Synaptic plasticity rules with physiological calcium levels

Coincidence detection between apical and basal dendrites drives STDP in cerebellar Golgi cells

Cerebellar Golgi cells (GoCs), segregate parallel fiber (pf), and mossy fiber (mf) inputs on apical and basal dendrites. Computational modeling predicted that this anatomical arrangement, coupled with a specific ionic channel localization, could be instrumental to drive STDP at mf-GoC synapses. Here, we test this hypothesis with GoC patch-clamp recordings in acute mouse cerebellar slices. Repeated mf-pf pairing on the theta-band within a ± 50 ms time window induces anti-symmetric Hebbian-STDP, with spike-timing long-term potentiation or depression (st-LTP or st-LTD) occurring when action potentials (APs) elicited by pf stimulation follow or precede the activation of mf synapses, respectively. Mf-GoC STDP induction requires AP backpropagation from apical to basal dendrites, NMDA receptor activation at mf-GoC synapses, and intracellular calcium changes. Importantly, STDP is inverted by inhibitory control. Thus, experimental evidence confirms and extends model predictions suggesting that GoC STDP can bind molecular layer to granular layer activity, regulating cerebellar computation and learning.

Alzheimer's disease patient-derived high-molecular-weight tau impairs bursting in hippocampal neurons

Tau accumulation is closely related to cognitive symptoms in Alzheimer's disease (AD). However, the cellular drivers of tau-dependent decline of memory-based cognition remain elusive. Here, we employed in vivo Neuropixels and patch-clamp recordings in mouse models and demonstrate that tau, independent of β-amyloid, selectively debilitates complex-spike burst firing of CA1 hippocampal neurons, a fundamental cellular mechanism underpinning learning and memory. Impaired bursting was associated with altered hippocampal network activities that are coupled to burst firing patterns (i.e., theta rhythms and high-frequency ripples) and was concurrent with reduced neuronal expression of CaV2.3 calcium channels, which are essential for burst firing in vivo. We subsequently identify soluble high molecular weight (HMW) tau, isolated from human AD brain, as the tau species responsible for suppression of burst firing. These data provide a cellular mechanism for tau-dependent cognitive decline in AD and implicate a rare species of intracellular HMW tau as a therapeutic target.

The Leaky Integrate-and-Fire Neuron Is a Change-Point Detector for Compound Poisson Processes

Animal nervous systems can detect changes in their environments within hundredths of a second. They do so by discerning abrupt shifts in sensory neural activity. Many neuroscience studies have employed change-point detection (CPD) algorithms to estimate such abrupt shifts in neural activity. But very few studies have suggested that spiking neurons themselves are online change-point detectors. We show that a leaky integrate-and-fire (LIF) neuron implements an online CPD algorithm for a compound Poisson process. We quantify the CPD performance of an LIF neuron under various regions of its parameter space. We show that CPD can be a recursive algorithm where the output of one algorithm can be input to another. Then we show that a simple feedforward network of LIF neurons can quickly and reliably detect very small changes in input spiking rates. For example, our network detects a 5% change in input rates within 20 ms on average, and false-positive detections are extremely rare. In a rigorous statistical context, we interpret the salient features of the LIF neuron: its membrane potential, synaptic weight, time constant, resting potential, action potentials, and threshold. Our results potentially generalize beyond the LIF neuron model and its associated CPD problem. If spiking neurons perform change-point detection on their inputs, then the electrophysiological properties of their membranes must be related to the spiking statistics of their inputs. We demonstrate one example of this relationship for the LIF neuron and compound Poisson processes and suggest how to test this hypothesis more broadly. Maybe neurons are not noisy devices whose action potentials must be averaged over time or populations. Instead, neurons might implement sophisticated, optimal, and online statistical algorithms on their inputs.

Synaptic plasticity rules driving representational shifting in the hippocampus

Synaptic plasticity is widely thought to support memory storage in the brain, but the rules determining impactful synaptic changes in vivo are not known. We considered the trial-by-trial shifting dynamics of hippocampal place fields (PF) as an indicator of ongoing plasticity during memory formation and familiarization. By implementing different plasticity rules in computational models of spiking place cells and comparing them to experimentally measured PFs from mice navigating familiar and new environments, we found that behavioral timescale synaptic plasticity (BTSP), rather than Hebbian spike-timing-dependent plasticity (STDP), best explains PF shifting dynamics. BTSP-triggering events are rare, but more frequent during new experiences. During exploration, their probability is dynamic-it decays after PF onset, but continually drives a population-level representational drift. Additionally, our results show that BTSP occurs in CA3 but is less frequent and phenomenologically different than in CA1. Overall, our study provides a new framework to understand how synaptic plasticity continuously shapes neuronal representations during learning.

Postsynaptic spiking determines anti-Hebbian LTD in visual cortex basket cells

Long-term plasticity at pyramidal cell to basket cell (PC → BC) synapses is important for the functioning of cortical microcircuits. It is well known that at neocortical PC → PC synapses, dendritic calcium (Ca2+) dynamics signal coincident pre-and postsynaptic spiking which in turn triggers long-term potentiation (LTP). However, the link between dendritic Ca2+ dynamics and long-term plasticity at PC → BC synapses of primary visual cortex (V1) is not as well known. Here, we explored if PC → BC synaptic plasticity in developing V1 is sensitive to postsynaptic spiking. Two-photon (2P) Ca2+ imaging revealed that action potentials (APs) in dendrites of V1 layer-5 (L5) BCs back-propagated decrementally but actively to the location of PC → BC putative synaptic contacts. Pairing excitatory inputs with postsynaptic APs elicited dendritic Ca2+ supralinearities for pre-before-postsynaptic but not post-before-presynaptic temporal ordering, suggesting that APs could impact synaptic plasticity. In agreement, extracellular stimulation as well as high-throughput 2P optogenetic mapping of plasticity both revealed that pre-before-postsynaptic but not post-before-presynaptic pairing resulted in anti-Hebbian long-term depression (LTD). Our results demonstrate that V1 BC dendritic Ca2+ nonlinearities and synaptic plasticity at PC → BC connections are both sensitive to somatic spiking.

mPFC DCC coupling with CaMKII+ neuronal excitation participates in behavioral despair in male mice

A longed lack of control over harmful stimuli can lead to learned helplessness (LH), a significant factor in depression. However, the cellular and molecular mechanisms underlying LH, and eventually behavioral despair, remain largely unknown. The deleted in colorectal cancer (dcc) gene is associated with the risk of depression. However, the therapeutic potential and regulation mechanism of DCC in behavioral despair are still uncertain. In this study, we showed that depressive stimulators, including LH, lipopolysaccharide, and unpredictable chronic mild stress, triggered an elevation in DCC expression in the medial prefrontal cortex (mPFC). Additionally, elevated DCC expression in the mPFC was crucial in inducing behavioral despair, as evidenced by the induction of behavioral despair in normal mice and exacerbation of behavioral despair in LH mice upon DCC overexpression. By contrast, neutralizing DCC activity ameliorated LH-induced behavioral despair. Importantly, we elucidated that pathological DCC expression was attributable to the excessive excitation of CaMKII+ neurons in a manner dependent on the calpain-mediated degradation of SCOP and aberrant phosphorylation of the ERK signaling pathway. In addition, the increase in DCC expression led to a decreased excitability threshold in CaMKII+ neurons in the mPFC, which was supported by the observation that the ligand netrin 1 increased the frequency of action potential firing and of spontaneous excitatory postsynaptic currents in CaMKII+ neurons. In conclusion, our data indicate that LH triggers the excessive excitation of CaMKII+ neurons and activation of calpain-SCOP/ERK signaling to promote DCC expression, and DCC represents a crucial target for the treatment of LH-induced behavioral despair in male mice.

Dendritic excitations govern back-propagation via a spike-rate accelerometer

Dendrites on neurons support electrical excitations, but the computational significance of these events is not well understood. We developed molecular, optical, and computational tools for all-optical electrophysiology in dendrites. We mapped sub-millisecond voltage dynamics throughout the dendritic trees of CA1 pyramidal neurons under diverse optogenetic and synaptic stimulus patterns, in acute brain slices. Our data show history-dependent spike back-propagation in distal dendrites, driven by locally generated Na+ spikes (dSpikes). Dendritic depolarization created a transient window for dSpike propagation, opened by A-type KV channel inactivation, and closed by slow NaV inactivation. Collisions of dSpikes with synaptic inputs triggered calcium channel and N-methyl-D-aspartate receptor (NMDAR)-dependent dendritic plateau potentials and accompanying complex spikes at the soma. This hierarchical ion channel network acts as a spike-rate accelerometer, providing an intuitive picture connecting dendritic biophysics to associative plasticity rules.

Adenosine and Cortical Plasticity

Brain plasticity is the ability of the nervous system to change its structure and functioning in response to experiences. These changes occur mainly at synaptic connections, and this plasticity is named synaptic plasticity. During postnatal development, environmental influences trigger changes in synaptic plasticity that will play a crucial role in the formation and refinement of brain circuits and their functions in adulthood. One of the greatest challenges of present neuroscience is to try to explain how synaptic connections change and cortical maps are formed and modified to generate the most suitable adaptive behavior after different external stimuli. Adenosine is emerging as a key player in these plastic changes at different brain areas. Here, we review the current knowledge of the mechanisms responsible for the induction and duration of synaptic plasticity at different postnatal brain development stages in which adenosine, probably released by astrocytes, directly participates in the induction of long-term synaptic plasticity and in the control of the duration of plasticity windows at different cortical synapses. In addition, we comment on the role of the different adenosine receptors in brain diseases and on the potential therapeutic effects of acting via adenosine receptors.

Cortical HFS-Induced Neo-Hebbian Local Plasticity Enhances Efferent Output Signal and Strengthens Afferent Input Connectivity

High-frequency stimulation (HFS)-induced long-term potentiation (LTP) is generally regarded as a homosynaptic Hebbian-type LTP, where synaptic changes are thought to occur at the synapses that project from the stimulation site and terminate onto the neurons at the recording site. In this study, we first investigated HFS-induced LTP on urethane-anesthetized rats and found that cortical HFS enhances neural responses at the recording site through the strengthening of local connectivity with nearby neurons at the stimulation site rather than through synaptic strengthening at the recording site. This enhanced local connectivity at the stimulation site leads to increased output propagation, resulting in signal potentiation at the recording site. Additionally, we discovered that HFS can also nonspecifically strengthen distant afferent synapses at the HFS site, thereby expanding its impact beyond local neural connections. This form of plasticity exhibits a neo-Hebbian characteristic as it exclusively manifests in the presence of cholecystokinin release, induced by HFS. The cortical HFS-induced local LTP was further supported by a behavioral task, providing additional evidence. Our results unveil a previously overlooked mechanism underlying cortical plasticity: synaptic plasticity is more likely to occur around the soma site of strongly activated cortical neurons rather than solely at their projection terminals.

Evaluation of mechanisms involved in regulation of intrinsic excitability by extracellular calcium in CA1 pyramidal neurons of rat

It is well recognized that changes in the extracellular concentration of calcium ions influence the excitability of neurons, yet what mechanism(s) mediate these effects is still a matter of debate. Using patch-clamp recordings from rat hippocampal CA1 pyramidal neurons, we examined the contribution of G-proteins and intracellular calcium-dependent signaling mechanisms to changes in intrinsic excitability evoked by altering the extracellular calcium concentration from physiological (1.2 mM) to a commonly used experimental (2 mM) level. We find that the inhibitory effect on intrinsic excitability of calcium ions is mainly expressed as an increased threshold for action potential firing (with no significant effect on resting membrane potential) that is not blocked by either the G-protein inhibitor GDPβS or the calcium chelator BAPTA. Our results therefore argue that in the concentration range studied, G-protein coupled calcium-sensing receptors, non-selective cation conductances, and intracellular calcium signaling pathways are not involved in mediating the effect of extracellular calcium ions on intrinsic excitability. Analysis of the derivative of the action potential, dV/dt versus membrane potential, indicates a current shift towards more depolarized membrane potentials at the higher calcium concentration. Our results are thus consistent with a mechanism in which extracellular calcium ions act directly on the voltage-gated sodium channels by neutralizing negative charges on the extracellular surface of these channels to modulate the threshold for action potential activation.

Membrane potential states gate synaptic consolidation in human neocortical tissue

Synaptic mechanisms that contribute to human memory consolidation remain largely unexplored. Consolidation critically relies on sleep. During slow wave sleep, neurons exhibit characteristic membrane potential oscillations known as UP and DOWN states. Coupling of memory reactivation to these slow oscillations promotes consolidation, though the underlying mechanisms remain elusive. Here, we performed axonal and multineuron patch-clamp recordings in acute human brain slices, obtained from neurosurgeries, to show that sleep-like UP and DOWN states modulate axonal action potentials and temporarily enhance synaptic transmission between neocortical pyramidal neurons. Synaptic enhancement by UP and DOWN state sequences facilitates recruitment of postsynaptic action potentials, which in turn results in long-term stabilization of synaptic strength. In contrast, synapses undergo lasting depression if presynaptic neurons fail to recruit postsynaptic action potentials. Our study offers a mechanistic explanation for how coupling of neural activity to slow waves can cause synaptic consolidation, with potential implications for brain stimulation strategies targeting memory performance.

Expansion of epileptogenic networks via neuroplasticity in neural mass models

Neuroplasticity refers to functional and structural changes in brain regions in response to healthy and pathological activity. Activity dependent plasticity induced by epileptic activity can involve healthy brain regions into the epileptogenic network by perturbing their excitation/inhibition balance. In this article, we present a new neural mass model, which accounts for neuroplasticity, for investigating the possible mechanisms underlying the epileptogenic network expansion. Our multiple-timescale model is inspired by physiological calcium-mediated synaptic plasticity and pathological extrasynaptic N-methyl-D-aspartate (NMDA) dependent plasticity dynamics. The model highlights that synaptic plasticity at excitatory connections and structural changes in the inhibitory system can transform a healthy region into a secondary epileptic focus under recurrent seizures and interictal activity occurring in the primary focus. Our results suggest that the latent period of this transformation can provide a window of opportunity to prevent the expansion of epileptogenic networks, formation of an epileptic focus, or other comorbidities associated with epileptic activity.

Mild focal cooling selectively impacts computations in dendritic trees

Focal cooling is a powerful technique to temporally scale neural dynamics. However, the underlying cellular mechanisms causing this scaling remain unresolved. Here, using targeted focal cooling (with a spatial resolution of 100 micrometers), dual somato-dendritic patch clamp recordings, two-photon calcium imaging, transmitter uncaging, and modeling we reveal that a 5°C drop can enhance synaptic transmission, plasticity, and input-output transformations in the distal apical tuft, but not in the basal dendrites of intrinsically bursting L5 pyramidal neurons. This enhancement depends on N-methyl-D-aspartate (NMDA) and Kv4.2, suggesting electrical structure modulation. Paradoxically, and despite the increase in tuft excitability, we observe a reduced rate of recovery from inactivation for apical Na+ channels, thereby regulating back-propagating action potential invasion, coincidence detection, and overall burst probability, resulting in an "apparent" slowing of somatic spike output. Our findings reveal a differential temperature sensitivity along the basal-tuft axis of L5 neurons analog modulates cortical output.

The role of astrocytes from synaptic to non-synaptic plasticity

Information storage and transfer in the brain require a high computational power. Neuronal network display various local or global mechanisms to allow information storage and transfer in the brain. From synaptic to intrinsic plasticity, the rules of input-output function modulation have been well characterized in neurons. In the past years, astrocytes have been suggested to increase the computational power of the brain and we are only just starting to uncover their role in information processing. Astrocytes maintain a close bidirectional communication with neurons to modify neuronal network excitability, transmission, axonal conduction, and plasticity through various mechanisms including the release of gliotransmitters or local ion homeostasis. Astrocytes have been significantly studied in the context of long-term or short-term synaptic plasticity, but this is not the only mechanism involved in memory formation. Plasticity of intrinsic neuronal excitability also participates in memory storage through regulation of voltage-gated ion channels or axonal morphological changes. Yet, the contribution of astrocytes to these other forms of non-synaptic plasticity remains to be investigated. In this review, we summarized the recent advances on the role of astrocytes in different forms of plasticity and discuss new directions and ideas to be explored regarding astrocytes-neuronal communication and regulation of plasticity.

Competitive processes shape multi-synapse plasticity along dendritic segments

Neurons receive thousands of inputs onto their dendritic arbour, where individual synapses undergo activity-dependent plasticity. Long-lasting changes in postsynaptic strengths correlate with changes in spine head volume. The magnitude and direction of such structural plasticity - potentiation (sLTP) and depression (sLTD) - depend upon the number and spatial distribution of stimulated synapses. However, how neurons allocate resources to implement synaptic strength changes across space and time amongst neighbouring synapses remains unclear. Here we combined experimental and modelling approaches to explore the elementary processes underlying multi-spine plasticity. We used glutamate uncaging to induce sLTP at varying number of synapses sharing the same dendritic branch, and we built a model incorporating a dual role Ca2+-dependent component that induces spine growth or shrinkage. Our results suggest that competition among spines for molecular resources is a key driver of multi-spine plasticity and that spatial distance between simultaneously stimulated spines impacts the resulting spine dynamics.

Dynamic control of sequential retrieval speed in networks with heterogeneous learning rules

Temporal rescaling of sequential neural activity has been observed in multiple brain areas during behaviors involving time estimation and motor execution at variable speeds. Temporally asymmetric Hebbian rules have been used in network models to learn and retrieve sequential activity, with characteristics that are qualitatively consistent with experimental observations. However, in these models sequential activity is retrieved at a fixed speed. Here, we investigate the effects of a heterogeneity of plasticity rules on network dynamics. In a model in which neurons differ by the degree of temporal symmetry of their plasticity rule, we find that retrieval speed can be controlled by varying external inputs to the network. Neurons with temporally symmetric plasticity rules act as brakes and tend to slow down the dynamics, while neurons with temporally asymmetric rules act as accelerators of the dynamics. We also find that such networks can naturally generate separate 'preparatory' and 'execution' activity patterns with appropriate external inputs.

A dendritic mechanism for balancing synaptic flexibility and stability

Biological and artificial neural networks learn by modifying synaptic weights, but it is unclear how these systems retain previous knowledge and also acquire new information. Here, we show that cortical pyramidal neurons can solve this plasticity-versus-stability dilemma by differentially regulating synaptic plasticity at distinct dendritic compartments. Oblique dendrites of adult mouse layer 5 cortical pyramidal neurons selectively receive monosynaptic thalamic input, integrate linearly, and lack burst-timing synaptic potentiation. In contrast, basal dendrites, which do not receive thalamic input, exhibit conventional NMDA receptor (NMDAR)-mediated supralinear integration and synaptic potentiation. Congruently, spiny synapses on oblique branches show decreased structural plasticity in vivo. The selective decline in NMDAR activity and expression at synapses on oblique dendrites is controlled by a critical period of visual experience. Our results demonstrate a biological mechanism for how single neurons can safeguard a set of inputs from ongoing plasticity by altering synaptic properties at distinct dendritic domains.

Co-existence of synaptic plasticity and metastable dynamics in a spiking model of cortical circuits

Evidence for metastable dynamics and its role in brain function is emerging at a fast pace and is changing our understanding of neural coding by putting an emphasis on hidden states of transient activity. Clustered networks of spiking neurons have enhanced synaptic connections among groups of neurons forming structures called cell assemblies; such networks are capable of producing metastable dynamics that is in agreement with many experimental results. However, it is unclear how a clustered network structure producing metastable dynamics may emerge from a fully local plasticity rule, i.e., a plasticity rule where each synapse has only access to the activity of the neurons it connects (as opposed to the activity of other neurons or other synapses). Here, we propose a local plasticity rule producing ongoing metastable dynamics in a deterministic, recurrent network of spiking neurons. The metastable dynamics co-exists with ongoing plasticity and is the consequence of a self-tuning mechanism that keeps the synaptic weights close to the instability line where memories are spontaneously reactivated. In turn, the synaptic structure is stable to ongoing dynamics and random perturbations, yet it remains sufficiently plastic to remap sensory representations to encode new sets of stimuli. Both the plasticity rule and the metastable dynamics scale well with network size, with synaptic stability increasing with the number of neurons. Overall, our results show that it is possible to generate metastable dynamics over meaningful hidden states using a simple but biologically plausible plasticity rule which co-exists with ongoing neural dynamics.

Co-existence of synaptic plasticity and metastable dynamics in a spiking model of cortical circuits

Evidence for metastable dynamics and its role in brain function is emerging at a fast pace and is changing our understanding of neural coding by putting an emphasis on hidden states of transient activity. Clustered networks of spiking neurons have enhanced synaptic connections among groups of neurons forming structures called cell assemblies; such networks are capable of producing metastable dynamics that is in agreement with many experimental results. However, it is unclear how a clustered network structure producing metastable dynamics may emerge from a fully local plasticity rule, i.e., a plasticity rule where each synapse has only access to the activity of the neurons it connects (as opposed to the activity of other neurons or other synapses). Here, we propose a local plasticity rule producing ongoing metastable dynamics in a deterministic, recurrent network of spiking neurons. The metastable dynamics co-exists with ongoing plasticity and is the consequence of a self-tuning mechanism that keeps the synaptic weights close to the instability line where memories are spontaneously reactivated. In turn, the synaptic structure is stable to ongoing dynamics and random perturbations, yet it remains sufficiently plastic to remap sensory representations to encode new sets of stimuli. Both the plasticity rule and the metastable dynamics scale well with network size, with synaptic stability increasing with the number of neurons. Overall, our results show that it is possible to generate metastable dynamics over meaningful hidden states using a simple but biologically plausible plasticity rule which co-exists with ongoing neural dynamics.

Physiological roles of endocytosis and presynaptic scaffold in vesicle replenishment at fast and slow central synapses

After exocytosis, release sites are cleared of vesicular residues to replenish with transmitter-filled vesicles. Endocytic and scaffold proteins are thought to underlie this site-clearance mechanism. However, the physiological significance of this mechanism at diverse mammalian central synapses remains unknown. Here, we tested this in a physiologically optimized condition using action potential evoked EPSCs at fast calyx synapse and relatively slow hippocampal CA1 synapse, in post-hearing mice brain slices at 37°C and in 1.3 mM [Ca2+]. Pharmacological block of endocytosis enhanced synaptic depression at the calyx synapse, whereas it attenuated synaptic facilitation at the hippocampal synapse. Block of scaffold protein activity likewise enhanced synaptic depression at the calyx but had no effect at the hippocampal synapse. At the fast calyx synapse, block of endocytosis or scaffold protein activity significantly enhanced synaptic depression as early as 10 ms after the stimulation onset. Unlike previous reports, neither endocytic blockers nor scaffold protein inhibitors prolonged the recovery from short-term depression. We conclude that the release-site clearance by endocytosis can be a universal phenomenon supporting vesicle replenishment at both fast and slow synapses, whereas the presynaptic scaffold mechanism likely plays a specialized role in vesicle replenishment predominantly at fast synapses.

Synaptopodin is required for long-term depression at Schaffer collateral-CA1 synapses

Synaptopodin (SP), an actin-associated protein found in telencephalic neurons, affects activity-dependant synaptic plasticity and dynamic changes of dendritic spines. While being required for long-term depression (LTD) mediated by metabotropic glutamate receptor (mGluR-LTD), little is known about its role in other forms of LTD induced by low frequency stimulation (LFS-LTD) or spike-timing dependent plasticity (STDP). Using electrophysiology in ex vivo hippocampal slices from SP-deficient mice (SPKO), we show that absence of SP is associated with a deficit of LTD at Sc-CA1 synapses induced by LFS-LTD and STDP. As LTD is known to require AMPA- receptors internalization and IP3-receptors calcium signaling, we tested by western blotting and immunochemistry if there were changes in their expression which we found to be reduced. While we were not able to induce LTD, long-term potentiation (LTP), albeit diminished in SPKO, can be recovered by using a stronger stimulation protocol. In SPKO we found no differences in NMDAR, which are the primary site of calcium signalling to induce LTP. Our study shows, for the first time, the key role of the requirement of SP to allow induction of activity-dependant LTD at Sc-CA1 synapses.

Functional subtypes of synaptic dynamics in mouse and human

Synapses preferentially respond to particular temporal patterns of activity with a large degree of heterogeneity that is informally or tacitly separated into classes. Yet, the precise number and properties of such classes are unclear. Do they exist on a continuum and, if so, when is it appropriate to divide that continuum into functional regions? In a large dataset of glutamatergic cortical connections, we perform model-based characterization to infer the number and characteristics of functionally distinct subtypes of synaptic dynamics. In rodent data, we find five clusters that partially converge with transgenic-associated subtypes. Strikingly, the application of the same clustering method in human data infers a highly similar number of clusters, supportive of stable clustering. This nuanced dictionary of functional subtypes shapes the heterogeneity of cortical synaptic dynamics and provides a lens into the basic motifs of information transmission in the brain.

A biochemical description of postsynaptic plasticity-with timescales ranging from milliseconds to seconds

Synaptic plasticity [long-term potentiation/depression (LTP/D)], is a cellular mechanism underlying learning. Two distinct types of early LTP/D (E-LTP/D), acting on very different time scales, have been observed experimentally-spike timing dependent plasticity (STDP), on time scales of tens of ms; and behavioral time scale synaptic plasticity (BTSP), on time scales of seconds. BTSP is a candidate for a mechanism underlying rapid learning of spatial location by place cells. Here, a computational model of the induction of E-LTP/D at a spine head of a synapse of a hippocampal pyramidal neuron is developed. The single-compartment model represents two interacting biochemical pathways for the activation (phosphorylation) of the kinase (CaMKII) with a phosphatase, with ion inflow through channels (NMDAR, CaV1,Na). The biochemical reactions are represented by a deterministic system of differential equations, with a detailed description of the activation of CaMKII that includes the opening of the compact state of CaMKII. This single model captures realistic responses (temporal profiles with the differing timescales) of STDP and BTSP and their asymmetries. The simulations distinguish several mechanisms underlying STDP vs. BTSP, including i) the flow of [Formula: see text] through NMDAR vs. CaV1 channels, and ii) the origin of several time scales in the activation of CaMKII. The model also realizes a priming mechanism for E-LTP that is induced by [Formula: see text] flow through CaV1.3 channels. Once in the spine head, this small additional [Formula: see text] opens the compact state of CaMKII, placing CaMKII ready for subsequent induction of LTP.

Synaptic plasticity through a naturalistic lens

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

Making time and space for calcium control of neuron activity

Calcium directly controls or indirectly regulates numerous functions that are critical for neuronal network activity. Intracellular calcium concentration is tightly regulated by numerous molecular mechanisms because spatial domains and temporal dynamics (not just peak amplitude) are critical for calcium control of synaptic plasticity and ion channel activation, which in turn determine neuron spiking activity. The computational models investigating calcium control are valuable because experiments achieving high spatial and temporal resolution simultaneously are technically unfeasible. Simulations of calcium nanodomains reveal that specific calcium sources can couple to specific calcium targets, providing a mechanism to determine the direction of synaptic plasticity. Cooperativity of calcium domains opposes specificity, suggesting that the dendritic branch might be the preferred computational unit of the neuron.

Nitric oxide modulates NMDA receptor through a negative feedback mechanism and regulates the dynamical behavior of neuronal postsynaptic components

Nitric oxide (NO) is known to be an important regulator of neurological processes in the central nervous system which acts directly on the presynaptic neuron and enhances the release of neurotransmitters like glutamate into the synaptic cleft. Calcium influx activates a cascade of biochemical reactions to influence the production of nitric oxide in the postsynaptic neuron. This has been modeled in the present work as a system of ordinary differential equations, to explore the dynamics of the interacting components and predict the dynamical behavior of the postsynaptic neuron. It has been hypothesized that nitric oxide modulates the NMDA receptor via a feedback mechanism and regulates the dynamic behavior of postsynaptic components. Results obtained by numerical analyses indicate that the biochemical system is stimulus-dependent and shows oscillations of calcium and other components within a limited range of concentration. Some of the parameters such as stimulus strength, extracellular calcium concentration, and rate of nitric oxide feedback are crucial for the dynamics of the components in the postsynaptic neuron.

Silences, spikes and bursts: Three-part knot of the neural code

When a neuron breaks silence, it can emit action potentials in a number of patterns. Some responses are so sudden and intense that electrophysiologists felt the need to single them out, labelling action potentials emitted at a particularly high frequency with a metonym - bursts. Is there more to bursts than a figure of speech? After all, sudden bouts of high-frequency firing are expected to occur whenever inputs surge. The burst coding hypothesis advances that the neural code has three syllables: silences, spikes and bursts. We review evidence supporting this ternary code in terms of devoted mechanisms for burst generation, synaptic transmission and synaptic plasticity. We also review the learning and attention theories for which such a triad is beneficial.

The combination of Hebbian and predictive plasticity learns invariant object representations in deep sensory networks

Recognition of objects from sensory stimuli is essential for survival. To that end, sensory networks in the brain must form object representations invariant to stimulus changes, such as size, orientation and context. Although Hebbian plasticity is known to shape sensory networks, it fails to create invariant object representations in computational models, raising the question of how the brain achieves such processing. In the present study, we show that combining Hebbian plasticity with a predictive form of plasticity leads to invariant representations in deep neural network models. We derive a local learning rule that generalizes to spiking neural networks and naturally accounts for several experimentally observed properties of synaptic plasticity, including metaplasticity and spike-timing-dependent plasticity. Finally, our model accurately captures neuronal selectivity changes observed in the primate inferotemporal cortex in response to altered visual experience. Thus, we provide a plausible normative theory emphasizing the importance of predictive plasticity mechanisms for successful representational learning.

BTSP, not STDP, Drives Shifts in Hippocampal Representations During Familiarization

Synaptic plasticity is widely thought to support memory storage in the brain, but the rules determining impactful synaptic changes in-vivo are not known. We considered the trial-by-trial shifting dynamics of hippocampal place fields (PFs) as an indicator of ongoing plasticity during memory formation. By implementing different plasticity rules in computational models of spiking place cells and comparing to experimentally measured PFs from mice navigating familiar and novel environments, we found that Behavioral-Timescale-Synaptic-Plasticity (BTSP), rather than Hebbian Spike-Timing-Dependent-Plasticity, is the principal mechanism governing PF shifting dynamics. BTSP-triggering events are rare, but more frequent during novel experiences. During exploration, their probability is dynamic: it decays after PF onset, but continually drives a population-level representational drift. Finally, our results show that BTSP occurs in CA3 but is less frequent and phenomenologically different than in CA1. Overall, our study provides a new framework to understand how synaptic plasticity shapes neuronal representations during learning.

Modular architecture facilitates noise-driven control of synchrony in neuronal networks

High-level information processing in the mammalian cortex requires both segregated processing in specialized circuits and integration across multiple circuits. One possible way to implement these seemingly opposing demands is by flexibly switching between states with different levels of synchrony. However, the mechanisms behind the control of complex synchronization patterns in neuronal networks remain elusive. Here, we use precision neuroengineering to manipulate and stimulate networks of cortical neurons in vitro, in combination with an in silico model of spiking neurons and a mesoscopic model of stochastically coupled modules to show that (i) a modular architecture enhances the sensitivity of the network to noise delivered as external asynchronous stimulation and that (ii) the persistent depletion of synaptic resources in stimulated neurons is the underlying mechanism for this effect. Together, our results demonstrate that the inherent dynamical state in structured networks of excitable units is determined by both its modular architecture and the properties of the external inputs.

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.

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

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

Learning cortical hierarchies with temporal Hebbian updates

A key driver of mammalian intelligence is the ability to represent incoming sensory information across multiple abstraction levels. For example, in the visual ventral stream, incoming signals are first represented as low-level edge filters and then transformed into high-level object representations. Similar hierarchical structures routinely emerge in artificial neural networks (ANNs) trained for object recognition tasks, suggesting that similar structures may underlie biological neural networks. However, the classical ANN training algorithm, backpropagation, is considered biologically implausible, and thus alternative biologically plausible training methods have been developed such as Equilibrium Propagation, Deep Feedback Control, Supervised Predictive Coding, and Dendritic Error Backpropagation. Several of those models propose that local errors are calculated for each neuron by comparing apical and somatic activities. Notwithstanding, from a neuroscience perspective, it is not clear how a neuron could compare compartmental signals. Here, we propose a solution to this problem in that we let the apical feedback signal change the postsynaptic firing rate and combine this with a differential Hebbian update, a rate-based version of classical spiking time-dependent plasticity (STDP). We prove that weight updates of this form minimize two alternative loss functions that we prove to be equivalent to the error-based losses used in machine learning: the inference latency and the amount of top-down feedback necessary. Moreover, we show that the use of differential Hebbian updates works similarly well in other feedback-based deep learning frameworks such as Predictive Coding or Equilibrium Propagation. Finally, our work removes a key requirement of biologically plausible models for deep learning and proposes a learning mechanism that would explain how temporal Hebbian learning rules can implement supervised hierarchical learning.

The Cannabinoids, CBDA and THCA, Rescue Memory Deficits and Reduce Amyloid-Beta and Tau Pathology in an Alzheimer’s Disease-like Mouse Model

Most studies related to hemp are focused on Cannabidiol (CBD) and Tetrahydrocannabinol (THC); however, up to 120 types of phytocannabinoids are present in hemp. Hemp leaves contain large amounts of Cannabidiolic acid (CBDA) and Tetrahydrocannabinolic acid (THCA), which are acidic variants of CBD and THC and account for the largest proportion of CBDA. In recent studies, CBDA exhibited anti-hyperalgesia and anti-inflammatory effects. THCA also showed anti-inflammatory and neuroprotective effects that may be beneficial for treating neurodegenerative diseases. CBDA and THCA can penetrate the blood–brain barrier (BBB) and affect the central nervous system. The purpose of this study was to determine whether CBDA and THCA ameliorate Alzheimer’s disease (AD)-like features in vitro and in vivo. The effect of CBDA and THCA was evaluated in the Aβ1–42-treated mouse model. We observed that Aβ1–42-treated mice had more hippocampal Aβ and p-tau levels, pathological markers of AD, and loss of cognitive function compared with PBS-treated mice. However, CBDA- and THCA-treated mice showed decreased hippocampal Aβ and p-tau and superior cognitive function compared with Aβ1–42-treated mice. In addition, CBDA and THCA lowered Aβ and p-tau levels, alleviated calcium dyshomeostasis, and exhibited neuroprotective effects in primary neurons. Our results suggest that CBDA and THCA have anti-AD effects and mitigate memory loss and resilience to increased hippocampal Ca2+, Aβ, and p-tau levels. Together, CBDA and THCA may be useful therapeutic agents for treating AD.

GluN2B-NMDAR subunit contribution on synaptic plasticity: A phenomenological model for CA3-CA1 synapses

Synaptic plasticity is believed to be a key mechanism underlying learning and memory. We developed a phenomenological N-methyl-D-aspartate (NMDA) receptor-based voltage-dependent synaptic plasticity model for synaptic modifications at hippocampal CA3-CA1 synapses on a hippocampal CA1 pyramidal neuron. The model incorporates the GluN2A-NMDA and GluN2B-NMDA receptor subunit-based functions and accounts for the synaptic strength dependence on the postsynaptic NMDA receptor composition and functioning without explicitly modeling the NMDA receptor-mediated intracellular calcium, a local trigger of synaptic plasticity. We embedded the model into a two-compartmental model of a hippocampal CA1 pyramidal cell and validated it against experimental data of spike-timing-dependent synaptic plasticity (STDP), high and low-frequency stimulation. The developed model predicts altered learning rules in synapses formed on the apical dendrites of the detailed compartmental model of CA1 pyramidal neuron in the presence of the GluN2B-NMDA receptor hypofunction and can be used in hippocampal networks to model learning in health and disease.

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.

2-AG-Mediated Control of GABAergic Signaling Is Impaired in a Model of Epilepsy

Repeated seizures result in a persistent maladaptation of endocannabinoid (eCB) signaling, mediated part by anandamide signaling deficiency in the basolateral amygdala (BLA) that manifests as aberrant synaptic function and altered emotional behavior. Here, we determined the effect of repeated seizures (kindling) on 2-arachidonoylglycerol (2-AG) signaling on GABA transmission by directly measuring tonic and phasic eCB-mediated retrograde signaling in an in vitro BLA slice preparation from male rats. We report that both activity-dependent and muscarinic acetylcholine receptor (mAChR)-mediated depression of GABA synaptic transmission was reduced following repeated seizure activity. These effects were recapitulated in sham rats by preincubating slices with the 2-AG synthesizing enzyme inhibitor DO34. Conversely, preincubating slices with the 2-AG degrading enzyme inhibitor KML29 rescued activity-dependent 2-AG signaling, but not mAChR-mediated synaptic depression, over GABA transmission in kindled rats. These effects were not attributable to a change in cannabinoid type 1 (CB1) receptor sensitivity or altered 2-AG tonic signaling since the application of the highly selective CB1 receptor agonist CP55,940 provoked a similar reduction in GABA synaptic activity in both sham and kindled rats, while no effect of either DO34 or of the CB1 inverse agonist AM251 was observed on frequency and amplitude of spontaneous IPSCs in either sham or kindled rats. Collectively, these data provide evidence that repeated amygdala seizures persistently alter phasic 2-AG-mediated retrograde signaling at BLA GABAergic synapses, probably by impairing stimulus-dependent 2-AG synthesis/release, which contributes to the enduring aberrant synaptic plasticity associated with seizure activity.SIGNIFICANCE STATEMENT The plastic reorganization of endocannabinoid (eCB) signaling after seizures and during epileptogenesis may contribute to the negative neurobiological consequences associated with seizure activity. Therefore, a deeper understanding of the molecular basis underlying the pathologic long-term eCB signaling remodeling following seizure activity will be crucial to the development of novel therapies for epilepsy that not only target seizure activity, but, most importantly, the epileptogenesis and the comorbid conditions associated with epilepsy.

Regulation of circuit organization and function through inhibitory synaptic plasticity

Diverse inhibitory neurons in the mammalian brain shape circuit connectivity and dynamics through mechanisms of synaptic plasticity. Inhibitory plasticity can establish excitation/inhibition (E/I) balance, control neuronal firing, and affect local calcium concentration, hence regulating neuronal activity at the network, single neuron, and dendritic level. Computational models can synthesize multiple experimental results and provide insight into how inhibitory plasticity controls circuit dynamics and sculpts connectivity by identifying phenomenological learning rules amenable to mathematical analysis. We highlight recent studies on the role of inhibitory plasticity in modulating excitatory plasticity, forming structured networks underlying memory formation and recall, and implementing adaptive phenomena and novelty detection. We conclude with experimental and modeling progress on the role of interneuron-specific plasticity in circuit computation and context-dependent learning.

Co-exposure of melamine and cyanuric acid as a risk factor for oxidative stress and energy metabolism: Adverse effects on hippocampal neuronal and synaptic function induced by excessive ROS production

Melamine (MEL) and cyanuric acid (CA) alone have relatively low toxicity, but together they may cause serious damage to multiple organs, including the central nervous system, however, the underlying mechanism is unknown. This study aimed to determine and compare the neurotoxic effects of MEL (20 μg/mL), CA (20 μg/mL) and their combination (10 μg/mL MEL and 10 μg/mL CA) on cultured hippocampal neurons. The cell viability, apoptosis, anti-oxidative and energy metabolic indices were detected following 24 h of incubations. The miniature excitatory postsynaptic currents (mEPSCs), miniature inhibitory postsynaptic currents (mIPSCs) and synaptic plasticity in the hippocampal CA1 neurons were recorded. Moreover, ROS scavenger NAC was co-infused to investigate the potential mechanism. We found the complex of MEL and CA but not their alone caused severe cell death and disturbed energy production through activation caspase-3-mediated apoptosis. Meanwhile, the combination significantly reduced the amplitude, decay time and frequency of mEPSCs but not mIPSCs, indicating the pre- and post-synaptic inhibitory actions on neuronal activity. Paired-pulsed ratio (PPR) and long-term potentiation (LTP) at the Schaffer collateral-CA1 synapses were critically depressed. However, the co-application of NAC could effectively mitigate the cellular apoptosis, energy metabolism dysfunction and the impairments in neuronal and synaptic function. Our findings provide the first evidence that the combination of MEL and CA can exert more prominently neurotoxic effects than their alone and certify that one of the potential mechanisms for neuronal and synaptic dysfunction is the ROS-mediated signaling pathway.

Theta patterns of stimulation induce synaptic and intrinsic potentiation in O-LM interneurons

Brain oscillations have long-lasting effects on synaptic and cellular properties. For instance, synaptic stimulation at theta (θ) frequency induces persistent depression of both excitatory synaptic transmission and intrinsic excitability in CA1 principal neurons. However, the incidence of θ activity on synaptic transmission and intrinsic excitability in hippocampal GABAergic interneurons is unclear. We report here the induction of both synaptic and intrinsic potentiation in oriens-lacunosum moleculare (O-LM) interneurons following stimulation of afferent glutamatergic inputs in the θ frequency range (∼5 Hz). Long-term synaptic potentiation (LTP) is induced by synaptic activation of calcium-permeable AMPA receptors (CP-AMPAR), whereas long-term potentiation of intrinsic excitability (LTP-IE) results from the mGluR1-dependent down-regulation of Kv7 voltage-dependent potassium channel and hyperpolarization activated and cyclic nucleotide-gated (HCN) channel through the depletion of phosphatidylinositol-4,5-biphosphate (PIP2). LTP and LTP-IE are reversible, demonstrating that both synaptic and intrinsic changes are bidirectional in O-LM cells. We conclude that synaptic activity at θ frequency induces both synaptic and intrinsic potentiation in O-LM interneurons, i.e., the opposite of what is typically seen in glutamatergic neurons.

Druggable transcriptomic pathways revealed in Parkinson's patient-derived midbrain neurons

Complex genetic predispositions accelerate the chronic degeneration of midbrain substantia nigra neurons in Parkinson’s disease (PD). Deciphering the human molecular makeup of PD pathophysiology can guide the discovery of therapeutics to slow the disease progression. However, insights from human postmortem brain studies only portray the latter stages of PD, and there is a lack of data surrounding molecular events preceding the neuronal loss in patients. We address this gap by identifying the gene dysregulation of live midbrain neurons reprogrammed in vitro from the skin cells of 42 individuals, including sporadic and familial PD patients and matched healthy controls. To minimize bias resulting from neuronal reprogramming and RNA-seq methods, we developed an analysis pipeline integrating PD transcriptomes from different RNA-seq datasets (unsorted and sorted bulk vs. single-cell and Patch-seq) and reprogramming strategies (induced pluripotency vs. direct conversion). This PD cohort’s transcriptome is enriched for human genes associated with known clinical phenotypes of PD, regulation of locomotion, bradykinesia and rigidity. Dysregulated gene expression emerges strongest in pathways underlying synaptic transmission, metabolism, intracellular trafficking, neural morphogenesis and cellular stress/immune responses. We confirmed a synaptic impairment with patch-clamping and identified pesticides and endoplasmic reticulum stressors as the most significant gene-chemical interactions in PD. Subsequently, we associated the PD transcriptomic profile with candidate pharmaceuticals in a large database and a registry of current clinical trials. This study highlights human transcriptomic pathways that can be targeted therapeutically before the irreversible neuronal loss. Furthermore, it demonstrates the preclinical relevance of unbiased large transcriptomic assays of reprogrammed patient neurons.

Postsynaptic burst reactivation of hippocampal neurons enables associative plasticity of temporally discontiguous inputs

A fundamental unresolved problem in neuroscience is how the brain associates in memory events that are separated in time. Here, we propose that reactivation-induced synaptic plasticity can solve this problem. Previously, we reported that the reinforcement signal dopamine converts hippocampal spike timing-dependent depression into potentiation during continued synaptic activity (Brzosko et al., 2015). Here, we report that postsynaptic bursts in the presence of dopamine produce input-specific LTP in mouse hippocampal synapses 10 min after they were primed with coincident pre- and post-synaptic activity (post-before-pre pairing; Δt = -20 ms). This priming activity induces synaptic depression and sets an NMDA receptor-dependent silent eligibility trace which, through the cAMP-PKA cascade, is rapidly converted into protein synthesis-dependent synaptic potentiation, mediated by a signaling pathway distinct from that of conventional LTP. This synaptic learning rule was incorporated into a computational model, and we found that it adds specificity to reinforcement learning by controlling memory allocation and enabling both 'instructive' and 'supervised' reinforcement learning. We predicted that this mechanism would make reactivated neurons activate more strongly and carry more spatial information than non-reactivated cells, which was confirmed in freely moving mice performing a reward-based navigation task.

Multiplicative Shot-Noise: A New Route to Stability of Plastic Networks

Fluctuations of synaptic weights, among many other physical, biological, and ecological quantities, are driven by coincident events of two "parent" processes. We propose a multiplicative shot-noise model that can capture the behaviors of a broad range of such natural phenomena, and analytically derive an approximation that accurately predicts its statistics. We apply our results to study the effects of a multiplicative synaptic plasticity rule that was recently extracted from measurements in physiological conditions. Using mean-field theory analysis and network simulations, we investigate how this rule shapes the connectivity and dynamics of recurrent spiking neural networks. The multiplicative plasticity rule is shown to support efficient learning of input stimuli, and it gives a stable, unimodal synaptic-weight distribution with a large fraction of strong synapses. The strong synapses remain stable over long times but do not "run away." Our results suggest that the multiplicative shot-noise offers a new route to understand the tradeoff between flexibility and stability in neural circuits and other dynamic networks.

Adenosine and Astrocytes Determine the Developmental Dynamics of Spike Timing-Dependent Plasticity in the Somatosensory Cortex

During development, critical periods of synaptic plasticity facilitate the reordering and refinement of neural connections, allowing the definitive synaptic circuits responsible for correct adult physiology to be established. The L4-L2/3 synapses in the somatosensory cortex (S1) exhibit a presynaptic form of spike timing-dependent long-term depression (t-LTD) that probably fulfills a role in synaptic refinement. This t-LTD persists until the fourth postnatal week in mice, disappearing thereafter. When we investigated the mechanisms underlying this maturation-related loss of t-LTD in either sex mouse slices, we found that it could be completely recovered by antagonizing adenosine type 1 receptors. By contrast, an agonist of A1R impeded the induction of t-LTD at P13-27. Furthermore, we found that the adenosine that mediated the loss of t-LTD at the end of the fourth week of development is most probably supplied by astrocytes. At more mature stages (P38-60), we found that the protocol used to induce t-LTD provokes t-LTP. We characterized the mechanisms underlying the induction of this form of LTP, and we found it to be expressed presynaptically, as witnessed by paired-pulse and coefficient of variation analysis. In addition, this form of presynaptic t-LTP requires the activation of NMDARs and mGlu1Rs, and the entry of Ca2+ into the postsynaptic neuron through L-type voltage-dependent Ca2+ channels. Nitric oxide is also required for t-LTP as a messenger in the postsynaptic neuron as are the adenosine and glutamate that are released in association with astrocyte signaling. These results provide direct evidence of the mechanisms that close the window of plasticity associated with t-LTD and that drive the switch in synaptic transmission from t-LTD to t-LTP at L4-L2/3 synapses, in which astrocytes play a central role.SIGNIFICANCE STATEMENT During development, critical periods of plasticity facilitate the reordering and refining of neural connections, allowing correct adult physiology to be established. The L4-L2/3 synapses in the somatosensory cortex exhibit a presynaptic form plasticity (LTD) that probably fulfills a role in synaptic refinement. It is present until the fourth postnatal week in mice, disappearing thereafter. The mechanisms that are responsible for this loss of plasticity are not clear. We describe here these mechanisms and those involved in the switch from LTD to LTP observed as the brain matures. Defining these events responsible for closing (and opening) plasticity windows may be important for brain repair, sensorial recovery, the treatment of neurodevelopmental disorders, and for educational policy.

Synaptic NMDA receptor activity at resting membrane potentials

NMDA receptors (NMDARs) are crucial for glutamatergic synaptic signaling in the mammalian central nervous system. When activated by glutamate and glycine/D-serine, the NMDAR ion channel can open, but current flux is further regulated by voltage-dependent block conferred by extracellular Mg²⁺ ions. The unique biophysical property of ligand- and voltage-dependence positions NMDARs as synaptic coincidence detectors, controlling a major source of synaptic Ca²⁺ influx. We measured synaptic currents in layer 2/3 neurons after stimulation in layer 4 of somatosensory cortex and found measurable NMDAR currents at all voltages tested. This NMDAR current did not require concurrent AMPAR depolarization. In physiological ionic conditions, the NMDAR current response at negative potentials was enhanced relative to ionic conditions typically used in slice experiments. NMDAR activity was also seen in synaptic recordings from hippocampal CA1 neurons, indicating a general property of NMDAR signaling. Using a fluorescent Ca²⁺ indicator, we measured responses to stimulation in layer 4 at individual synaptic sites, and Ca²⁺ influx could be detected even with AMPARs blocked. In current clamp recordings, we found that resting membrane potential was hyperpolarized by ∼7 mV and AP firing threshold depolarized by ∼4 mV in traditional compared to physiological ionic concentrations, and that NMDARs contribute to EPSPs at resting membrane potentials. These measurements demonstrate that, even in the presence of extracellular Mg²⁺ and absence of postsynaptic depolarization, NMDARs contribute to synaptic currents and Ca²⁺ influx.

A calcium-based plasticity model for predicting long-term potentiation and depression in the neocortex

Pyramidal cells (PCs) form the backbone of the layered structure of the neocortex, and plasticity of their synapses is thought to underlie learning in the brain. However, such long-term synaptic changes have been experimentally characterized between only a few types of PCs, posing a significant barrier for studying neocortical learning mechanisms. Here we introduce a model of synaptic plasticity based on data-constrained postsynaptic calcium dynamics, and show in a neocortical microcircuit model that a single parameter set is sufficient to unify the available experimental findings on long-term potentiation (LTP) and long-term depression (LTD) of PC connections. In particular, we find that the diverse plasticity outcomes across the different PC types can be explained by cell-type-specific synaptic physiology, cell morphology and innervation patterns, without requiring type-specific plasticity. Generalizing the model to in vivo extracellular calcium concentrations, we predict qualitatively different plasticity dynamics from those observed in vitro. This work provides a first comprehensive null model for LTP/LTD between neocortical PC types in vivo, and an open framework for further developing models of cortical synaptic plasticity.

Dendritic Excitability and Synaptic Plasticity In Vitro and In Vivo

Much of our understanding of dendritic and synaptic physiology comes from in vitro experimentation, where the afforded mechanical stability and convenience of applying drugs allowed patch-clamping based recording techniques to investigate ion channel distributions, their gating kinetics, and to uncover dendritic integrative and synaptic plasticity rules. However, with current efforts to study these questions in vivo, there is a great need to translate existing knowledge between in vitro and in vivo experimental conditions. In this review, we identify discrepancies between in vitro and in vivo ionic composition of extracellular media and discuss how changes in ionic composition alter dendritic excitability and plasticity induction. Here, we argue that under physiological in vivo ionic conditions, dendrites are expected to be more excitable and the threshold for synaptic plasticity induction to be lowered. Consequently, the plasticity rules described in vitro vary significantly from those implemented in vivo.

Frequency- and spike-timing-dependent mitochondrial Ca2+ signaling regulates the metabolic rate and synaptic efficacy in cortical

Mitochondrial activity is crucial for the plasticity of central synapses, but how the firing pattern of pre- and postsynaptic neurons affects the mitochondria remains elusive. We recorded changes in the fluorescence of cytosolic and mitochondrial Ca²⁺ indicators in cell bodies, axons, and dendrites of cortical pyramidal neurons in mouse brain slices while evoking pre- and postsynaptic spikes. Postsynaptic spike firing elicited fast mitochondrial Ca²⁺ responses that were about threefold larger in the somas and apical dendrites than in basal dendrites and axons. The amplitude of these responses and metabolic activity were extremely sensitive to the firing frequency. Furthermore, while an EPSP alone caused no detectable Ca²⁺ elevation in the dendritic mitochondria, the coincidence of EPSP with a backpropagating spike produced prominent, highly localized mitochondrial Ca²⁺ hotspots. Our results indicate that mitochondria decode the spike firing frequency and the Hebbian temporal coincidences into the Ca²⁺ signals, which are further translated into the metabolic output and most probably lead to long-term changes in synaptic efficacy.

Bidirectional synaptic plasticity rapidly modifies hippocampal representations

Learning requires neural adaptations thought to be mediated by activity-dependent synaptic plasticity. A relatively non-standard form of synaptic plasticity driven by dendritic calcium spikes, or plateau potentials, has been reported to underlie place field formation in rodent hippocampal CA1 neurons. Here, we found that this behavioral timescale synaptic plasticity (BTSP) can also reshape existing place fields via bidirectional synaptic weight changes that depend on the temporal proximity of plateau potentials to pre-existing place fields. When evoked near an existing place field, plateau potentials induced less synaptic potentiation and more depression, suggesting BTSP might depend inversely on postsynaptic activation. However, manipulations of place cell membrane potential and computational modeling indicated that this anti-correlation actually results from a dependence on current synaptic weight such that weak inputs potentiate and strong inputs depress. A network model implementing this bidirectional synaptic learning rule suggested that BTSP enables population activity, rather than pairwise neuronal correlations, to drive neural adaptations to experience.