Hebbian Synaptic Plasticity Research Articles

Synaptopodin: a key regulator of Hebbian plasticity.

Synaptopodin, an actin-associated protein found in a subset of dendritic spines in telencephalic neurons, has been described to influence both functional and morphological plasticity under various plasticity paradigms. Synaptopodin is necessary and sufficient for the formation of the spine apparatus, stacks of smooth endoplasmic reticulum cisternae. The spine apparatus is a calcium store that locally regulates calcium dynamics in response to different patterns of activity and is also thought to be a site for local protein synthesis. Synaptopodin is present in ~30% of telencephalic large dendritic spines in vivo and in vitro highlighting the heterogeneous microanatomy and molecular architecture of dendritic spines, an important but not well understood aspect of neuroplasticity. In recent years, it has become increasingly clear that synaptopodin is a formidable regulator of multiple mechanisms essential for learning and memory. In fact, synaptopodin appears to be the decisive factor that determines whether plasticity can occur, acting as a key regulator for synaptic changes. In this review, we summarize the current understanding of synaptopodin's role in various forms of Hebbian synaptic plasticity.

Sensory experience steers representational drift in mouse visual cortex

Representational drift—the gradual continuous change of neuronal representations—has been observed across many brain areas. It is unclear whether drift is caused by synaptic plasticity elicited by sensory experience, or by the intrinsic volatility of synapses. Here, using chronic two-photon calcium imaging in primary visual cortex of female mice, we find that the preferred stimulus orientation of individual neurons slowly drifts over the course of weeks. By using cylinder lens goggles to limit visual experience to a narrow range of orientations, we show that the direction of drift, but not its magnitude, is biased by the statistics of visual input. A network model suggests that drift of preferred orientation largely results from synaptic volatility, which under normal visual conditions is counteracted by experience-driven Hebbian mechanisms, stabilizing preferred orientation. Under deprivation conditions these Hebbian mechanisms enable adaptation. Thus, Hebbian synaptic plasticity steers drift to match the statistics of the environment.

Spiking representation learning for associative memories.

Networks of interconnected neurons communicating through spiking signals offer the bedrock of neural computations. Our brain's spiking neural networks have the computational capacity to achieve complex pattern recognition and cognitive functions effortlessly. However, solving real-world problems with artificial spiking neural networks (SNNs) has proved to be difficult for a variety of reasons. Crucially, scaling SNNs to large networks and processing large-scale real-world datasets have been challenging, especially when compared to their non-spiking deep learning counterparts. The critical operation that is needed of SNNs is the ability to learn distributed representations from data and use these representations for perceptual, cognitive and memory operations. In this work, we introduce a novel SNN that performs unsupervised representation learning and associative memory operations leveraging Hebbian synaptic and activity-dependent structural plasticity coupled with neuron-units modelled as Poisson spike generators with sparse firing (~1 Hz mean and ~100 Hz maximum firing rate). Crucially, the architecture of our model derives from the neocortical columnar organization and combines feedforward projections for learning hidden representations and recurrent projections for forming associative memories. We evaluated the model on properties relevant for attractor-based associative memories such as pattern completion, perceptual rivalry, distortion resistance, and prototype extraction.

The multi-stage plasticity in the aggression circuit underlying the winner effect.

Winning increases the readiness to attack and the probability of winning, a widespread phenomenon known as the "winner effect". Here, we reveal a transition from target-specific to generalized aggression enhancement over 10 days of winning in male mice, which is supported by three stages of plasticity in the ventrolateral part of the ventromedial hypothalamus (VMHvl), a critical node for aggression. Over 10-day winning, VMHvl cells experience monotonic potentiation of long-range excitatory inputs, a transient local connectivity strengthening, and a delayed excitability increase. These plasticity events are causally linked. Optogenetically coactivating the posterior amygdala (PA) terminals and VMHvl cells potentiates the PA-VMHvl pathway and triggers the cascade of plasticity events as those during repeated winning. Optogenetically blocking PA-VMHvl synaptic potentiation eliminates all winning-induced plasticity. These results reveal the complex Hebbian synaptic and excitability plasticity in the aggression circuit during winning that ultimately leads to an increase in "aggressiveness" in repeated winners.

Promiscuous involvement of metabotropic glutamate receptors in the storage of N-methyl-d-aspartate receptor-dependent short-term potentiation.

Short- and long-term forms of N-methyl-d-aspartate receptor (NMDAR)-dependent potentiation (most commonly termed short-term potentiation (STP) and long-term potentiation (LTP)) are co-induced in hippocampal slices by theta-burst stimulation, which mimics naturally occurring patterns of neuronal activity. While NMDAR-dependent LTP (NMDAR-LTP) is said to be the cellular correlate of long-term memory storage, NMDAR-dependent STP (NMDAR-STP) is thought to underlie the encoding of shorter-lasting memories. The mechanisms of NMDAR-LTP have been researched much more extensively than those of NMDAR-STP, which is characterized by its extreme stimulation dependence. Thus, in the absence of low-frequency test stimulation, which is used to test the magnitude of potentiation, NMDAR-STP does not decline until the stimulation is resumed. NMDAR-STP represents, therefore, an inverse variant of Hebbian synaptic plasticity, illustrating that inactive synapses can retain their strength unchanged until they become active again. The mechanisms, by which NMDAR-STP is stored in synapses without a decrement, are unknown and we report here that activation of metabotropic glutamate receptors may be critical in maintaining the potentiated state of synaptic transmission. This article is part of a discussion meeting issue 'Long-term potentiation: 50 years on'.

Forgetting Leads to Chaos in Attractor Networks

Attractor networks are an influential theory for memory storage in brain systems. This theory has recently been challenged by the observation of strong temporal variability in neuronal recordings during memory tasks. In this work, we study a sparsely connected attractor network where memories are learned according to a Hebbian synaptic plasticity rule. After recapitulating known results for the continuous, sparsely connected Hopfield model, we investigate a model in which new memories are learned continuously and old memories are forgotten, using an online synaptic plasticity rule. We show that for a forgetting timescale that optimizes storage capacity, the qualitative features of the network’s memory retrieval dynamics are age dependent: most recent memories are retrieved as fixed-point attractors while older memories are retrieved as chaotic attractors characterized by strong heterogeneity and temporal fluctuations. Therefore, fixed-point and chaotic attractors coexist in the network phase space. The network presents a continuum of statistically distinguishable memory states, where chaotic fluctuations appear abruptly above a critical age and then increase gradually until the memory disappears. We develop a dynamical mean field theory to analyze the age-dependent dynamics and compare the theory with simulations of large networks. We compute the optimal forgetting timescale for which the number of stored memories is maximized. We found that the maximum age at which memories can be retrieved is given by an instability at which old memories destabilize and the network converges instead to a more recent one. Our numerical simulations show that a high degree of sparsity is necessary for the dynamical mean field theory to accurately predict the network capacity. To test the robustness and biological plausibility of our results, we study numerically the dynamics of a network with learning rules and transfer function inferred from in vivo data in the online learning scenario. We found that all aspects of the network’s dynamics characterized analytically in the simpler model also hold in this model. These results are highly robust to noise. Finally, our theory provides specific predictions for delay response tasks with aging memoranda. In particular, it predicts a higher degree of temporal fluctuations in retrieval states associated with older memories, and it also predicts fluctuations should be faster in older memories. Overall, our theory of attractor networks that continuously learn new information at the price of forgetting old memories can account for the observed diversity of retrieval states in the cortex, and in particular, the strong temporal fluctuations of cortical activity.1 MoreReceived 3 December 2021Revised 31 August 2022Accepted 6 December 2022DOI:https://doi.org/10.1103/PhysRevX.13.011009Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article’s title, journal citation, and DOI.Published by the American Physical SocietyPhysics Subject Headings (PhySH)Research AreasLearningMemoryNeuronal networksPhysical SystemsArtificial neural networksTechniquesDynamical mean field theoryInterdisciplinary PhysicsNetworksStatistical PhysicsNonlinear DynamicsBiological Physics

Memory-Dependent Computation and Learning in Spiking Neural Networks Through Hebbian Plasticity.

Spiking neural networks (SNNs) are the basis for many energy-efficient neuromorphic hardware systems. While there has been substantial progress in SNN research, artificial SNNs still lack many capabilities of their biological counterparts. In biological neural systems, memory is a key component that enables the retention of information over a huge range of temporal scales, ranging from hundreds of milliseconds up to years. While Hebbian plasticity is believed to play a pivotal role in biological memory, it has so far been analyzed mostly in the context of pattern completion and unsupervised learning in artificial and SNNs. Here, we propose that Hebbian plasticity is fundamental for computations in biological and artificial spiking neural systems. We introduce a novel memory-augmented SNN architecture that is enriched by Hebbian synaptic plasticity. We show that Hebbian enrichment renders SNNs surprisingly versatile in terms of their computational as well as learning capabilities. It improves their abilities for out-of-distribution generalization, one-shot learning, cross-modal generative association, language processing, and reward-based learning. This suggests that powerful cognitive neuromorphic systems can be built based on this principle.

Stability and learning in excitatory synapses by nonlinear inhibitory plasticity.

Synaptic changes are hypothesized to underlie learning and memory formation in the brain. But Hebbian synaptic plasticity of excitatory synapses on its own is unstable, leading to either unlimited growth of synaptic strengths or silencing of neuronal activity without additional homeostatic mechanisms. To control excitatory synaptic strengths, we propose a novel form of synaptic plasticity at inhibitory synapses. Using computational modeling, we suggest two key features of inhibitory plasticity, dominance of inhibition over excitation and a nonlinear dependence on the firing rate of postsynaptic excitatory neurons whereby inhibitory synaptic strengths change with the same sign (potentiate or depress) as excitatory synaptic strengths. We demonstrate that the stable synaptic strengths realized by this novel inhibitory plasticity model affects excitatory/inhibitory weight ratios in agreement with experimental results. Applying a disinhibitory signal can gate plasticity and lead to the generation of receptive fields and strong bidirectional connectivity in a recurrent network. Hence, a novel form of nonlinear inhibitory plasticity can simultaneously stabilize excitatory synaptic strengths and enable learning upon disinhibition.

Towards New Generation, Biologically Plausible Deep Neural Network Learning

Artificial neural networks in their various different forms convincingly dominate machine learning of the present day. Nevertheless, the manner in which these networks are trained, in particular by using end-to-end backpropagation, presents a major limitation in practice and hampers research, and raises questions with regard to the very fundamentals of the learning algorithm design. Motivated by these challenges and the contrast between the phenomenology of biological (natural) neural networks that artificial ones are inspired by and the learning processes underlying the former, there has been an increasing amount of research on the design of biologically plausible means of training artificial neural networks. In this paper we (i) describe a biologically plausible learning method that takes advantage of various biological processes, such as Hebbian synaptic plasticity, and includes both supervised and unsupervised elements, (ii) conduct a series of experiments aimed at elucidating the advantages and disadvantages of the described biologically plausible learning as compared with end-to-end backpropagation, and (iii) discuss the findings which should serve as a means of illuminating the algorithmic fundamentals of interest and directing future research. Among our findings is the greater resilience of biologically plausible learning to data scarcity, which conforms to our expectations, but also its lesser robustness to additive, zero mean Gaussian noise.

Selective connectivity enhances storage capacity in attractor models of memory function.

Autoassociative neural networks provide a simple model of how memories can be stored through Hebbian synaptic plasticity as retrievable patterns of neural activity. Although progress has been made along the last decades in understanding the biological implementation of autoassociative networks, their modest theoretical storage capacity has remained a major constraint. While most previous approaches utilize randomly connected networks, here we explore the possibility of optimizing network performance by selective connectivity between neurons, that could be implemented in the brain through creation and pruning of synaptic connections. We show through numerical simulations that a reconfiguration of the connectivity matrix can improve the storage capacity of autoassociative networks up to one order of magnitude compared to randomly connected networks, either by reducing the noise or by making it reinforce the signal. Our results indicate that the signal-reinforcement scenario is not only the best performing but also the most adequate for brain-like highly diluted connectivity. In this scenario, the optimized network tends to select synapses characterized by a high consensus across stored patterns. We also introduced an online algorithm in which the network modifies its connectivity while learning new patterns. We observed that, similarly to what happens in the human brain, creation of connections dominated in an initial stage, followed by a stage characterized by pruning, leading to an equilibrium state that was independent of the initial connectivity of the network. Our results suggest that selective connectivity could be a key component to make attractor networks in the brain viable in terms of storage capacity.

Traces of semantization - from episodic to semantic memory in a spiking cortical network model.

Episodic memory is a recollection of past personal experiences associated with particular times and places. This kind of memory is commonly subject to loss of contextual information or “semantization,” which gradually decouples the encoded memory items from their associated contexts while transforming them into semantic or gist-like representations. Novel extensions to the classical Remember/Know (R/K) behavioral paradigm attribute the loss of episodicity to multiple exposures of an item in different contexts. Despite recent advancements explaining semantization at a behavioral level, the underlying neural mechanisms remain poorly understood. In this study, we suggest and evaluate a novel hypothesis proposing that Bayesian–Hebbian synaptic plasticity mechanisms might cause semantization of episodic memory. We implement a cortical spiking neural network model with a Bayesian–Hebbian learning rule called Bayesian Confidence Propagation Neural Network (BCPNN), which captures the semantization phenomenon and offers a mechanistic explanation for it. Encoding items across multiple contexts leads to item-context decoupling akin to semantization. We compare BCPNN plasticity with the more commonly used spike-timing-dependent plasticity (STDP) learning rule in the same episodic memory task. Unlike BCPNN, STDP does not explain the decontextualization process. We further examine how selective plasticity modulation of isolated salient events may enhance preferential retention and resistance to semantization. Our model reproduces important features of episodicity on behavioral timescales under various biological constraints while also offering a novel neural and synaptic explanation for semantization, thereby casting new light on the interplay between episodic and semantic memory processes.

Sequence learning, prediction, and replay in networks of spiking neurons

Sequence learning, prediction and replay have been proposed to constitute the universal computations performed by the neocortex. The Hierarchical Temporal Memory (HTM) algorithm realizes these forms of computation. It learns sequences in an unsupervised and continuous manner using local learning rules, permits a context specific prediction of future sequence elements, and generates mismatch signals in case the predictions are not met. While the HTM algorithm accounts for a number of biological features such as topographic receptive fields, nonlinear dendritic processing, and sparse connectivity, it is based on abstract discrete-time neuron and synapse dynamics, as well as on plasticity mechanisms that can only partly be related to known biological mechanisms. Here, we devise a continuous-time implementation of the temporal-memory (TM) component of the HTM algorithm, which is based on a recurrent network of spiking neurons with biophysically interpretable variables and parameters. The model learns high-order sequences by means of a structural Hebbian synaptic plasticity mechanism supplemented with a rate-based homeostatic control. In combination with nonlinear dendritic input integration and local inhibitory feedback, this type of plasticity leads to the dynamic self-organization of narrow sequence-specific subnetworks. These subnetworks provide the substrate for a faithful propagation of sparse, synchronous activity, and, thereby, for a robust, context specific prediction of future sequence elements as well as for the autonomous replay of previously learned sequences. By strengthening the link to biology, our implementation facilitates the evaluation of the TM hypothesis based on experimentally accessible quantities. The continuous-time implementation of the TM algorithm permits, in particular, an investigation of the role of sequence timing for sequence learning, prediction and replay. We demonstrate this aspect by studying the effect of the sequence speed on the sequence learning performance and on the speed of autonomous sequence replay.

Hebbian plasticity: the elusive missing link at the heart of Alzheimer's disease pathogenesis?

Hebbian plasticity: the elusive missing link at the heart of Alzheimer's disease pathogenesis?

Postsynaptic Potential Energy as Determinant of Synaptic Plasticity.

Metabolic energy can be used as a unifying principle to control neuronal activity. However, whether and how metabolic energy alone can determine the outcome of synaptic plasticity remains unclear. This study proposes a computational model of synaptic plasticity that is completely determined by energy. A simple quantitative relationship between synaptic plasticity and postsynaptic potential energy is established. Synaptic weight is directly proportional to the difference between the baseline potential energy and the suprathreshold potential energy and is constrained by the maximum energy supply. Results show that the energy constraint improves the performance of synaptic plasticity and avoids setting the hard boundary of synaptic weights. With the same set of model parameters, our model can reproduce several classical experiments in homo- and heterosynaptic plasticity. The proposed model can explain the interaction mechanism of Hebbian and homeostatic plasticity at the cellular level. Homeostatic synaptic plasticity at different time scales coexists. Homeostatic plasticity operating on a long time scale is caused by heterosynaptic plasticity and, on the same time scale as Hebbian synaptic plasticity, is caused by the constraint of energy supply.

Stimulus-Selective Response Plasticity in Primary Visual Cortex: Progress and Puzzles.

Stimulus-selective response plasticity (SRP) is a robust and lasting modification of primary visual cortex (V1) that occurs in response to exposure to novel visual stimuli. It is readily observed as a pronounced increase in the magnitude of visual evoked potentials (VEPs) recorded in response to phase-reversing grating stimuli in neocortical layer 4. The expression of SRP at the individual neuron level is equally robust, but the qualities vary depending on the neuronal type and how activity is measured. This form of plasticity is highly selective for stimulus features such as stimulus orientation, spatial frequency, and contrast. Several key insights into the significance and underlying mechanisms of SRP have recently been made. First, it occurs concomitantly and shares core mechanisms with behavioral habituation, indicating that SRP reflects the formation of long-term familiarity that can support recognition of innocuous stimuli. Second, SRP does not manifest within a recording session but only emerges after an off-line period of several hours that includes sleep. Third, SRP requires not only canonical molecular mechanisms of Hebbian synaptic plasticity within V1, but also the opposing engagement of two key subclasses of cortical inhibitory neuron: the parvalbumin- and somatostatin-expressing GABAergic interneurons. Fourth, pronounced shifts in the power of cortical oscillations from high frequency (gamma) to low frequency (alpha/beta) oscillations provide respective readouts of the engagement of these inhibitory neuronal subtypes following familiarization. In this article we will discuss the implications of these findings and the outstanding questions that remain to gain a deeper understanding of this striking form of experience-dependent plasticity.

Cell-type–specific neuromodulation guides synaptic credit assignment in a spiking neural network

Brains learn tasks via experience-driven differential adjustment of their myriad individual synaptic connections, but the mechanisms that target appropriate adjustment to particular connections remain deeply enigmatic. While Hebbian synaptic plasticity, synaptic eligibility traces, and top-down feedback signals surely contribute to solving this synaptic credit-assignment problem, alone, they appear to be insufficient. Inspired by new genetic perspectives on neuronal signaling architectures, here, we present a normative theory for synaptic learning, where we predict that neurons communicate their contribution to the learning outcome to nearby neurons via cell-type-specific local neuromodulation. Computational tests suggest that neuron-type diversity and neuron-type-specific local neuromodulation may be critical pieces of the biological credit-assignment puzzle. They also suggest algorithms for improved artificial neural network learning efficiency.

How connectivity rules and synaptic properties shape the efficacy of pattern separation in the entorhinal cortex-dentate gyrus-CA3 network.

Pattern separation is a fundamental brain computation that converts small differences in input patterns into large differences in output patterns. Several synaptic mechanisms of pattern separation have been proposed, including code expansion, inhibition and plasticity; however, which of these mechanisms play a role in the entorhinal cortex (EC)-dentate gyrus (DG)-CA3 circuit, a classical pattern separation circuit, remains unclear. Here we show that a biologically realistic, full-scale EC-DG-CA3 circuit model, including granule cells (GCs) and parvalbumin-positive inhibitory interneurons (PV+-INs) in the DG, is an efficient pattern separator. Both external gamma-modulated inhibition and internal lateral inhibition mediated by PV+-INs substantially contributed to pattern separation. Both local connectivity and fast signaling at GC-PV+-IN synapses were important for maximum effectiveness. Similarly, mossy fiber synapses with conditional detonator properties contributed to pattern separation. By contrast, perforant path synapses with Hebbian synaptic plasticity and direct EC-CA3 connection shifted the network towards pattern completion. Our results demonstrate that the specific properties of cells and synapses optimize higher-order computations in biological networks and might be useful to improve the deep learning capabilities of technical networks.

A Multisubcellular Compartment Model of AMPA Receptor Trafficking for Neuromodulation of Hebbian Synaptic Plasticity

Neuromodulation can profoundly impact the gain and polarity of postsynaptic changes in Hebbian synaptic plasticity. An emerging pattern observed in multiple central synapses is a pull–push type of control in which activation of receptors coupled to the G-protein Gs promote long-term potentiation (LTP) at the expense of long-term depression (LTD), whereas receptors coupled to Gq promote LTD at the expense of LTP. Notably, coactivation of both Gs- and Gq-coupled receptors enhances the gain of both LTP and LTD. To account for these observations, we propose a simple kinetic model in which AMPA receptors (AMPARs) are trafficked between multiple subcompartments in and around the postsynaptic spine. In the model AMPARs in the postsynaptic density compartment (PSD) are the primary contributors to synaptic conductance. During LTP induction, AMPARs are trafficked to the PSD primarily from a relatively small perisynaptic (peri-PSD) compartment. Gs-coupled receptors promote LTP by replenishing peri-PSD through increased AMPAR exocytosis from a pool of endocytic AMPAR. During LTD induction AMPARs are trafficked in the reverse direction, from the PSD to the peri-PSD compartment, and Gq-coupled receptors promote LTD by clearing the peri-PSD compartment through increased AMPAR endocytosis. We claim that the model not only captures essential features of the pull–push neuromodulation of synaptic plasticity, but it is also consistent with other actions of neuromodulators observed in slice experiments and is compatible with the current understanding of AMPAR trafficking.

Activity-induced secretion of semaphorin 3A mediates learning.

The semaphorin family is a well‐characterized family of secreted or membrane‐bound proteins that are involved in activity‐independent neurodevelopmental processes, such as axon guidance, cell migration, and immune functions. Although semaphorins have recently been demonstrated to regulate activity‐dependent synaptic scaling, their roles in Hebbian synaptic plasticity as well as learning and memory remain poorly understood. Here, using a rodent model, we found that an inhibitory avoidance task, a hippocampus‐dependent contextual learning paradigm, increased secretion of semaphorin 3A in the hippocampus. Furthermore, the secreted semaphorin 3A in the hippocampus mediated contextual memory formation likely by driving AMPA receptors into hippocampal synapses via the neuropilin1–plexin A4–semaphorin receptor complex. This signaling process involves alteration of the phosphorylation status of collapsin response mediator protein 2, which has been characterized as a downstream molecule in semaphorin signaling. These findings implicate semaphorin family as a regulator of Hebbian synaptic plasticity and learning.

Ferroelectric Synaptic Transistor Network for Associative Memory

AbstractBrain‐inspired associative memory is meaningful for pattern recognitions and image/speech processing. Here, a ferroelectric synaptic transistor network is proposed that is capable of associative learning and one‐step recalling of a whole set of data from only partial information. The competition between an external field and the internal depolarization field governs the ferroelectric creep of domain walls and offers each single ferroelectric synapse a full and subfemtojoule‐energy‐cost Hebbian synaptic plasticity, including short‐term memory (STM) to long‐term memory (LTM) transition, and remarkably both spike‐timing‐dependent plasticity (STDP) and spike‐rate‐dependent plasticity (SRDP). Assisted by the third terminal to control the ferroelectric domain dynamics, self‐adaptive coupling between neurons is realized by updating synaptic weight concurrently. Pavlov's dog experiment and multiassociative memories are demonstrated in this ferroelectric synaptic transistor network. Such ferroelectric synaptic transistor network is available for building multilayer neural networks and provides new avenues for associative‐memory information processing.