDP induction protocols triggered non-hebbian excitatory synaptic depression in the mouse or no plasticity at human cortical synapses. DA reverted long term synaptic depression to baseline in mouse via dopamine 2 type receptors or elicited long term synaptic potentiation in human cortical synapses. Furthermore, when DA was applied during an STDP protocol it depressed presynaptic inhibition in the mouse but not in the human cortex. Thus, DA modulates excitatory synaptic plasticity differently in human vs. mouse cortex. The data strengthens the importance of DA in gating cognition in humans, and may inform on therapeutic interventions to recover brain function from diseases.

Synaptic learning rules for sequence learning

Remembering the temporal order of a sequence of events is a task easily performed by humans in everyday life, but the underlying neuronal mechanisms are unclear. This problem is particularly intriguing as human behavior often proceeds on a time scale of seconds, which is in stark contrast to the much faster millisecond time-scale of neuronal processing in our brains. One long-held hypothesis in sequence learning suggests that a particular temporal fine-structure of neuronal activity - termed 'phase precession' - enables the compression of slow behavioral sequences down to the fast time scale of the induction of synaptic plasticity. Using mathematical analysis and computer simulations, we find that - for short enough synaptic learning windows - phase precession can improve temporal-order learning tremendously and that the asymmetric part of the synaptic learning window is essential for temporal-order learning. To test these predictions, we suggest experiments that selectively alter phase precession or the learning window and evaluate memory of temporal order.

Differential mapping spiking neural network for sensor-based robot control

In this work, a spiking neural network (SNN) is proposed for approximating differential sensorimotor maps of robotic systems. The computed model is used as a local Jacobian-like projection that relates changes in sensor space to changes in motor space. The SNN consists of an input (sensory) layer and an output (motor) layer connected through plastic synapses, with inter-inhibitory connections at the output layer. Spiking neurons are modeled as Izhikevich neurons with a synaptic learning rule based on spike timing-dependent plasticity. Feedback data from proprioceptive and exteroceptive sensors are encoded and fed into the input layer through a motor babbling process. A guideline for tuning the network parameters is proposed and applied along with the particle swarm optimization technique. Our proposed control architecture takes advantage of biologically plausible tools of an SNN to achieve the target reaching task while minimizing deviations from the desired path, and consequently minimizing the execution time. Thanks to the chosen architecture and optimization of the parameters, the number of neurons and the amount of data required for training are considerably low. The SNN is capable of handling noisy sensor readings to guide the robot movements in real-time. Experimental results are presented to validate the control methodology with a vision-guided robot.

Loss of mGluR1-LTD following cocaine exposure accumulates Ca2+-permeable AMPA receptors and facilitates synaptic potentiation in the prefrontal cortex

Addiction results from drug-elicited alterations of synaptic plasticity mechanisms in dopaminergic reward circuits. Impaired metabotropic glutamate receptor (mGluR)-dependent long-term depression (LTD) and accumulation of synaptic Ca²⁺-permeable AMPA receptors (CP-AMPARs) following drug exposure have emerged as important mechanisms underlying drug craving and relapse. Here we show that repeated cocaine exposure in vivo causes transient but complete loss of mGluR1- and mTOR (mammalian target of rapamycin)-dependent LTD in layer 5 pyramidal neurons of mouse prefrontal cortex (PFC), a major dopaminergic target in the reward circuitry. This mGluR1-LTD impairment was prevented by in vivo administration of an mGluR1 positive allosteric modulator (PAM) and rescued by inhibition of dopamine D1 receptors, suggesting that impaired mGluR1 tone and excessive D1 signaling underlie this LTD deficit. Concurrently, CP-AMPARs were generated, indicated by increased sensitivity to the CP-AMPAR inhibitor Naspm and rectification of synaptic AMPAR currents, which were reversed by PAM in cocaine-exposed mice. Finally, these CP-AMPARs mediate an abnormal spike-timing-dependent long-term potentiation enabled by cocaine exposure. Our findings reveal a mechanism by which cocaine impairs LTD and remodels synaptic AMPARs to influence Hebbian plasticity in the PFC. Failure to undergo LTD may prevent the reversal of drug-potentiated brain circuits to their baseline states, perpetuating addictive behaviors.HIGHLIGHTSA mGluR1- and mTOR-dependent LTD is present in the mouse medial prefrontal cortex.Repeated cocaine exposure in vivo temporally but completely abolishes prefrontal mGluR1-LTD.Impaired mGluR1 function and excessive D1 DA signaling likely underlie cocaine impairment of mGluR1-LTD.Ca²⁺-permeable AMPA receptors are generated by cocaine exposure, likely resulting from mGluR1-LTD impairment, and contribute to a cocaine-induced extended spike timing LTP.

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.

Time as the fourth dimension in the hippocampus

Experiences of animal and human beings are structured by the continuity of space and time coupled with the uni-directionality of time. In addition to its pivotal position in spatial processing and navigation, the hippocampal system also plays a central, multiform role in several types of temporal processing. These include timing and sequence learning, at scales ranging from meso-scales of seconds to macro-scales of minutes, hours, days and beyond, encompassing the classical functions of short term memory, working memory, long term memory, and episodic memories (comprised of information about when, what, and where). This review article highlights the principal findings and behavioral contexts of experiments in rats showing: 1) timing: tracking time during delays by hippocampal 'time cells' and during free behavior by hippocampal-afferent lateral entorhinal cortex ramping cells; 2) 'online' sequence processing: activity coding sequences of events during active behavior; 3) 'off-line' sequence replay: during quiescence or sleep, orderly reactivation of neuronal assemblies coding awake sequences. Studies in humans show neurophysiological correlates of episodic memory comparable to awake replay. Neural mechanisms are discussed, including ion channel properties, plateau and ramping potentials, oscillations of excitation and inhibition of population activity, bursts of high amplitude discharges (sharp wave ripples), as well as short and long term synaptic modifications among and within cell assemblies. Specifically conceived neural network models will suggest processes supporting the emergence of scalar properties (Weber's law), and include different classes of feedforward and recurrent network models, with intrinsic hippocampal coding for 'transitions' (sequencing of events or places).

Distinct effects of reward and navigation history on hippocampal forward and reverse replays

To better understand the functional roles of hippocampal forward and reverse replays, we trained rats in a spatial sequence memory task and examined how these replays are modulated by reward and navigation history. We found that reward enhances both forward and reverse replays during the awake state, but in different ways. Reward enhances the rate of reverse replays, but it increases the fidelity of forward replays for recently traveled as well as other alternative trajectories heading toward a rewarding location. This suggests roles for forward and reverse replays in reinforcing representations for all potential rewarding trajectories. We also found more faithful reactivation of upcoming than already rewarded trajectories in forward replays. This suggests a role for forward replays in preferentially reinforcing representations for high-value trajectories. We propose that hippocampal forward and reverse replays might contribute to constructing a map of potential navigation trajectories and their associated values (a "value map") via distinct mechanisms.

Reinforcement Learning in Spiking Neural Networks with Stochastic and Deterministic Synapses

Though succeeding in solving various learning tasks, most existing reinforcement learning (RL) models have failed to take into account the complexity of synaptic plasticity in the neural system. Models implementing reinforcement learning with spiking neurons involve only a single plasticity mechanism. Here, we propose a neural realistic reinforcement learning model that coordinates the plasticities of two types of synapses: stochastic and deterministic. The plasticity of the stochastic synapse is achieved by the hedonistic rule through modulating the release probability of synaptic neurotransmitter, while the plasticity of the deterministic synapse is achieved by a variant of a reward-modulated spike-timing-dependent plasticity rule through modulating the synaptic strengths. We evaluate the proposed learning model on two benchmark tasks: learning a logic gate function and the 19-state random walk problem. Experimental results show that the coordination of diverse synaptic plasticities can make the RL model learn in a rapid and stable form.

Bayesian Filtering with Multiple Internal Models: Toward a Theory of Social Intelligence

To exhibit social intelligence, animals have to recognize whom they are communicating with. One way to make this inference is to select among internal generative models of each conspecific who may be encountered. However, these models also have to be learned via some form of Bayesian belief updating. This induces an interesting problem: When receiving sensory input generated by a particular conspecific, how does an animal know which internal model to update? We consider a theoretical and neurobiologically plausible solution that enables inference and learning of the processes that generate sensory inputs (e.g., listening and understanding) and reproduction of those inputs (e.g., talking or singing), under multiple generative models. This is based on recent advances in theoretical neurobiology-namely, active inference and post hoc (online) Bayesian model selection. In brief, this scheme fits sensory inputs under each generative model. Model parameters are then updated in proportion to the probability that each model could have generated the input (i.e., model evidence). The proposed scheme is demonstrated using a series of (real zebra finch) birdsongs, where each song is generated by several different birds. The scheme is implemented using physiologically plausible models of birdsong production. We show that generalized Bayesian filtering, combined with model selection, leads to successful learning across generative models, each possessing different parameters. These results highlight the utility of having multiple internal models when making inferences in social environments with multiple sources of sensory information.

A biologically plausible supervised learning method for spiking neural networks using the symmetric STDP rule

Spiking neural networks (SNNs) possess energy-efficient potential due to event-based computation. However, supervised training of SNNs remains a challenge as spike activities are non-differentiable. Previous SNNs training methods can be generally categorized into two basic classes, i.e., backpropagation-like training methods and plasticity-based learning methods. The former methods are dependent on energy-inefficient real-valued computation and non-local transmission, as also required in artificial neural networks (ANNs), whereas the latter are either considered to be biologically implausible or exhibit poor performance. Hence, biologically plausible (bio-plausible) high-performance supervised learning (SL) methods for SNNs remain deficient. In this paper, we proposed a novel bio-plausible SNN model for SL based on the symmetric spike-timing dependent plasticity (sym-STDP) rule found in neuroscience. By combining the sym-STDP rule with bio-plausible synaptic scaling and intrinsic plasticity of the dynamic threshold, our SNN model implemented SL well and achieved good performance in the benchmark recognition task (MNIST dataset). To reveal the underlying mechanism of our SL model, we visualized both layer-based activities and synaptic weights using the t-distributed stochastic neighbor embedding (t-SNE) method after training and found that they were well clustered, thereby demonstrating excellent classification ability. Furthermore, to verify the robustness of our model, we trained it on another more realistic dataset (Fashion-MNIST), which also showed good performance. As the learning rules were bio-plausible and based purely on local spike events, our model could be easily applied to neuromorphic hardware for online training and may be helpful for understanding SL information processing at the synaptic level in biological neural systems.

Neonatal Injury Alters Sensory Input and Synaptic Plasticity in GABAergic Interneurons of the Adult Mouse Dorsal Horn

Neonatal tissue injury disrupts the balance between primary afferent-evoked excitation and inhibition onto adult spinal projection neurons. However, whether this reflects cell-type-specific alterations at synapses onto ascending projection neurons, or rather is indicative of global changes in synaptic signaling across the mature superficial dorsal horn (SDH), remains unknown. Therefore the present study investigated the effects of neonatal surgical injury on primary afferent synaptic input to adult mouse SDH interneurons using in vitro patch-clamp techniques. Hindpaw incision at postnatal day (P)3 significantly diminished total primary afferent-evoked glutamatergic drive to adult Gad67-GFP and non-GFP neurons, and reduced their firing in response to sensory input, in both males and females. Early tissue damage also shaped the relative prevalence of monosynaptic A- versus C-fiber-mediated input to mature GABAergic neurons, with an increased prevalence of Aβ- and Aδ-fiber input observed in neonatally-incised mice compared with naive littermate controls. Paired presynaptic and postsynaptic stimulation at an interval that exclusively produced spike timing-dependent long-term potentiation (t-LTP) in projection neurons predominantly evoked NMDAR-dependent long-term depression in naive Gad67-GFP interneurons. Meanwhile, P3 tissue damage enhanced the likelihood of t-LTP generation at sensory synapses onto the mature GABAergic population, and increased the contribution of Ca²⁺-permeable AMPARs to the overall glutamatergic response. Collectively, the results indicate that neonatal injury suppresses sensory drive to multiple subpopulations of interneurons in the adult SDH, which likely represents one mechanism contributing to reduced feedforward inhibition of ascending projection neurons, and the priming of developing pain pathways, following early life trauma.SIGNIFICANCE STATEMENT Mounting clinical and preclinical evidence suggests that neonatal tissue damage can result in long-term changes in nociceptive processing within the CNS. Although recent work has demonstrated that early life injury weakens the ability of sensory afferents to evoke feedforward inhibition of adult spinal projection neurons, the underlying circuit mechanisms remain poorly understood. Here we demonstrate that neonatal surgical injury leads to persistent deficits in primary afferent drive to both GABAergic and presumed glutamatergic neurons in the mature superficial dorsal horn (SDH), and modifies activity-dependent plasticity at sensory synapses onto the GABAergic population. The functional denervation of spinal interneurons within the mature SDH may contribute to the "priming" of developing pain pathways following early life injury.

Rewards interact with repetition-dependent learning to enhance long-term retention of motor memories

The combination of behavioral experiences that enhance long-term retention remains largely unknown. Informed by neurophysiological lines of work, this study tested the hypothesis that performance-contingent monetary rewards potentiate repetition-dependent forms of learning, as induced by extensive practice at asymptote, to enhance long-term retention of motor memories. To this end, six groups of 14 participants (n = 84) acquired novel motor behaviors by adapting to a gradual visuomotor rotation while these factors were manipulated. Retention was assessed 24 h later. While all groups similarly acquired the novel motor behaviors, results from the retention session revealed an interaction indicating that rewards enhanced long-term retention, but only when practice was extended to asymptote. Specifically, the interaction indicated that this effect selectively occurred when rewards were intermittently available (i.e., 50%), but not when they were absent (i.e., 0%) or continuously available (i.e., 100%) during acquisition. This suggests that the influence of rewards on extensive practice and long-term retention is nonlinear, as continuous rewards did not further enhance retention as compared with intermittent rewards. One possibility is that rewards' intermittent availability allows to maintain their subjective value during acquisition, which may be key to potentiate long-term retention.

Multi-context blind source separation by error-gated Hebbian rule

Animals need to adjust their inferences according to the context they are in. This is required for the multi-context blind source separation (BSS) task, where an agent needs to infer hidden sources from their context-dependent mixtures. The agent is expected to invert this mixing process for all contexts. Here, we show that a neural network that implements the error-gated Hebbian rule (EGHR) with sufficiently redundant sensory inputs can successfully learn this task. After training, the network can perform the multi-context BSS without further updating synapses, by retaining memories of all experienced contexts. This demonstrates an attractive use of the EGHR for dimensionality reduction by extracting low-dimensional sources across contexts. Finally, if there is a common feature shared across contexts, the EGHR can extract it and generalize the task to even inexperienced contexts. The results highlight the utility of the EGHR as a model for perceptual adaptation in animals.

First-Spike-Based Visual Categorization Using Reward-Modulated STDP

Reinforcement learning (RL) has recently regained popularity with major achievements such as beating the European game of Go champion. Here, for the first time, we show that RL can be used efficiently to train a spiking neural network (SNN) to perform object recognition in natural images without using an external classifier. We used a feedforward convolutional SNN and a temporal coding scheme where the most strongly activated neurons fire first, while less activated ones fire later, or not at all. In the highest layers, each neuron was assigned to an object category, and it was assumed that the stimulus category was the category of the first neuron to fire. If this assumption was correct, the neuron was rewarded, i.e., spike-timing-dependent plasticity (STDP) was applied, which reinforced the neuron's selectivity. Otherwise, anti-STDP was applied, which encouraged the neuron to learn something else. As demonstrated on various image data sets (Caltech, ETH-80, and NORB), this reward-modulated STDP (R-STDP) approach has extracted particularly discriminative visual features, whereas classic unsupervised STDP extracts any feature that consistently repeats. As a result, R-STDP has outperformed STDP on these data sets. Furthermore, R-STDP is suitable for online learning and can adapt to drastic changes such as label permutations. Finally, it is worth mentioning that both feature extraction and classification were done with spikes, using at most one spike per neuron. Thus, the network is hardware friendly and energy efficient.

Dopamine Receptor Activation Is Required for GABAergic Spike Timing-Dependent Plasticity in Response to Complex Spike Pairing in the Ventral Tegmental Area

One of the most influential synaptic learning rules explored in the past decades is activity dependent spike-timing-dependent plasticity (STDP). In STDP, synapses are either potentiated or depressed based on the order of pre- and postsynaptic neuronal activation within narrow, milliseconds-long, time intervals. STDP is subject to neuromodulation by dopamine (DA), a potent neurotransmitter that significantly impacts synaptic plasticity and reward-related behavioral learning. Previously, we demonstrated that GABAergic synapses onto ventral tegmental area (VTA) DA neurons are able to express STDP (Kodangattil et al., 2013), however it is still unclear whether DA modulates inhibitory STDP in the VTA. Here, we used whole-cell recordings in rat midbrain slices to investigate whether DA D1-like and/or D2-like receptor (D1R/D2R) activation is required for induction of STDP in response to a complex pattern of spiking. We found that VTA but not Substantia nigra pars compact (SNc) DA neurons exhibit long-term depression (LTD_GABA) in response to a combination of positive (pre-post) and negative (post-pre) timing of spiking (a complex STDP protocol). Blockade of either D1Rs or D2Rs prevented the induction of LTD_GABA while activation of D1Rs did not affect the plasticity in response to this complex STDP protocol in VTA DA neurons.Our data suggest that this DA-dependent GABAergic STDP is selectively expressed at GABAergic synapses onto VTA DA neurons which could be targeted by drugs of abuse to mediate drug-induced modulation of DA signaling within the VTA, as well as in VTA-projection areas, thereby affecting reward-related learning and drug-associated memories.

Neuromodulation of hippocampal long-term synaptic plasticity

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Acetylcholine, noradrenaline, dopamine and serotonin all facilitate long-term synaptic plasticity.

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Neuromodulators facilitate long-term synaptic plasticity by common and divergent mechanisms.

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Common mechanisms include NMDA receptor facilitation by potassium channel inhibition, gliotransmission and disinhibition.

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Divergent mechanisms include diversity of disinhibition and temporal and spatial neuromodulator release.

Multiple neuromodulators including acetylcholine, noradrenaline, dopamine and serotonin are released in response to uncertainty to focus attention on events where the predicted outcome does not match observed reality. In these situations, internal representations need to be updated, a process that requires long-term synaptic plasticity. Through a variety of common and divergent mechanisms, it is recently shown that all these neuromodulators facilitate the induction and/or expression of long-term synaptic plasticity within the hippocampus. Under physiological conditions, this may be critical for suprathreshold induction of plasticity endowing neuromodulators with a gating function and providing a mechanism by which neuromodulators enable the targeted updating of memory with relevant information to improve the accuracy of future predictions.

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.

The brain’s capacity to treat information, learn and store memory relies on synaptic connectivity patterns, which are altered through synaptic plasticity mechanisms. Experimentally, such plasticities were evidenced through protocols involving numerous repetitive stimulations of a given synapse, and were shown to be supported by multiple pathways. Using a simple biologically grounded mathematical model, we show how activation timescales and inactivation levels of each pathway interact and alter plasticity in an intricate manner as stimuli are presented. Building upon data from the synapse between cortex and striatum, we show that synaptic changes may revert or re-emerge as stimuli are presented, and predict specific responses to changes in stimulus frequency or to distinct simulation patterns. Our general model shows that a given plasticity profile emerging in response to a repetitive stimulation protocol can unfold into various scenarii upon variations of the number of stimulus presentations or patterns, which tightly depends on the underlying activated pathways. Altogether, these results argue that in order to better understand learning and memory, single plasticity responses obtained through intensive stimulations do not reveal the complexity of the responses for smaller number of presentations, which may have a strong impact in fast learning of stimuli with low numbers of presentations.

Eligibility Traces and Plasticity on Behavioral Time Scales: Experimental Support of NeoHebbian Three-Factor Learning Rules

Most elementary behaviors such as moving the arm to grasp an object or walking into the next room to explore a museum evolve on the time scale of seconds; in contrast, neuronal action potentials occur on the time scale of a few milliseconds. Learning rules of the brain must therefore bridge the gap between these two different time scales. Modern theories of synaptic plasticity have postulated that the co-activation of pre- and postsynaptic neurons sets a flag at the synapse, called an eligibility trace, that leads to a weight change only if an additional factor is present while the flag is set. This third factor, signaling reward, punishment, surprise, or novelty, could be implemented by the phasic activity of neuromodulators or specific neuronal inputs signaling special events. While the theoretical framework has been developed over the last decades, experimental evidence in support of eligibility traces on the time scale of seconds has been collected only during the last few years. Here we review, in the context of three-factor rules of synaptic plasticity, four key experiments that support the role of synaptic eligibility traces in combination with a third factor as a biological implementation of neoHebbian three-factor learning rules.

Beyond STDP-towards diverse and functionally relevant plasticity rules

Synaptic plasticity, induced by the close temporal association of two neural signals, supports associative forms of learning. However, the millisecond timescales for association often do not match the much longer delays for behaviorally relevant signals that supervise learning. In particular, information about the behavioral outcome of neural activity can be delayed, leading to a problem of temporal credit assignment. Recent studies suggest that synaptic plasticity can have temporal rules that not only accommodate the delays relevant to the circuit, but also be precisely tuned to the behavior the circuit supports. These discoveries highlight the diversity of plasticity rules, whose temporal requirements may depend on circuit delays and the contingencies of behavior.

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.