The endurance and selectivity of spatial patterns of long-term potentiation/depression in dendrites under homeostatic synaptic plasticity

Half a century legacy of long-term potentiation

In 1973, two papers from Bliss and Lømo and from Bliss and Gardner-Medwin reported that high-frequency synaptic stimulation in the dentate gyrus of rabbits resulted in a long-lasting increase in synaptic strength. This form of synaptic plasticity, commonly referred to as long-term potentiation (LTP), was immediately considered as an attractive mechanism accounting for the ability of the brain to store information. In this historical piece looking back over the past 50 years, we discuss how these two landmark contributions directly motivated a colossal research effort and detail some of the resulting milestones that have shaped our evolving understanding of the molecular and cellular underpinnings of LTP. We highlight the main features of LTP, cover key experiments that defined its induction and expression mechanisms, and outline the evidence supporting a potential role of LTP in learning and memory. We also briefly explore some ramifications of LTP on network stability, consider current limitations of LTP as a model of associative memory, and entertain future research orientations.

Heterogeneous off-target impact of ion-channel deletion on intrinsic properties of hippocampal model neurons that self-regulate calcium

How do neurons that implement cell-autonomous self-regulation of calcium react to knockout of individual ion-channel conductances? To address this question, we used a heterogeneous population of 78 conductance-based models of hippocampal pyramidal neurons that maintained cell-autonomous calcium homeostasis while receiving theta-frequency inputs. At calcium steady-state, we individually deleted each of the 11 active ion-channel conductances from each model. We measured the acute impact of deleting each conductance (one at a time) by comparing intrinsic electrophysiological properties before and immediately after channel deletion. The acute impact of deleting individual conductances on physiological properties (including calcium homeostasis) was heterogeneous, depending on the property, the specific model, and the deleted channel. The underlying many-to-many mapping between ion channels and properties pointed to ion-channel degeneracy. Next, we allowed the other conductances (barring the deleted conductance) to evolve towards achieving calcium homeostasis during theta-frequency activity. When calcium homeostasis was perturbed by ion-channel deletion, post-knockout plasticity in other conductances ensured resilience of calcium homeostasis to ion-channel deletion. These results demonstrate degeneracy in calcium homeostasis, as calcium homeostasis in knockout models was implemented in the absence of a channel that was earlier involved in the homeostatic process. Importantly, in reacquiring homeostasis, ion-channel conductances and physiological properties underwent heterogenous plasticity (dependent on the model, the property, and the deleted channel), even introducing changes in properties that were not directly connected to the deleted channel. Together, post-knockout plasticity geared towards maintaining homeostasis introduced heterogenous off-target effects on several channels and properties, suggesting that extreme caution be exercised in interpreting experimental outcomes involving channel knockouts.

miR-329- and miR-495-mediated Prr7 down-regulation is required for homeostatic synaptic depression in rat hippocampal neurons

In rat hippocampal neurons, miRNA-dependent regulation of the synaptic Prr7 protein is required for the homeostatic synaptic depression of excitatory synapses upstream of the CDK5-SPAR pathway.

Homeostatic synaptic depression (HSD) in excitatory neurons is a cell-autonomous mechanism which protects excitatory neurons from over-excitation as a consequence of chronic increases in network activity. In this process, excitatory synapses are weakened and eventually eliminated, as evidenced by a reduction in synaptic AMPA receptor expression and dendritic spine loss. Originally considered a global, cell-wide mechanism, local forms of regulation, such as the local control of mRNA translation in dendrites, are being increasingly recognized in HSD. Yet, identification of excitatory proteins whose local regulation is required for HSD is still limited. Here, we show that proline-rich protein 7/transmembrane adapter protein 3 (Prr7) down-regulation in dendrites of rat hippocampal neurons is necessary for HSD induced by chronic increase in network activity resulting from a blockade of inhibitory synaptic transmission by picrotoxin (PTX). We further identify two activity-regulated miRNAs, miR-329-3p and miR-495-3p, which inhibit Prr7 mRNA translation and are required for HSD. Moreover, we found that Prr7 knockdown reduces expression of the synaptic scaffolding protein SPAR, which is rescued by pharmacological inhibition of CDK5, indicating a role of Prr7 protein in the maintenance of excitatory synapses via protection of SPAR from degradation. Together, our findings highlight a novel HSD mechanism in which chronic activity leads to miR-329– and miR-495–mediated Prr7 reduction upstream of the CDK5-SPAR pathway.

Modulating STDP Balance Impacts the Dendritic Mosaic

The ability for cortical neurons to adapt their input/output characteristics and information processing capabilities ultimately relies on the interplay between synaptic plasticity, synapse location, and the nonlinear properties of the dendrite. Collectively, they shape both the strengths and spatial arrangements of convergent afferent inputs to neuronal dendrites. Recent experimental and theoretical studies support a clustered plasticity model, a view that synaptic plasticity promotes the formation of clusters or hotspots of synapses sharing similar properties. We have previously shown that spike timing-dependent plasticity (STDP) can lead to synaptic efficacies being arranged into spatially segregated clusters. This effectively partitions the dendritic tree into a tessellated imprint which we have called a dendritic mosaic. Here, using a biophysically detailed neuron model of a reconstructed layer 2/3 pyramidal cell and STDP learning, we investigated the impact of altered STDP balance on forming such a spatial organization. We show that cluster formation and extend depend on several factors, including the balance between potentiation and depression, the afferents' mean firing rate and crucially on the dendritic morphology. We find that STDP balance has an important role to play for this emergent mode of spatial organization since any imbalances lead to severe degradation- and in some case even destruction- of the mosaic. Our model suggests that, over a broad range of of STDP parameters, synaptic plasticity shapes the spatial arrangement of synapses, favoring the formation of clustered efficacy engrams.

Mechanisms of homeostatic plasticity in the excitatory synapse

Brain development, sensory information processing, and learning and memory processes depend on Hebbian forms of synaptic plasticity, and on the remodeling and pruning of synaptic connections. Neurons in networks implicated in these processes carry out their functions while facing constant perturbation; homeostatic responses are therefore required to maintain neuronal activity within functional ranges for proper brain function. Here, we will review in vitro and in vivo studies demonstrating that several mechanisms underlie homeostatic plasticity of excitatory synapses, and identifying participant molecular players. Emerging evidence suggests a link between disrupted homeostatic synaptic plasticity and neuropsychiatric and neurologic disorders. Hebbian forms of synaptic plasticity, such as long-term potentiation (LTP), induce long-lasting changes in synaptic strength, which can be destabilizing and drive activity to saturation. Conversely, homeostatic plasticity operates to compensate for prolonged activity changes, stabilizing neuronal firing within a dynamic physiological range. We review mechanisms underlying homeostatic plasticity, and address how neurons integrate distinct forms of plasticity for proper brain function. This article is part of a mini review series: "Synaptic Function and Dysfunction in Brain Diseases".

Variability in State-Dependent Plasticity of Intrinsic Properties during Cell-Autonomous Self-Regulation of Calcium Homeostasis in Hippocampal Model Neurons

How do neurons reconcile the maintenance of calcium homeostasis with perpetual switches in patterns of afferent activity? Here, we assessed state-dependent evolution of calcium homeostasis in a population of hippocampal pyramidal neuron models, through an adaptation of a recent study on stomatogastric ganglion neurons. Calcium homeostasis was set to emerge through cell-autonomous updates to 12 ionic conductances, responding to different types of synaptically driven afferent activity. We first assessed the impact of theta-frequency inputs on the evolution of ionic conductances toward maintenance of calcium homeostasis. Although calcium homeostasis emerged efficaciously across all models in the population, disparate changes in ionic conductances that mediated this emergence resulted in variable plasticity to several intrinsic properties, also manifesting as significant differences in firing responses across models. Assessing the sensitivity of this form of plasticity, we noted that intrinsic neuronal properties and the firing response were sensitive to the target calcium concentration and to the strength and frequency of afferent activity. Next, we studied the evolution of calcium homeostasis when afferent activity was switched, in different temporal sequences, between two behaviorally distinct types of activity: theta-frequency inputs and sharp-wave ripples riding on largely silent periods. We found that the conductance values, intrinsic properties, and firing response of neurons exhibited differential robustness to an intervening switch in the type of afferent activity. These results unveil critical dissociations between different forms of homeostasis, and call for a systematic evaluation of the impact of state-dependent switches in afferent activity on neuronal intrinsic properties during neural coding and homeostasis.

Inter-synaptic learning of combination rules in a cortical network model

Selecting responses in working memory while processing combinations of stimuli depends strongly on their relations stored in long-term memory. However, the learning of XOR-like combinations of stimuli and responses according to complex rules raises the issue of the non-linear separability of the responses within the space of stimuli. One proposed solution is to add neurons that perform a stage of non-linear processing between the stimuli and responses, at the cost of increasing the network size. Based on the non-linear integration of synaptic inputs within dendritic compartments, we propose here an inter-synaptic (IS) learning algorithm that determines the probability of potentiating/depressing each synapse as a function of the co-activity of the other synapses within the same dendrite. The IS learning is effective with random connectivity and without either a priori wiring or additional neurons. Our results show that IS learning generates efficacy values that are sufficient for the processing of XOR-like combinations, on the basis of the sole correlational structure of the stimuli and responses. We analyze the types of dendrites involved in terms of the number of synapses from pre-synaptic neurons coding for the stimuli and responses. The synaptic efficacy values obtained show that different dendrites specialize in the detection of different combinations of stimuli. The resulting behavior of the cortical network model is analyzed as a function of inter-synaptic vs. Hebbian learning. Combinatorial priming effects show that the retrospective activity of neurons coding for the stimuli trigger XOR-like combination-selective prospective activity of neurons coding for the expected response. The synergistic effects of inter-synaptic learning and of mixed-coding neurons are simulated. The results show that, although each mechanism is sufficient by itself, their combined effects improve the performance of the network.

The less things change, the more they are different: contributions of long-term synaptic plasticity and homeostasis to memory

An important cellular mechanism contributing to the strength and duration of memories is activity-dependent alterations in the strength of synaptic connections within the neural circuit encoding the memory. Reversal of the memory is typically correlated with a reversal of the cellular changes to levels expressed prior to the stimulation. Thus, for stimulus-induced changes in synapse strength and their reversals to be functionally relevant, cellular mechanisms must regulate and maintain synapse strength both prior to and after the stimuli inducing learning and memory. The strengths of synapses within a neural circuit at any given moment are determined by cellular and molecular processes initiated during development and those subsequently regulated by the history of direct activation of the neural circuit and system-wide stimuli such as stress or motivational state. The cumulative actions of stimuli and other factors on an already modified neural circuit are attenuated by homeostatic mechanisms that prevent changes in overall synaptic inputs and excitability above or below specific set points (synaptic scaling). The mechanisms mediating synaptic scaling prevent potential excitotoxic alterations in the circuit but also may attenuate additional cellular changes required for learning and memory, thereby apparently limiting information storage. What cellular and molecular processes control synaptic strengths before and after experience/activity and its reversals? In this review we will explore the synapse-, whole cell-, and circuit level-specific processes that contribute to an overall zero sum-like set of changes and long-term maintenance of synapse strengths as a consequence of the accommodative interactions between long-term synaptic plasticity and homeostasis.

NMDA-receptor inhibition increases spine stability of denervated mouse dentate granule cells and accelerates spine density recovery following entorhinal denervation in vitro

Neuronal networks are reorganized following brain injury. At the structural level this is in part reflected by changes in the spine turnover of the denervated neurons. Using the entorhinal cortex lesion in vitro model, we recently showed that mouse dentate granule cells respond to entorhinal denervation with coordinated functional and structural changes: During the early phase after denervation spine density decreases, while excitatory synaptic strength increases in a homeostatic manner. At later stages spine density increases again, and synaptic strength decreases back to baseline. In the present study, we have addressed the question of whether the denervation-induced homeostatic strengthening of excitatory synapses could not only be a result of the deafferentation, but could, in turn, affect the dynamics of the spine reorganization process following entorhinal denervation in vitro. Using a computational approach, time-lapse imaging of neurons in organotypic slice cultures prepared from Thy1-GFP mice, and patch-clamp recordings we provide experimental evidence which suggests that the strengthening of surviving synapses can lead to the destabilization of spines formed after denervation. This activity-dependent pruning of newly formed spines requires the activation of N-methyl-d-aspartate receptors (NMDA-Rs), since pharmacological inhibition of NMDA-Rs resulted in a stabilization of spines and in an accelerated spine density recovery after denervation. Thus, NMDA-R inhibitors may restore the ability of neurons to form new stable synaptic contacts under conditions of denervation-induced homeostatic synaptic up-scaling, which may contribute to their beneficial effect seen in the context of some neurological diseases.

Time scales of memory, learning, and plasticity

After only about 10 days would the storage capacity of our nervous system be reached if we stored every bit of input. The nervous system relies on at least two mechanisms that counteract this capacity limit: compression and forgetting. But the latter mechanism needs to know how long an entity should be stored: some memories are relevant only for the next few minutes, some are important even after the passage of several years. Psychology and physiology have found and described many different memory mechanisms, and these mechanisms indeed use different time scales. In this prospect we review these mechanisms with respect to their time scale and propose relations between mechanisms in learning and memory and their underlying physiological basis.

An improved test for detecting multiplicative homeostatic synaptic scaling

Homeostatic scaling of synaptic strengths is essential for maintenance of network "gain", but also poses a risk of losing the distinctions among relative synaptic weights, which are possibly cellular correlates of memory storage. Multiplicative scaling of all synapses has been proposed as a mechanism that would preserve the relative weights among them, because they would all be proportionately adjusted. It is crucial for this hypothesis that all synapses be affected identically, but whether or not this actually occurs is difficult to determine directly. Mathematical tests for multiplicative synaptic scaling are presently carried out on distributions of miniature synaptic current amplitudes, but the accuracy of the test procedure has not been fully validated. We now show that the existence of an amplitude threshold for empirical detection of miniature synaptic currents limits the use of the most common method for detecting multiplicative changes. Our new method circumvents the problem by discarding the potentially distorting subthreshold values after computational scaling. This new method should be useful in assessing the underlying neurophysiological nature of a homeostatic synaptic scaling transformation, and therefore in evaluating its functional significance.

Influence fields: a quantitative framework for representation and analysis of active dendrites

Neuronal dendrites express numerous voltage-gated ion channels (VGICs), typically with spatial gradients in their densities and properties. Dendritic VGICs, their gradients, and their plasticity endow neurons with information processing capabilities that are higher than those of neurons with passive dendrites. Despite this, frameworks that incorporate dendritic VGICs and their plasticity into neurophysiological and learning theory models have been far and few. Here, we develop a generalized quantitative framework to analyze the extent of influence of a spatially localized VGIC conductance on different physiological properties along the entire stretch of a neuron. Employing this framework, we show that the extent of influence of a VGIC conductance is largely independent of the conductance magnitude but is heavily dependent on the specific physiological property and background conductances. Morphologically, our analyses demonstrate that the influences of different VGIC conductances located on an oblique dendrite are confined within that oblique dendrite, thus providing further credence to the postulate that dendritic branches act as independent computational units. Furthermore, distinguishing between active and passive propagation of signals within a neuron, we demonstrate that the influence of a VGIC conductance is spatially confined only when propagation is active. Finally, we reconstruct functional gradients from VGIC conductance gradients using influence fields and demonstrate that the cumulative contribution of VGIC conductances in adjacent compartments plays a critical role in determining physiological properties at a given location. We suggest that our framework provides a quantitative basis for unraveling the roles of dendritic VGICs and their plasticity in neural coding, learning, and homeostasis.

Homeostatic synaptic plasticity: local and global mechanisms for stabilizing neuronal function

Neural circuits must maintain stable function in the face of many plastic challenges, including changes in synapse number and strength, during learning and development. Recent work has shown that these destabilizing influences are counterbalanced by homeostatic plasticity mechanisms that act to stabilize neuronal and circuit activity. One such mechanism is synaptic scaling, which allows neurons to detect changes in their own firing rates through a set of calcium-dependent sensors that then regulate receptor trafficking to increase or decrease the accumulation of glutamate receptors at synaptic sites. Additional homeostatic mechanisms may allow local changes in synaptic activation to generate local synaptic adaptations, and network-wide changes in activity to generate network-wide adjustments in the balance between excitation and inhibition. The signaling pathways underlying these various forms of homeostatic plasticity are currently under intense scrutiny, and although dozens of molecular pathways have now been implicated in homeostatic plasticity, a clear picture of how homeostatic feedback is structured at the molecular level has not yet emerged. On a functional level, neuronal networks likely use this complex set of regulatory mechanisms to achieve homeostasis over a wide range of temporal and spatial scales.

Synaptic scaling in combination with many generic plasticity mechanisms stabilizes circuit connectivity

Synaptic scaling is a slow process that modifies synapses, keeping the firing rate of neural circuits in specific regimes. Together with other processes, such as conventional synaptic plasticity in the form of long term depression and potentiation, synaptic scaling changes the synaptic patterns in a network, ensuring diverse, functionally relevant, stable, and input-dependent connectivity. How synaptic patterns are generated and stabilized, however, is largely unknown. Here we formally describe and analyze synaptic scaling based on results from experimental studies and demonstrate that the combination of different conventional plasticity mechanisms and synaptic scaling provides a powerful general framework for regulating network connectivity. In addition, we design several simple models that reproduce experimentally observed synaptic distributions as well as the observed synaptic modifications during sustained activity changes. These models predict that the combination of plasticity with scaling generates globally stable, input-controlled synaptic patterns, also in recurrent networks. Thus, in combination with other forms of plasticity, synaptic scaling can robustly yield neuronal circuits with high synaptic diversity, which potentially enables robust dynamic storage of complex activation patterns. This mechanism is even more pronounced when considering networks with a realistic degree of inhibition. Synaptic scaling combined with plasticity could thus be the basis for learning structured behavior even in initially random networks.

Bi-directional effect of cholecystokinin receptor-2 overexpression on stress-triggered fear memory and anxiety in the mouse

Fear, an emotional response of animals to environmental stress/threats, plays an important role in initiating and driving adaptive response, by which the homeostasis in the body is maintained. Overwhelming/uncontrollable fear, however, represents a core symptom of anxiety disorders, and may disturb the homeostasis. Because to recall or imagine certain cue(s) of stress/threats is a compulsory inducer for the expression of anxiety, it is generally believed that the pathogenesis of anxiety is associated with higher attention (acquisition) selectively to stress or mal-enhanced fear memory, despite that the actual relationship between fear memory and anxiety is not yet really established. In this study, inducible forebrain-specific cholecystokinin receptor-2 transgenic (IF-CCKR-2 tg) mice, different stress paradigms, batteries of behavioral tests, and biochemical assays were used to evaluate how different CCKergic activities drive fear behavior and hormonal reaction in response to stresses with different intensities. We found that in IF-CCKR-2 tg mice, contextual fear was impaired following 1 trial of footshock, while overall fear behavior was enhanced following 36 trials of footshock, compared to their littermate controls. In contrast to a standard Yerkes-Dodson (inverted-U shaped) stress-fear relationship in control mice, a linearized stress-fear curve was observed in CCKR-2 tg mice following gradient stresses. Moreover, compared to 1 trial, 36 trials of footshock in these transgenic mice enhanced anxiety-like behavior in other behavioral tests, impaired spatial and recognition memories, and prolonged the activation of adrenocorticotropic hormone (ACTH) and glucocorticoids (CORT) following new acute stress. Taken together, these results indicate that stress may trigger two distinctive neurobehavioral systems, depending on both of the intensity of stress and the CCKergic tone in the brain. A “threshold theory” for this two-behavior system has been suggested.

Spike timing-dependent plasticity as the origin of the formation of clustered synaptic efficacy engrams

Synapse location, dendritic active properties and synaptic plasticity are all known to play some role in shaping the different input streams impinging onto a neuron. It remains unclear however, how the magnitude and spatial distribution of synaptic efficacies emerge from this interplay. Here, we investigate this interplay using a biophysically detailed neuron model of a reconstructed layer 2/3 pyramidal cell and spike timing-dependent plasticity (STDP). Specifically, we focus on the issue of how the efficacy of synapses contributed by different input streams are spatially represented in dendrites after STDP learning. We construct a simple feed forward network where a detailed model neuron receives synaptic inputs independently from multiple yet equally sized groups of afferent fibers with correlated activity, mimicking the spike activity from different neuronal populations encoding, for example, different sensory modalities. Interestingly, ensuing STDP learning, we observe that for all afferent groups, STDP leads to synaptic efficacies arranged into spatially segregated clusters effectively partitioning the dendritic tree. These segregated clusters possess a characteristic global organization in space, where they form a tessellation in which each group dominates mutually exclusive regions of the dendrite. Put simply, the dendritic imprint from different input streams left after STDP learning effectively forms what we term a “dendritic efficacy mosaic.” Furthermore, we show how variations of the inputs and STDP rule affect such an organization. Our model suggests that STDP may be an important mechanism for creating a clustered plasticity engram, which shapes how different input streams are spatially represented in dendrite.

Spike-timing dependent plasticity beyond synapse - pre- and post-synaptic plasticity of intrinsic neuronal excitability

Long-lasting plasticity of synaptic transmission is classically thought to be the cellular substrate for information storage in the brain. Recent data indicate however that it is not the whole story and persistent changes in the intrinsic neuronal excitability have been shown to occur in parallel to the induction of long-term synaptic modifications. This form of plasticity depends on the regulation of voltage-gated ion channels. Here we review the experimental evidence for plasticity of neuronal excitability induced at pre- or postsynaptic sites when long-term plasticity of synaptic transmission is induced with Spike-Timing Dependent Plasticity (STDP) protocols. We describe the induction and expression mechanisms of the induced changes in excitability. Finally, the functional synergy between synaptic and non-synaptic plasticity and their spatial extent are discussed.

Receptive field dynamics underlying MST neuronal optic flow selectivity

Optic flow informs moving observers about their heading direction. Neurons in monkey medial superior temporal (MST) cortex show heading selective responses to optic flow and planar direction selective responses to patches of local motion. We recorded MST neuronal responses to a 90 x 90 degrees optic flow display and to a 3 x 3 array of local motion patches covering the same area. Our goal was to test the hypothesis that the optic flow responses reflect the sum of the local motion responses. The local motion responses of each neuron were modeled as mixtures of Gaussians, combining the effects of two Gaussian response functions derived using a genetic algorithm, and then used to predict that neuron's optic flow responses. Some neurons showed good correspondence between local motion models and optic flow responses, others showed substantial differences. We used the genetic algorithm to modulate the relative strength of each local motion segment's responses to accommodate interactions between segments that might modulate their relative efficacy during co-activation by global patterns of optic flow. These gain modulated models showed uniformly better fits to the optic flow responses, suggesting that coactivation of receptive field segments alters neuronal response properties. We tested this hypothesis by simultaneously presenting local motion stimuli at two different sites. These two-segment stimuli revealed that interactions between response segments have direction and location specific effects that can account for aspects of optic flow selectivity. We conclude that MST's optic flow selectivity reflects dynamic interactions between spatially distributed local planar motion response mechanisms.

Model calcium sensors for network homeostasis: sensor and readout parameter analysis from a database of model neuronal networks

In activity-dependent homeostatic regulation (ADHR) of neuronal and network properties, the intracellular Ca(2+) concentration is a good candidate for sensing activity levels because it is correlated with the electrical activity of the cell. Previous ADHR models, developed with abstract activity sensors for model pyloric neurons and networks of the crustacean stomatogastric ganglion, showed that functional activity can be maintained by a regulation mechanism that senses activity levels solely from Ca(2+). At the same time, several intracellular pathways have been discovered for Ca(2+)-dependent regulation of ion channels. To generate testable predictions for dynamics of these signaling pathways, we undertook a parameter study of model Ca(2+) sensors across thousands of model pyloric networks. We found that an optimal regulation signal can be generated for 86% of model networks with a sensing mechanism that activates with a time constant of 1 ms and that inactivates within 1 s. The sensor performed robustly around this optimal point and did not need to be specific to the role of the cell. When multiple sensors with different time constants were used, coverage extended to 88% of the networks. Without changing the sensors, it extended to 95% of the networks by letting the sensors affect the readout nonlinearly. Specific to this pyloric network model, the sensor of the follower pyloric constrictor cell was more informative than the pacemaker anterior burster cell for producing a regulatory signal. Conversely, a global signal indicating network activity that was generated by summing the sensors in individual cells was less informative for regulation.

Possible dendritic contribution to unimodal numerosity tuning and weber-fechner law-dependent numerical cognition

Humans and animals are known to share an ability to estimate or compare the numerosity of visual stimuli, and this ability is considered to be supported by the cortical neurons that have unimodal tuning for numerosity, referred to as the numerosity detector neurons. How such unimodal numerosity tuning is shaped through plasticity mechanisms is unknown. Here, I propose a testable hypothetical mechanism based on recently revealed features of the neuronal dendrite, namely, cooperative plasticity induction and nonlinear input integration at nearby dendritic sites, on the basis of the existing proposal that individual visual stimuli are represented as similar localized activities regardless of the size or the shape in a cortical region in the dorsal visual pathway. Intriguingly, the proposed mechanism naturally explains a prominent feature of the numerosity detector neurons, namely, the broadening of the tuning curve in proportion to the preferred numerosity, which is considered to underlie the known Weber-Fechner law-dependent accuracy of numerosity estimation and comparison. The simulated tuning curves are less sharp than reality, however, and together with the evidence from human imaging studies that numerical representation is a distributed phenomenon, it may not be likely that the proposed mechanism operates by itself. Rather, the proposed mechanism might facilitate the formation of hierarchical circuitry proposed in the previous studies, which includes neurons with monotonic numerosity tuning as well as those with sharp unimodal tuning, by serving as an efficient initial condition.

Analysis of c-fos and zif268 expression reveals time-dependent changes in activity inside and outside the lesion projection zone in adult cat area 17 after retinal lesions

Retinal lesions induce a topographic reorganization in the corresponding lesion projection zone (LPZ) in the visual cortex of adult cats. To gain a better insight into the reactivation dynamics, we investigated the alterations in cortical activity throughout area 17. We implemented in situ hybridization and real-time polymerase chain reaction to analyze the spatiotemporal expression patterns of the activity marker genes zif268 and c-fos. The immediate early gene (IEG) data confirmed a strong and permanent activity decrease in the center of the LPZ as previously described by electrophysiology. A recovery of IEG expression was clearly measured in the border of the LPZ. We were able to register reorganization over 2.5-6 mm. We also present evidence that the central retinal lesions concomitantly influence the activity in far peripheral parts of area 17. Its IEG expression levels appeared dependent of time and distance from the LPZ. We therefore propose that coupled changes in activity occur inside and outside the LPZ. In conclusion, alterations in activity reporter gene expression throughout area 17 contribute to the lesion-induced functional reorganization.

Dendritic excitability and synaptic plasticity

Most synaptic inputs are made onto the dendritic tree. Recent work has shown that dendrites play an active role in transforming synaptic input into neuronal output and in defining the relationships between active synapses. In this review, we discuss how these dendritic properties influence the rules governing the induction of synaptic plasticity. We argue that the location of synapses in the dendritic tree, and the type of dendritic excitability associated with each synapse, play decisive roles in determining the plastic properties of that synapse. Furthermore, since the electrical properties of the dendritic tree are not static, but can be altered by neuromodulators and by synaptic activity itself, we discuss how learning rules may be dynamically shaped by tuning dendritic function. We conclude by describing how this reciprocal relationship between plasticity of dendritic excitability and synaptic plasticity has changed our view of information processing and memory storage in neuronal networks.

Pathological effect of homeostatic synaptic scaling on network dynamics in diseases of the cortex

Slow periodic EEG discharges are common in CNS disorders. The pathophysiology of this aberrant rhythmic activity is poorly understood. We used a computational model of a neocortical network with a dynamic homeostatic scaling rule to show that loss of input (partial deafferentation) can trigger network reorganization that results in pathological periodic discharges. The decrease in average firing rate in the network by deafferentation was compensated by homeostatic synaptic scaling of recurrent excitation among pyramidal cells. Synaptic scaling succeeded in recovering the network target firing rate for all degrees of deafferentation (fraction of deafferented cells), but there was a critical degree of deafferentation for pathological network reorganization. For deafferentation degrees below this value, homeostatic upregulation of recurrent excitation had minimal effect on the macroscopic network dynamics. For deafferentation above this threshold, however, a slow periodic oscillation appeared, patterns of activity were less sparse, and bursting occurred in individual neurons. Also, comparison of spike-triggered afferent and recurrent excitatory conductances revealed that information transmission was strongly impaired. These results suggest that homeostatic plasticity can lead to secondary functional impairment in case of cortical disorders associated with cell loss.

A propagating ERKII switch forms zones of elevated dendritic activation correlated with plasticity

Strong inputs to neurons trigger complex biochemical events leading to synaptic plasticity. These biochemical events occur at many spatial scales, ranging from submicron dendritic spines to signals that propagate hundreds of microns from dendrites to the nucleus. ERKII is an important signaling molecule that is involved in many aspects of plasticity, including local excitability, communication with the nucleus, and control of local protein synthesis. We observed that ERKII activation spreads long distances in apical dendrites of stimulated hippocampal CA1 pyramidal neurons. We combined experiments and models to show that this >100 mum spread was too large to be explained by biochemical reaction-diffusion effects. We show that two modes of calcium entry along the dendrite contribute to the extensive activation of ERKII. We predict the occurrence of feedback between biophysical events resulting in calcium entry, and biochemical events resulting in ERKII activation. This feedback causes a switch-like propagation of ERKII activation, coupled with enhanced electrical excitability, along the apical dendrite. We propose that this propagating switch forms zones on dendrites in which plasticity is facilitated.

A propagating ERKII switch forms zones of elevated dendritic activation correlated with plasticity

Strong inputs to neurons trigger complex biochemical events leading to synaptic plasticity. These biochemical events occur at many spatial scales, ranging from submicron dendritic spines to signals that propagate hundreds of microns from dendrites to the nucleus. ERKII is an important signaling molecule that is involved in many aspects of plasticity, including local excitability, communication with the nucleus, and control of local protein synthesis. We observed that ERKII activation spreads long distances in apical dendrites of stimulated hippocampal CA1 pyramidal neurons. We combined experiments and models to show that this >100 mum spread was too large to be explained by biochemical reaction-diffusion effects. We show that two modes of calcium entry along the dendrite contribute to the extensive activation of ERKII. We predict the occurrence of feedback between biophysical events resulting in calcium entry, and biochemical events resulting in ERKII activation. This feedback causes a switch-like propagation of ERKII activation, coupled with enhanced electrical excitability, along the apical dendrite. We propose that this propagating switch forms zones on dendrites in which plasticity is facilitated.