Dendritic synapse location and neocortical spike-timing-dependent plasticity

Acetylcholine facilitates localized synaptic potentiation and location specific feature binding

Forebrain acetylcholine (ACh) signaling has been shown to drive attention and learning. Recent experimental evidence of spatially and temporally constrained cholinergic signaling has sparked interest to investigate how it facilitates stimulus-induced learning. We use biophysical excitatory-inhibitory (E-I) multi-module neural network models to show that external stimuli and ACh signaling can mediate spatially constrained synaptic potentiation patterns. The effects of ACh on neural excitability are simulated by varying the conductance of a muscarinic receptor-regulated hyperpolarizing slow K+ current (m-current). Each network module consists of an E-I network with local excitatory connectivity and global inhibitory connectivity. The modules are interconnected with plastic excitatory synaptic connections, that change via a spike-timing-dependent plasticity (STDP) rule. Our results indicate that spatially constrained ACh release influences the information flow represented by network dynamics resulting in selective reorganization of inter-module interactions. Moreover the information flow depends on the level of synchrony in the network. For highly synchronous networks, the more excitable module leads firing in the less excitable one resulting in strengthening of the outgoing connections from the former and weakening of its incoming synapses. For networks with more noisy firing patterns, activity in high ACh regions is prone to induce feedback firing of synchronous volleys and thus strengthening of the incoming synapses to the more excitable region and weakening of outgoing synapses. Overall, these results suggest that spatially and directionally specific plasticity patterns, as are presumed necessary for feature binding, can be mediated by spatially constrained ACh release.

Synapse-specific diversity of distinct postsynaptic GluN2 subtypes defines transmission strength in spinal lamina I

The unitary postsynaptic response to presynaptic quantal glutamate release is the fundamental basis of excitatory information transfer between neurons. The view, however, of individual glutamatergic synaptic connections in a population as homogenous, fixed-strength units of neural communication is becoming increasingly scrutinized. Here, we used minimal stimulation of individual glutamatergic afferent axons to evoke single synapse resolution postsynaptic responses from central sensory lamina I neurons in an ex vivo adult rat spinal slice preparation. We detected unitary events exhibiting a NMDA receptor component with distinct kinetic properties across synapses conferred by specific GluN2 subunit composition, indicative of GluN2 subtype-based postsynaptic heterogeneity. GluN2A, 2A and 2B, or 2B and 2D synaptic predominance functioned on distinct lamina I neuron types to narrowly, intermediately, or widely tune, respectively, the duration of evoked unitary depolarization events from resting membrane potential, which enabled individual synapses to grade differentially depolarizing steps during temporally patterned afferent input. Our results lead to a model wherein a core locus of proteomic complexity prevails at this central glutamatergic sensory synapse that involves distinct GluN2 subtype configurations. These findings have major implications for subthreshold integrative capacity and transmission strength in spinal lamina I and other CNS regions.

Nicotinic acetylcholine receptors and learning and memory deficits in Neuroinflammatory diseases

Animal survival depends on cognitive abilities such as learning and memory to adapt to environmental changes. Memory functions require an enhanced activity and connectivity of a particular arrangement of engram neurons, supported by the concerted action of neurons, glia, and vascular cells. The deterioration of the cholinergic system is a common occurrence in neurological conditions exacerbated by aging such as traumatic brain injury (TBI), posttraumatic stress disorder (PTSD), Alzheimer's disease (AD), and Parkinson's disease (PD). Cotinine is a cholinergic modulator with neuroprotective, antidepressant, anti-inflammatory, antioxidant, and memory-enhancing effects. Current evidence suggests Cotinine's beneficial effects on cognition results from the positive modulation of the α7-nicotinic acetylcholine receptors (nAChRs) and the inhibition of the toll-like receptors (TLRs). The α7nAChR affects brain functions by modulating the function of neurons, glia, endothelial, immune, and dendritic cells and regulates inhibitory and excitatory neurotransmission throughout the GABA interneurons. In addition, Cotinine acting on the α7 nAChRs and TLR reduces neuroinflammation by inhibiting the release of pro-inflammatory cytokines by the immune cells. Also, α7nAChRs stimulate signaling pathways supporting structural, biochemical, electrochemical, and cellular changes in the Central nervous system during the cognitive processes, including Neurogenesis. Here, the mechanisms of memory formation as well as potential mechanisms of action of Cotinine on memory preservation in aging and neurological diseases are discussed.

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

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

Neural synchrony in cortical networks: mechanisms and implications for neural information processing and coding

Synchronization of neuronal discharges on the millisecond scale has long been recognized as a prevalent and functionally important attribute of neural activity. In this article, I review classical concepts and corresponding evidence of the mechanisms that govern the synchronization of distributed discharges in cortical networks and relate those mechanisms to their possible roles in coding and cognitive functions. To accommodate the need for a selective, directed synchronization of cells, I propose that synchronous firing of distributed neurons is a natural consequence of spike-timing-dependent plasticity (STDP) that associates cells repetitively receiving temporally coherent input: the “synchrony through synaptic plasticity” hypothesis. Neurons that are excited by a repeated sequence of synaptic inputs may learn to selectively respond to the onset of this sequence through synaptic plasticity. Multiple neurons receiving coherent input could thus actively synchronize their firing by learning to selectively respond at corresponding temporal positions. The hypothesis makes several predictions: first, the position of the cells in the network, as well as the source of their input signals, would be irrelevant as long as their input signals arrive simultaneously; second, repeating discharge patterns should get compressed until all or some part of the signals are synchronized; and third, this compression should be accompanied by a sparsening of signals. In this way, selective groups of cells could emerge that would respond to some recurring event with synchronous firing. Such a learned response pattern could further be modulated by synchronous network oscillations that provide a dynamic, flexible context for the synaptic integration of distributed signals. I conclude by suggesting experimental approaches to further test this new hypothesis.

Analysis of the mechanism of synaptic integration focusing on the charge held in the spine

Successful synaptic integration is said to require that multiple excitatory postsynaptic potentials (EPSPs) occur almost simultaneously over a short period of time, so that they overlap and increase. However, if brain function is based on a chain of successful synaptic integrations, then constraints on the spacing of multiple EPSP generation must be released to allow for a higher probability of successful synaptic integration. This paper demonstrates that Ca²⁺ ions retained in spines after EPSP generation polarize spine neck fluid and dendritic fluid as a dielectric medium, that polarization is transmitted through dendrites to the cell body (soma), that polarization is enhanced by the addition of polarization from each spine, and that I propose that synaptic integration is successful when the membrane potential, as determined by the enhanced polarization and membrane capacitance, reaches the threshold of voltage-gated Na⁺ channels. Furthermore, the approach taken in this study suggests that a single neuron can integrate synapses for many combinations of synaptic inputs, that successful synaptic integration depends on spine neck capacitance and spine head size, and that spines farther from the soma are able to contribute to successful synaptic integration, and led to the elucidation of a number of important issues, including the fact that inhibitory post-synapses on dendrites suppress s effectively synaptic integration.

Role of NMDAR plasticity in a computational model of synaptic memory

A largely unexplored question in neuronal plasticity is whether synapses are capable of encoding and learning the timing of synaptic inputs. We address this question in a computational model of synaptic input time difference learning (SITDL), where N-methyl-d-aspartate receptor (NMDAR) isoform expression in silent synapses is affected by time differences between glutamate and voltage signals. We suggest that differences between NMDARs' glutamate and voltage gate conductances induce modifications of the synapse's NMDAR isoform population, consequently changing the timing of synaptic response. NMDAR expression at individual synapses can encode the precise time difference between signals. Thus, SITDL enables the learning and reconstruction of signals across multiple synapses of a single neuron. In addition to plausibly predicting the roles of NMDARs in synaptic plasticity, SITDL can be usefully applied in artificial neural network models.

Alternation of Neuronal Feature Selectivity Induced by Paired Optogenetic-Mechanical Stimulation in the Barrel Cortex

Paired stimulation has been applied to modulate neuronal functions in the primary somatosensory cortex but its utility in the alternation of tuning function, such as direction tuning for whisker stimuli, remains unclear. In the present study, we attempted to manipulate feature preferences in barrel cortical neurons using repetitive paired whisker deflection combined with optogenetic stimulation and to obtain optimal parameters that can induce neuroplasticity. We found no significant response changes across stimulus parameters, such as onset asynchronies and paired directions. Only when paired stimulation was applied in the nonpreferred direction of the principal whisker of a neuron, were the neuron’s responses enhanced in that direction. Importantly, this effect was only observed when the optogenetic stimulus preceded the mechanical stimulus. Our findings indicate that repetitive paired optogenetic-mechanical stimulation can induce in vivo neuroplasticity of feature selectivity in limited situations.

Neurostimulation for Stroke Rehabilitation

Neurological injuries such as strokes can lead to important loss in motor function. Thanks to neuronal plasticity, some of the lost functionality may be recovered over time. However, the recovery process is often slow and incomplete, despite the most effective conventional rehabilitation therapies. As we improve our understanding of the rules governing activity-dependent plasticity, neuromodulation interventions are being developed to harness neural plasticity to achieve faster and more complete recovery. Here, we review the principles underlying stimulation-driven plasticity as well as the most commonly used stimulation techniques and approaches. We argue that increased spatiotemporal precision is an important factor to improve the efficacy of neurostimulation and drive a more useful neuronal reorganization. Consequently, closed-loop systems and optogenetic stimulation hold theoretical promise as interventions to promote brain repair after stroke.

A spike-timing-dependent plasticity rule for dendritic spines

The structural organization of excitatory inputs supporting spike-timing-dependent plasticity (STDP) remains unknown. We performed a spine STDP protocol using two-photon (2P) glutamate uncaging (pre) paired with postsynaptic spikes (post) in layer 5 pyramidal neurons from juvenile mice. Here we report that pre-post pairings that trigger timing-dependent LTP (t-LTP) produce shrinkage of the activated spine neck and increase in synaptic strength; and post-pre pairings that trigger timing-dependent LTD (t-LTD) decrease synaptic strength without affecting spine shape. Furthermore, the induction of t-LTP with 2P glutamate uncaging in clustered spines (<5 μm apart) enhances LTP through a NMDA receptor-mediated spine calcium accumulation and actin polymerization-dependent neck shrinkage, whereas t-LTD was dependent on NMDA receptors and disrupted by the activation of clustered spines but recovered when separated by >40 μm. These results indicate that synaptic cooperativity disrupts t-LTD and extends the temporal window for the induction of t-LTP, leading to STDP only encompassing LTP.

Heterosynaptic Plasticity Determines the Set Point for Cortical Excitatory-Inhibitory Balance

Excitation in neural circuits must be carefully controlled by inhibition to regulate information processing and network excitability. During development, cortical inhibitory and excitatory inputs are initially mismatched but become co-tuned or balanced with experience. However, little is known about how excitatory-inhibitory balance is defined at most synapses or about the mechanisms for establishing or maintaining this balance at specific set points. Here we show how coordinated long-term plasticity calibrates populations of excitatory-inhibitory inputs onto mouse auditory cortical pyramidal neurons. Pairing pre- and postsynaptic activity induced plasticity at paired inputs and different forms of heterosynaptic plasticity at the strongest unpaired synapses, which required minutes of activity and dendritic Ca²⁺ signaling to be computed. Theoretical analyses demonstrated how the relative rate of heterosynaptic plasticity could normalize and stabilize synaptic strengths to achieve any possible excitatory-inhibitory correlation. Thus, excitatory-inhibitory balance is dynamic and cell specific, determined by distinct plasticity rules across multiple excitatory and inhibitory synapses.

Cognitive Enhancement via Network-Targeted Cortico-cortical Associative Brain Stimulation

Fluid intelligence (gf) represents a crucial component of human cognition, as it correlates with academic achievement, successful aging, and longevity. However, it has strong resilience against enhancement interventions, making the identification of gf enhancement approaches a key unmet goal of cognitive neuroscience. Here, we applied a spike-timing-dependent plasticity (STDP)-inducing brain stimulation protocol, named cortico-cortical paired associative stimulation (cc-PAS), to modulate gf in 29 healthy young subjects (13 females-mean ± standard deviation, 25.43 years ± 3.69), based on dual-coil transcranial magnetic stimulation (TMS). Pairs of neuronavigated TMS pulses (10-ms interval) were delivered over two frontoparietal nodes of the gf network, based on individual functional magnetic resonance imaging data and in accordance with cognitive models of information processing across the prefrontal and parietal lobe. cc-PAS enhanced accuracy at gf tasks, with parieto-frontal and fronto-parietal stimulation significantly increasing logical and relational reasoning, respectively. Results suggest the possibility of using SPTD-inducing TMS protocols to causally validate cognitive models by selectively engaging relevant networks and manipulating inter-regional temporal dynamics supporting specific cognitive functions.

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

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

Neural correlates of sparse coding and dimensionality reduction

Supported by recent computational studies, there is increasing evidence that a wide range of neuronal responses can be understood as an emergent property of nonnegative sparse coding (NSC), an efficient population coding scheme based on dimensionality reduction and sparsity constraints. We review evidence that NSC might be employed by sensory areas to efficiently encode external stimulus spaces, by some associative areas to conjunctively represent multiple behaviorally relevant variables, and possibly by the basal ganglia to coordinate movement. In addition, NSC might provide a useful theoretical framework under which to understand the often complex and nonintuitive response properties of neurons in other brain areas. Although NSC might not apply to all brain areas (for example, motor or executive function areas) the success of NSC-based models, especially in sensory areas, warrants further investigation for neural correlates in other regions.

A structurally derived model of subunit-dependent NMDA receptor function

Key points

The kinetics of NMDA receptor (NMDAR) signalling are a critical aspect of the physiology of excitatory synaptic transmission in the brain.

Here we develop a mechanistic description of NMDAR function based on the receptor tetrameric structure and the principle that each agonist‐bound subunit must undergo some rate‐limiting conformational change after agonist binding, prior to channel opening.

By fitting this mechanism to single channel data using a new MATLAB‐based software implementation of maximum likelihood fitting with correction for limited time resolution, rate constants were derived for this mechanism that reflect distinct structural changes and predict the properties of macroscopic and synaptic NMDAR currents.

The principles applied here to develop a mechanistic description of the heterotetrameric NMDAR, and the software used in this analysis, can be equally applied to other heterotetrameric glutamate receptors, providing a unifying mechanistic framework to understanding the physiology of glutamate receptor signalling in the brain.

Abstract

NMDA receptors (NMDARs) are tetrameric complexes comprising two glycine‐binding GluN1 and two glutamate‐binding GluN2 subunits. Four GluN2 subunits encoded by different genes can produce up to 10 different di‐ and triheteromeric receptors. In addition, some neurological patients contain a de novo mutation or inherited rare variant in only one subunit. There is currently no mechanistic framework to describe tetrameric receptor function that can be extended to receptors with two different GluN1 or GluN2 subunits. Here we use the structural features of glutamate receptors to develop a mechanism describing both single channel and macroscopic NMDAR currents. We propose that each agonist‐bound subunit undergoes some rate‐limiting conformational change after agonist binding, prior to channel opening. We hypothesize that this conformational change occurs within a triad of interactions between a short helix preceding the M1 transmembrane helix, the highly conserved M3 motif encoded by the residues SYTANLAAF, and the linker preceding the M4 transmembrane helix of the adjacent subunit. Molecular dynamics simulations suggest that pre‐M1 helix motion is uncorrelated between subunits, which we interpret to suggest independent subunit‐specific conformational changes may influence these pre‐gating steps. According to this interpretation, these conformational changes are the main determinants of the key kinetic properties of NMDA receptor activation following agonist binding, and so these steps sculpt their physiological role. We show that this structurally derived tetrameric model describes both single channel and macroscopic data, giving a new approach to interpreting functional properties of synaptic NMDARs that provides a logical framework to understanding receptors with non‐identical subunits.

The kinetics of NMDA receptor (NMDAR) signalling are a critical aspect of the physiology of excitatory synaptic transmission in the brain.

Here we develop a mechanistic description of NMDAR function based on the receptor tetrameric structure and the principle that each agonist‐bound subunit must undergo some rate‐limiting conformational change after agonist binding, prior to channel opening.

By fitting this mechanism to single channel data using a new MATLAB‐based software implementation of maximum likelihood fitting with correction for limited time resolution, rate constants were derived for this mechanism that reflect distinct structural changes and predict the properties of macroscopic and synaptic NMDAR currents.

The principles applied here to develop a mechanistic description of the heterotetrameric NMDAR, and the software used in this analysis, can be equally applied to other heterotetrameric glutamate receptors, providing a unifying mechanistic framework to understanding the physiology of glutamate receptor signalling in the brain.

Dynamic Hebbian Cross-Correlation Learning Resolves the Spike Timing Dependent Plasticity Conundrum

Spike Timing-Dependent Plasticity has been found to assume many different forms. The classic STDP curve, with one potentiating and one depressing window, is only one of many possible curves that describe synaptic learning using the STDP mechanism. It has been shown experimentally that STDP curves may contain multiple LTP and LTD windows of variable width, and even inverted windows. The underlying STDP mechanism that is capable of producing such an extensive, and apparently incompatible, range of learning curves is still under investigation. In this paper, it is shown that STDP originates from a combination of two dynamic Hebbian cross-correlations of local activity at the synapse. The correlation of the presynaptic activity with the local postsynaptic activity is a robust and reliable indicator of the discrepancy between the presynaptic neuron and the postsynaptic neuron's activity. The second correlation is between the local postsynaptic activity with dendritic activity which is a good indicator of matching local synaptic and dendritic activity. We show that this simple time-independent learning rule can give rise to many forms of the STDP learning curve. The rule regulates synaptic strength without the need for spike matching or other supervisory learning mechanisms. Local differences in dendritic activity at the synapse greatly affect the cross-correlation difference which determines the relative contributions of different neural activity sources. Dendritic activity due to nearby synapses, action potentials, both forward and back-propagating, as well as inhibitory synapses will dynamically modify the local activity at the synapse, and the resulting STDP learning rule. The dynamic Hebbian learning rule ensures furthermore, that the resulting synaptic strength is dynamically stable, and that interactions between synapses do not result in local instabilities. The rule clearly demonstrates that synapses function as independent localized computational entities, each contributing to the global activity, not in a simply linear fashion, but in a manner that is appropriate to achieve local and global stability of the neuron and the entire dendritic structure.

Modeling somatic and dendritic spike mediated plasticity at the single neuron and network level

Synaptic plasticity is thought to be the principal neuronal mechanism underlying learning. Models of plastic networks typically combine point neurons with spike-timing-dependent plasticity (STDP) as the learning rule. However, a point neuron does not capture the local non-linear processing of synaptic inputs allowed for by dendrites. Furthermore, experimental evidence suggests that STDP is not the only learning rule available to neurons. By implementing biophysically realistic neuron models, we study how dendrites enable multiple synaptic plasticity mechanisms to coexist in a single cell. In these models, we compare the conditions for STDP and for synaptic strengthening by local dendritic spikes. We also explore how the connectivity between two cells is affected by these plasticity rules and by different synaptic distributions. Finally, we show that how memory retention during associative learning can be prolonged in networks of neurons by including dendrites.

Synaptic plasticity is the neuronal mechanism underlying learning. Here the authors construct biophysical models of pyramidal neurons that reproduce observed plasticity gradients along the dendrite and show that dendritic spike dependent LTP which is predominant in distal sections can prolong memory retention.

Demonstrating Hybrid Learning in a Flexible Neuromorphic Hardware System

We present results from a new approach to learning and plasticity in neuromorphic hardware systems: to enable flexibility in implementable learning mechanisms while keeping high efficiency associated with neuromorphic implementations, we combine a general-purpose processor with full-custom analog elements. This processor is operating in parallel with a fully parallel neuromorphic system consisting of an array of synapses connected to analog, continuous time neuron circuits. Novel analog correlation sensor circuits process spike events for each synapse in parallel and in real-time. The processor uses this pre-processing to compute new weights possibly using additional information following its program. Therefore, to a certain extent, learning rules can be defined in software giving a large degree of flexibility. Synapses realize correlation detection geared towards Spike-Timing Dependent Plasticity (STDP) as central computational primitive in the analog domain. Operating at a speed-up factor of 1000 compared to biological time-scale, we measure time-constants from tens to hundreds of micro-seconds. We analyze variability across multiple chips and demonstrate learning using a multiplicative STDP rule. We conclude that the presented approach will enable flexible and efficient learning as a platform for neuroscientific research and technological applications.

Stability and Competition in Multi-spike Models of Spike-Timing Dependent Plasticity

Spike-timing dependent plasticity (STDP) is a widespread plasticity mechanism in the nervous system. The simplest description of STDP only takes into account pairs of pre- and postsynaptic spikes, with potentiation of the synapse when a presynaptic spike precedes a postsynaptic spike and depression otherwise. In light of experiments that explored a variety of spike patterns, the pair-based STDP model has been augmented to account for multiple pre- and postsynaptic spike interactions. As a result, a number of different "multi-spike" STDP models have been proposed based on different experimental observations. The behavior of these models at the population level is crucial for understanding mechanisms of learning and memory. The challenging balance between the stability of a population of synapses and their competitive modification is well studied for pair-based models, but it has not yet been fully analyzed for multi-spike models. Here, we address this issue through numerical simulations of an integrate-and-fire model neuron with excitatory synapses subject to STDP described by three different proposed multi-spike models. We also analytically calculate average synaptic changes and fluctuations about these averages. Our results indicate that the different multi-spike models behave quite differently at the population level. Although each model can produce synaptic competition in certain parameter regions, none of them induces synaptic competition with its originally fitted parameters. The dichotomy between synaptic stability and Hebbian competition, which is well characterized for pair-based STDP models, persists in multi-spike models. However, anti-Hebbian competition can coexist with synaptic stability in some models. We propose that the collective behavior of synaptic plasticity models at the population level should be used as an additional guideline in applying phenomenological models based on observations of single synapses.

Dendritic sodium spikes are required for long-term potentiation at distal synapses on hippocampal pyramidal neurons

Dendritic integration of synaptic inputs mediates rapid neural computation as well as longer-lasting plasticity. Several channel types can mediate dendritically initiated spikes (dSpikes), which may impact information processing and storage across multiple timescales; however, the roles of different channels in the rapid vs long-term effects of dSpikes are unknown. We show here that dSpikes mediated by Nav channels (blocked by a low concentration of TTX) are required for long-term potentiation (LTP) in the distal apical dendrites of hippocampal pyramidal neurons. Furthermore, imaging, simulations, and buffering experiments all support a model whereby fast Nav channel-mediated dSpikes (Na-dSpikes) contribute to LTP induction by promoting large, transient, localized increases in intracellular calcium concentration near the calcium-conducting pores of NMDAR and L-type Cav channels. Thus, in addition to contributing to rapid neural processing, Na-dSpikes are likely to contribute to memory formation via their role in long-lasting synaptic plasticity.

Plasticity of cortical excitatory-inhibitory balance

Synapses are highly plastic and are modified by changes in patterns of neural activity or sensory experience. Plasticity of cortical excitatory synapses is thought to be important for learning and memory, leading to alterations in sensory representations and cognitive maps. However, these changes must be coordinated across other synapses within local circuits to preserve neural coding schemes and the organization of excitatory and inhibitory inputs, i.e., excitatory-inhibitory balance. Recent studies indicate that inhibitory synapses are also plastic and are controlled directly by a large number of neuromodulators, particularly during episodes of learning. Many modulators transiently alter excitatory-inhibitory balance by decreasing inhibition, and thus disinhibition has emerged as a major mechanism by which neuromodulation might enable long-term synaptic modifications naturally. This review examines the relationships between neuromodulation and synaptic plasticity, focusing on the induction of long-term changes that collectively enhance cortical excitatory-inhibitory balance for improving perception and behavior.

Depression biased non-Hebbian spike-timing-dependent synaptic plasticity in the rat subiculum

The subiculum is a structure that forms a bridge between the hippocampus and the entorhinal cortex (EC), and plays a major role in the memory consolidation process. Here, we demonstrate spike-timing-dependent plasticity (STDP) at the proximal excitatory inputs on the subicular pyramidal neurons of juvenile rat. Causal (positive) pairing of a single EPSP with a single back-propagating action potential (bAP) after a time interval of 10 ms (+10 ms) failed to induce plasticity. However, increasing the number of bAPs in a burst to three, at two different frequencies of 50 Hz (bAP burst) and 150 Hz, induced long-term depression (LTD) after a time interval of +10 ms in both the regular-firing (RF), and the weak burst firing (WBF) neurons. The LTD amplitude decreased with increasing time interval between the EPSP and the bAP burst. Reversing the order of the pairing of the EPSP and the bAP burst induced LTP at a time interval of -10 ms. This finding is in contrast with reports at other synapses, wherein pre- before postsynaptic (causal) pairing induced LTP and vice versa. Our results reaffirm the earlier observations that the relative timing of the pre- and postsynaptic activities can lead to multiple types of plasticity profiles. The induction of timing-dependent LTD (t-LTD) was dependent on postsynaptic calcium change via NMDA receptors in the WBF neurons, while it was independent of postsynaptic calcium change, but required active L-type calcium channels in the RF neurons. Thus the mechanism of synaptic plasticity may vary within a hippocampal subfield depending on the postsynaptic neuron involved. This study also reports a novel mechanism of LTD induction, where L-type calcium channels are involved in a presynaptically induced synaptic plasticity. The findings may have strong implications in the memory consolidation process owing to the central role of the subiculum and LTD in this process.

Biologically inspired load balancing mechanism in neocortical competitive learning

A unique delayed self-inhibitory pathway mediated by layer 5 Martinotti Cells was studied in a biologically inspired neural network simulation. Inclusion of this pathway along with layer 5 basket cell lateral inhibition caused balanced competitive learning, which led to the formation of neuronal clusters as were indeed reported in the same region. Martinotti pathway proves to act as a learning “conscience,” causing overly successful regions in the network to restrict themselves and let others fire. It thus spreads connectivity more evenly throughout the net and solves the “dead unit” problem of clustering algorithms in a local and biologically plausible manner.

Target-cell-specific short-term plasticity in local circuits

Short-term plasticity (STP) denotes changes in synaptic strength that last up to tens of seconds. It is generally thought that STP impacts information transfer across synaptic connections and may thereby provide neurons with, for example, the ability to detect input coherence, to maintain stability and to promote synchronization. STP is due to a combination of mechanisms, including vesicle depletion and calcium accumulation in synaptic terminals. Different forms of STP exist, depending on many factors, including synapse type. Recent evidence shows that synapse dependence holds true even for connections that originate from a single presynaptic cell, which implies that postsynaptic target cell type can determine synaptic short-term dynamics. This arrangement is surprising, since STP itself is chiefly due to presynaptic mechanisms. Target-specific synaptic dynamics in addition imply that STP is not a bug resulting from synapses fatiguing when driven too hard, but rather a feature that is selectively implemented in the brain for specific functional purposes. As an example, target-specific STP results in sequential somatic and dendritic inhibition in neocortical and hippocampal excitatory cells during high-frequency firing. Recent studies also show that the Elfn1 gene specifically controls STP at some synapse types. In addition, presynaptic NMDA receptors have been implicated in synapse-specific control of synaptic dynamics during high-frequency activity. We argue that synapse-specific STP deserves considerable further study, both experimentally and theoretically, since its function is not well known. We propose that synapse-specific STP has to be understood in the context of the local circuit, which requires combining different scientific disciplines ranging from molecular biology through electrophysiology to computer modeling.

The adaptation of spike backpropagation delays in cortical neurons

We measured the action potential backpropagation delays in apical dendrites of layer V pyramidal neurons of the somatosensory cortex under different stimulation regimes that exclude synaptic involvement. These delays showed robust features and did not correlate to either transient change in the stimulus strength or low frequency stimulation of suprathreshold membrane oscillations. However, our results indicate that backpropagation delays correlate with high frequency (>10 Hz) stimulation of membrane oscillations, and that persistent suprathreshold sinusoidal stimulation injected directly into the soma results in an increase of the backpropagation delay, suggesting an intrinsic adaptation of the backpropagating action potential (bAP), which does not involve any synaptic modifications. Moreover, the calcium chelator BAPTA eliminated the alterations in the backpropagation delays, strengthening the hypothesis that increased calcium concentration in the dendrites modulates dendritic excitability and can impact the backpropagation velocity. These results emphasize the impact of dendritic excitability on bAP velocity along the dendritic tree, which affects the precision of the bAP arrival at the synapse during specific stimulus regimes, and is capable of shifting the extent and polarity of synaptic strength during suprathreshold synaptic processes such as spike time-dependent plasticity.

Paired associative stimulation enforces the communication between interconnected areas

Paired associative stimulation (PAS) protocols induce forms of spike-timing-dependent-plasticity (STDP) when paired pulses are repeatedly applied with different timing over interconnected cortical areas such as the posterior parietal cortex (PPC) and the primary motor cortex (M1). However, the assessment of PAS effects is usually limited to M1 through recording of motor-evoked potential (MEP) amplitude. Here, by combining transcranial magnetic stimulation (TMS) with EEG we aimed at investigating PAS effects over both areas (PPC, M1) and the modulation induced on their connectivity in humans. In different PAS conditions, PPC preceded or followed M1 TMS by 5 ms. We found that TMS-evoked potentials (TEPs) changed differently according to the long-term depression (LTD) or potentiation (LTP) after-effect assessed by MEPs, but did not vary at PPC level. Moreover, there was no change in local oscillatory power. However, there was a remarkable increase of coherence between the PPC and the M1 areas. When the PAS protocol induced LTD as revealed by MEPs, there was a specific increase of the coherence between PPC and M1 within the beta band. On the contrary, when PAS induced LTP, the coherence crucially increased in the alpha band. The same LTP results were confirmed when rotating the stimulating coil in M1 during the PAS protocol. In conclusion, we report new evidence that opposite STDP-like effects induced by corticocortical PAS are associated with increased communication between involved brain areas and that antithetic forms of STDP-like after-effects result in distinct cortical rhythms connectivity changes.

Modulation of distal calcium electrogenesis by neuropeptide Y₁ receptors inhibits neocortical long-term depression

In layer 5 neocortical pyramidal neurons, backpropagating action potentials (bAPs) firing at rates above a critical frequency (CF) induce supralinear Ca²⁺ influx and regenerative potentials in apical dendrites. Paired temporally with an EPSP, this Ca²⁺ influx can result in synaptic plasticity. We studied the actions of neuropeptide Y (NPY), an abundant neocortical neuropeptide, on Ca²⁺ influx in layer 5 pyramidal neurons of somatosensory neocortex in Sprague Dawley and Wistar rats, using a combination of somatic and dendritic intracellular recordings and simultaneous Ca²⁺ imaging. Ca²⁺ influx induced by trains of bAPs above a neuron's CF was inhibited by NPY, acting only at the distal dendrite, via Y₁ receptors. NPY does not affect evoked synaptic glutamate release, paired synaptic facilitation, or synaptic rundown in longer trains. Extracellular Cs⁺ did not prevent NPY's postsynaptic effects, suggesting it does not act via either G-protein-activated inwardly rectifying K⁺ conductance (G(IRK)) or hyperpolarization-activated, cyclic nucleotide-gated channels. NPY application suppresses the induction of the long-term depression (LTD) normally caused by pairing 100 EPSPs with bursts of 2 bAPs evoked at a supracritical frequency. These findings suggest that distal dendritic Ca²⁺ influx is necessary for LTD induction, and selective inhibition of this distal dendritic Ca²⁺ influx by NPY can thus regulate synaptic plasticity in layer 5 pyramidal neurons.

GABAergic circuits control spike-timing-dependent plasticity

The spike-timing-dependent plasticity (STDP), a synaptic learning rule for encoding learning and memory, relies on relative timing of neuronal activity on either side of the synapse. GABAergic signaling has been shown to control neuronal excitability and consequently the spike timing, but whether GABAergic circuits rule the STDP remained unknown. Here we show that GABAergic signaling governs the polarity of STDP, because blockade of GABAA receptors was able to completely reverse the temporal order of plasticity at corticostriatal synapses in rats and mice. GABA controls the polarity of STDP in both striatopallidal and striatonigral output neurons. Biophysical simulations and experimental investigations suggest that GABA controls STDP polarity through depolarizing effects at distal dendrites of striatal output neurons by modifying the balance of two calcium sources, NMDARs and voltage-sensitive calcium channels. These findings establish a central role for GABAergic circuits in shaping STDP and suggest that GABA could operate as a Hebbian/anti-Hebbian switch.

Stable learning of functional maps in self-organizing spiking neural networks with continuous synaptic plasticity

This study describes a spiking model that self-organizes for stable formation and maintenance of orientation and ocular dominance maps in the visual cortex (V1). This self-organization process simulates three development phases: an early experience-independent phase, a late experience-independent phase and a subsequent refinement phase during which experience acts to shape the map properties. The ocular dominance maps that emerge accommodate the two sets of monocular inputs that arise from the lateral geniculate nucleus (LGN) to layer 4 of V1. The orientation selectivity maps that emerge feature well-developed iso-orientation domains and fractures. During the last two phases of development the orientation preferences at some locations appear to rotate continuously through ±180° along circular paths and referred to as pinwheel-like patterns but without any corresponding point discontinuities in the orientation gradient maps. The formation of these functional maps is driven by balanced excitatory and inhibitory currents that are established via synaptic plasticity based on spike timing for both excitatory and inhibitory synapses. The stability and maintenance of the formed maps with continuous synaptic plasticity is enabled by homeostasis caused by inhibitory plasticity. However, a prolonged exposure to repeated stimuli does alter the formed maps over time due to plasticity. The results from this study suggest that continuous synaptic plasticity in both excitatory neurons and interneurons could play a critical role in the formation, stability, and maintenance of functional maps in the cortex.

Pairwise analysis can account for network structures arising from spike-timing dependent plasticity

Spike timing-dependent plasticity (STDP) modifies synaptic strengths based on timing information available locally at each synapse. Despite this, it induces global structures within a recurrently connected network. We study such structures both through simulations and by analyzing the effects of STDP on pair-wise interactions of neurons. We show how conventional STDP acts as a loop-eliminating mechanism and organizes neurons into in- and out-hubs. Loop-elimination increases when depression dominates and turns into loop-generation when potentiation dominates. STDP with a shifted temporal window such that coincident spikes cause depression enhances recurrent connections and functions as a strict buffering mechanism that maintains a roughly constant average firing rate. STDP with the opposite temporal shift functions as a loop eliminator at low rates and as a potent loop generator at higher rates. In general, studying pairwise interactions of neurons provides important insights about the structures that STDP can produce in large networks.

Dark exposure extends the integration window for spike-timing-dependent plasticity

Metaplasticity, the adaptive changes of long-term potentiation (LTP) and long-term depression (LTD) in response to fluctuations in neural activity is well documented in visual cortex, where dark rearing shifts the frequency threshold for the induction of LTP and LTD. Here we studied metaplasticity affecting spike-timing-dependent plasticity, in which the polarity of plasticity is determined not by the stimulation frequency, but by the temporal relationship between near-coincidental presynaptic and postsynaptic firing. We found that in mouse visual cortex the same regime of deprivation that restricts the frequency range for inducing rate-dependent LTD extends the integration window for inducing timing-dependent LTD, enabling LTD induction with random presynaptic and postsynaptic firing. Notably, the underlying mechanism for the changes in both rate-dependent and time-dependent LTD appears to be an increase of NR2b-containing NMDAR at the synapse. Thus, the rules of metaplasticity might manifest in opposite directions, depending on the plasticity-induction paradigms.

The spike-timing dependence of plasticity

In spike-timing-dependent plasticity (STDP), the order and precise temporal interval between presynaptic and postsynaptic spikes determine the sign and magnitude of long-term potentiation (LTP) or depression (LTD). STDP is widely utilized in models of circuit-level plasticity, development, and learning. However, spike timing is just one of several factors (including firing rate, synaptic cooperativity, and depolarization) that govern plasticity induction, and its relative importance varies across synapses and activity regimes. This review summarizes this broader view of plasticity, including the forms and cellular mechanisms for the spike-timing dependence of plasticity, and, the evidence that spike timing is an important determinant of plasticity in vivo.

Dopamine Modulates Spike Timing-Dependent Plasticity and Action Potential Properties in CA1 Pyramidal Neurons of Acute Rat Hippocampal Slices

Spike timing-dependent plasticity (STDP) is a cellular model of Hebbian synaptic plasticity which is believed to underlie memory formation. In an attempt to establish a STDP paradigm in CA1 of acute hippocampal slices from juvenile rats (P15–20), we found that changes in excitability resulting from different slice preparation protocols correlate with the success of STDP induction. Slice preparation with sucrose containing ACSF prolonged rise time, reduced frequency adaptation, and decreased latency of action potentials in CA1 pyramidal neurons compared to preparation in conventional ASCF, while other basal electrophysiological parameters remained unaffected. Whereas we observed prominent timing-dependent long-term potentiation (t-LTP) to 171 ± 10% of controls in conventional ACSF, STDP was absent in sucrose prepared slices. This sucrose-induced STDP deficit could not be rescued by stronger STDP paradigms, applying either more pre- and/or postsynaptic stimuli, or by a higher stimulation frequency. Importantly, slice preparation with sucrose containing ACSF did not eliminate theta-burst stimulation induced LTP in CA1 in field potential recordings in our rat hippocampal slices. Application of dopamine (for 10–20 min) to sucrose prepared slices completely rescued t-LTP and recovered action potential properties back to levels observed in ACSF prepared slices. Conversely, acute inhibition of D1 receptor signaling impaired t-LTP in ACSF prepared slices. No similar restoring effect for STDP as seen with dopamine was observed in response to the β-adrenergic agonist isoproterenol. ELISA measurements demonstrated a significant reduction of endogenous dopamine levels (to 61.9 ± 6.9% of ACSF values) in sucrose prepared slices. These results suggest that dopamine signaling is involved in regulating the efficiency to elicit STDP in CA1 pyramidal neurons.

Optophysiological approach to resolve neuronal action potentials with high spatial and temporal resolution in cultured neurons

Cell to cell communication in the central nervous system is encoded into transient and local membrane potential changes (ΔVm). Deciphering the rules that govern synaptic transmission and plasticity entails to be able to perform V_m recordings throughout the entire neuronal arborization. Classical electrophysiology is, in most cases, not able to do so within small and fragile neuronal subcompartments. Thus, optical techniques based on the use of fluorescent voltage-sensitive dyes (VSDs) have been developed. However, reporting spontaneous or small ΔV_m from neuronal ramifications has been challenging, in part due to the limited sensitivity and phototoxicity of VSD-based optical measurements. Here we demonstrate the use of water soluble VSD, ANNINE-6plus, with laser-scanning microscopy to optically record ΔV_m in cultured neurons. We show that the sensitivity (>10% of fluorescence change for 100 mV depolarization) and time response (sub millisecond) of the dye allows the robust detection of action potentials (APs) even without averaging, allowing the measurement of spontaneous neuronal firing patterns. In addition, we show that back-propagating APs can be recorded, along distinct dendritic sites and within dendritic spines. Importantly, our approach does not induce any detectable phototoxic effect on cultured neurons. This optophysiological approach provides a simple, minimally invasive, and versatile optical method to measure electrical activity in cultured neurons with high temporal (ms) resolution and high spatial (μm) resolution.

Presynaptic ionotropic receptors controlling and modulating the rules for spike timing-dependent plasticity

Throughout life, activity-dependent changes in neuronal connection strength enable the brain to refine neural circuits and learn based on experience. In line with predictions made by Hebb, synapse strength can be modified depending on the millisecond timing of action potential firing (STDP). The sign of synaptic plasticity depends on the spike order of presynaptic and postsynaptic neurons. Ionotropic neurotransmitter receptors, such as NMDA receptors and nicotinic acetylcholine receptors, are intimately involved in setting the rules for synaptic strengthening and weakening. In addition, timing rules for STDP within synapses are not fixed. They can be altered by activation of ionotropic receptors located at, or close to, synapses. Here, we will highlight studies that uncovered how network actions control and modulate timing rules for STDP by activating presynaptic ionotropic receptors. Furthermore, we will discuss how interaction between different types of ionotropic receptors may create "timing" windows during which particular timing rules lead to synaptic changes.

A history of spike-timing-dependent plasticity

How learning and memory is achieved in the brain is a central question in neuroscience. Key to today's research into information storage in the brain is the concept of synaptic plasticity, a notion that has been heavily influenced by Hebb's (1949) postulate. Hebb conjectured that repeatedly and persistently co-active cells should increase connective strength among populations of interconnected neurons as a means of storing a memory trace, also known as an engram. Hebb certainly was not the first to make such a conjecture, as we show in this history. Nevertheless, literally thousands of studies into the classical frequency-dependent paradigm of cellular learning rules were directly inspired by the Hebbian postulate. But in more recent years, a novel concept in cellular learning has emerged, where temporal order instead of frequency is emphasized. This new learning paradigm - known as spike-timing-dependent plasticity (STDP) - has rapidly gained tremendous interest, perhaps because of its combination of elegant simplicity, biological plausibility, and computational power. But what are the roots of today's STDP concept? Here, we discuss several centuries of diverse thinking, beginning with philosophers such as Aristotle, Locke, and Ribot, traversing, e.g., Lugaro's plasticità and Rosenblatt's perceptron, and culminating with the discovery of STDP. We highlight interactions between theoretical and experimental fields, showing how discoveries sometimes occurred in parallel, seemingly without much knowledge of the other field, and sometimes via concrete back-and-forth communication. We point out where the future directions may lie, which includes interneuron STDP, the functional impact of STDP, its mechanisms and its neuromodulatory regulation, and the linking of STDP to the developmental formation and continuous plasticity of neuronal networks.

On the classification of pathways in the auditory midbrain, thalamus, and cortex

Auditory forebrain pathways exhibit several morphological and physiological properties that underlie their specific neurobiological roles in auditory processing. Anatomically, such projections can be distinguished by their terminal size, arborization patterns, and postsynaptic dendritic locations. These structural features correlate with several postsynaptic physiological properties, such as EPSP amplitude, short-term plasticity, and postsynaptic receptor types. Altogether, these synaptic properties segregate into two main classes that are associated with either primarily information-bearing (Class 1) or modulatory (Class 2) roles, and have been used to delineate the principle routes of information flow through the auditory midbrain, thalamus, and cortex. Moreover, these synaptic properties engender as yet unexplored issues regarding the neuronal processing of auditory information, such as the convergent integration and long-term plasticity of auditory forebrain inputs.

Questions about STDP as a General Model of Synaptic Plasticity

According to spike-timing-dependent plasticity (STDP), the timing of the Na⁺ spike relative to the EPSP determines whether LTP or LTD will occur. Here, we review our reservations about STDP. Most investigations of this process have been done under conditions in which the spike is evoked by postsynaptic current injection. Under more realistic conditions, in which the spike is evoked by the EPSP, the results do not generally support STDP. For instance, low-frequency stimulation of a group of synapses can cause LTD, not the LTP predicted by the pre-before-post sequence in STDP; this is true regardless of whether or not the EPSP is large enough to produce a Na⁺ spike. With stronger or more frequent stimulation, LTP can be induced by the same pre-before-post timing, but in this case block of Na⁺ spikes does not necessarily prevent LTP induction. Thus, Na⁺ spikes may facilitate LTP and/or LTD under some conditions, but they are not necessary, a finding consistent with their small size relative to the EPSP in many parts of pyramidal cell dendrites. The nature of the dendritic depolarizing events that control bidirectional plasticity is of central importance to understanding neural function. There are several candidates, including backpropagating action potentials, but also dendritic Ca²⁺ spikes, the AMPA receptor-mediated EPSP, and NMDA receptor-mediated EPSPs or spikes. These often appear to be more important than the Na⁺ spike in providing the depolarization necessary for plasticity. We thus feel that it is premature to accept STDP-like processes as the major determinant of LTP/LTD.