Long-Term Potentiation of Intrinsic Excitability in LV Visual Cortical Neurons

Induction of long-term hyperexcitability by memory-related cAMP signaling in isolated nociceptor cell bodies

Persistent hyperactivity of nociceptors is known to contribute significantly to long-lasting sensitization and ongoing pain in many clinical conditions. It is often assumed that nociceptor hyperactivity is mainly driven by continuing stimulation from inflammatory mediators. We have tested an additional possibility: that persistent increases in excitability promoting hyperactivity can be induced by a prototypical cellular signaling pathway long known to induce late-phase long-term potentiation (LTP) of synapses in brain regions involved in memory formation. This cAMP-PKA-CREB-gene transcription-protein synthesis pathway was tested using whole-cell current clamp methods on small dissociated sensory neurons (primarily nociceptors) from dorsal root ganglia (DRGs) excised from previously uninjured ("naïve") rats. Six-hour treatment with the specific Gαs-coupled 5-HT4 receptor agonist, prucalopride, or with the adenylyl cyclase activator, forskolin, induced long-term hyperexcitability (LTH) in DRG neurons that manifested 12-24 hours later as action potential (AP) discharge (ongoing activity, OA) during artificial depolarization to -45 mV, a membrane potential that is normally subthreshold for AP generation. Prucalopride treatment also induced significant long-lasting depolarization of resting membrane potential (from -69 to -66 mV), enhanced depolarizing spontaneous fluctuations (DSFs) of membrane potential, and indications of reduced AP threshold and rheobase. LTH was prevented by co-treatment of prucalopride with inhibitors of PKA, CREB, gene transcription, and protein synthesis. As in the induction of synaptic memory, many other cellular signals are likely to be involved. However, the discovery that this prototypical memory induction pathway can induce nociceptor LTH, along with reports that cAMP signaling and CREB activity in DRGs can induce hyperalgesic priming, suggest that early, temporary, cAMP-induced transcriptional and translational mechanisms can induce nociceptor LTH that might last for long periods. An interesting possibility is that these mechanisms can also be reactivated by re-exposure to inflammatory mediators such as serotonin during subsequent challenges to bodily integrity, "reconsolidating" the cellular memory and thereby extending the duration of persistent nociceptor hyperexcitability.

SanPy: Software for the analysis and visualization of whole-cell current-clamp recordings

The analysis of action potentials and other membrane voltage fluctuations provides a powerful approach for interrogating the function of excitable cells. However, a major bottleneck in the interpretation of this critical data is the lack of intuitive, agreed-upon software tools for its analysis. Here, we present SanPy, an open-source and freely available software package for the analysis and exploration of whole-cell current-clamp recordings written in Python. SanPy provides a robust computational engine with an application programming interface. Using this, we have developed a cross-platform desktop application with a graphical user interface that does not require programming. SanPy is designed to extract common parameters from action potentials, including threshold time and voltage, peak, half-width, and interval statistics. In addition, several cardiac parameters are measured, including the early diastolic duration and rate. SanPy is built to be fully extensible by providing a plugin architecture for the addition of new file loaders, analysis, and visualizations. A key feature of SanPy is its focus on quality control and data exploration. In the desktop interface, all plots of the data and analysis are linked, allowing simultaneous data visualization from different dimensions with the goal of obtaining ground-truth analysis. We provide documentation for all aspects of SanPy, including several use cases and examples. To test SanPy, we performed analysis on current-clamp recordings from heart and brain cells. Taken together, SanPy is a powerful tool for whole-cell current-clamp analysis and lays the foundation for future extension by the scientific community.

Neuronal ensembles: Building blocks of neural circuits

Neuronal ensembles, defined as groups of neurons displaying recurring patterns of coordinated activity, represent an intermediate functional level between individual neurons and brain areas. Novel methods to measure and optically manipulate the activity of neuronal populations have provided evidence of ensembles in the neocortex and hippocampus. Ensembles can be activated intrinsically or in response to sensory stimuli and play a causal role in perception and behavior. Here we review ensemble phenomenology, developmental origin, biophysical and synaptic mechanisms, and potential functional roles across different brain areas and species, including humans. As modular units of neural circuits, ensembles could provide a mechanistic underpinning of fundamental brain processes, including neural coding, motor planning, decision-making, learning, and adaptability.

SanPy: A whole-cell electrophysiology analysis pipeline

The analysis of action potentials and other membrane voltage fluctuations provide a powerful approach for interrogating the function of excitable cells. Yet, a major bottleneck in the interpretation of this critical data is the lack of intuitive, agreed upon software tools for its analysis. Here, we present SanPy, a Python-based open-source and freely available software pipeline for the analysis and exploration of whole-cell current-clamp recordings. SanPy provides a robust computational engine with an application programming interface. Using this, we have developed a cross-platform graphical user interface that does not require programming. SanPy is designed to extract common parameters from action potentials including threshold time and voltage, peak, half-width, and interval statistics. In addition, several cardiac parameters are measured including the early diastolic duration and rate. SanPy is built to be fully extensible by providing a plugin architecture for the addition of new file loaders, analysis, and visualizations. A key feature of SanPy is its focus on quality control and data exploration. In the desktop interface, all plots of the data and analysis are linked allowing simultaneous data visualization from different dimensions with the goal of obtaining ground truth analysis. We provide documentation for all aspects of SanPy including several use cases and examples. To test SanPy, we have performed analysis on current-clamp recordings from heart and brain cells. Taken together, SanPy is a powerful tool for whole-cell current-clamp analysis and lays the foundation for future extension by the scientific community.

The plasticitome of cortical interneurons

Hebb postulated that, to store information in the brain, assemblies of excitatory neurons coding for a percept are bound together via associative long-term synaptic plasticity. In this view, it is unclear what role, if any, is carried out by inhibitory interneurons. Indeed, some have argued that inhibitory interneurons are not plastic. Yet numerous recent studies have demonstrated that, similar to excitatory neurons, inhibitory interneurons also undergo long-term plasticity. Here, we discuss the many diverse forms of long-term plasticity that are found at inputs to and outputs from several types of cortical inhibitory interneuron, including their plasticity of intrinsic excitability and their homeostatic plasticity. We explain key plasticity terminology, highlight key interneuron plasticity mechanisms, extract overarching principles and point out implications for healthy brain functionality as well as for neuropathology. We introduce the concept of the plasticitome - the synaptic plasticity counterpart to the genome or the connectome - as well as nomenclature and definitions for dealing with this rich diversity of plasticity. We argue that the great diversity of interneuron plasticity rules is best understood at the circuit level, for example as a way of elucidating how the credit-assignment problem is solved in deep biological neural networks.

Effect of cortical extracellular GABA on motor response

To elucidate how the flattening of sensory tuning due to a deficit in tonic inhibition slows motor responses, we simulated a neural network model in which a sensory cortical network ([Formula: see text]) and a motor cortical network ([Formula: see text]) are reciprocally connected, and the [Formula: see text] projects to spinal motoneurons (Mns). The [Formula: see text] was presented with a feature stimulus and the reaction time of Mns was measured. The flattening of sensory tuning in [Formula: see text] caused by decreasing the concentration of gamma-aminobutyric acid (GABA) in extracellular space resulted in a decrease in the stimulus-sensitive [Formula: see text] pyramidal cell activity while increasing the stimulus-insensitive [Formula: see text] pyramidal cell activity, thereby prolonging the reaction time of Mns to the applied feature stimulus. We suggest that a reduction in extracellular GABA concentration in sensory cortex may interfere with selective activation in motor cortex, leading to slowing the activation of spinal motoneurons and therefore to slowing motor responses.

Intrinsic excitability mechanisms of neuronal ensemble formation

Neuronal ensembles are coactive groups of cortical neurons, found in spontaneous and evoked activity, that can mediate perception and behavior. To understand the mechanisms that lead to the formation of ensembles, we co-activated layer 2/3 pyramidal neurons in brain slices from mouse visual cortex, in animals of both sexes, replicating in vitro an optogenetic protocol to generate ensembles in vivo. Using whole-cell and perforated patch-clamp pair recordings we found that, after optogenetic or electrical stimulation, coactivated neurons increased their correlated activity, a hallmark of ensemble formation. Coactivated neurons showed small biphasic changes in presynaptic plasticity, with an initial depression followed by a potentiation after a recovery period. Optogenetic and electrical stimulation also induced significant increases in frequency and amplitude of spontaneous EPSPs, even after single-cell stimulation. In addition, we observed unexpected strong and persistent increases in neuronal excitability after stimulation, with increases in membrane resistance and reductions in spike threshold. A pharmacological agent that blocks changes in membrane resistance reverted this effect. These significant increases in excitability can explain the observed biphasic synaptic plasticity. We conclude that cell-intrinsic changes in excitability are involved in the formation of neuronal ensembles. We propose an ‘iceberg’ model, by which increased neuronal excitability makes subthreshold connections suprathreshold, enhancing the effect of already existing synapses, and generating a new neuronal ensemble.

In the brain, groups of neurons that are activated together – also known as neuronal ensembles – are the basic units that underpin perception and behavior. Yet, exactly how these coactive circuits are established remains under investigation.

In 1949, Canadian psychologist Donald Hebb proposed that, when brains learn something new, the neurons which are activated together connect to form ensembles, and their connections become stronger each time this specific piece of knowledge is recalled. This idea that ‘neurons that fire together, wire together’ can explain how memories are acquired and recalled, by strengthening their wiring.

However, recent studies have questioned whether strengthening connections is the only mechanism by which neural ensembles can be created. Changes in the excitability of neurons (how easily they are to fire and become activated) may also play a role. In other words, ensembles could emerge because certain neurons become more excitable and fire more readily.

To solve this conundrum, Alejandre-García et al. examined both hypotheses in the same system. Neurons in slices of the mouse visual cortex were stimulated electrically or optically, via a technique that controls neural activity with light. The activity of individual neurons and their connections was then measured with electrodes.

Spontaneous activity among connected neurons increased after stimulation, indicative of the formation of neuronal ensembles. Connected neurons also showed small changes in the strength of their connections, which first decreased and then rebounded after an initial recovery period.

Intriguingly, cells also showed unexpected strong and persistent increases in neuronal excitability after stimulation, such that neurons fired more readily to the same stimulus. In other words, neurons maintained a cellular memory of having been stimulated. The authors conclude that ensembles form because connected neurons become more excitable, which in turn, may strengthen connections of the circuit at a later stage.

These results provide fresh insights about the neural circuits underpinning learning and memory. In time, the findings could also help to understand disorders such as Alzheimer’s disease and schizophrenia, which are characterised by memory impairments and disordered thinking.

Mechanisms of Plasticity in Subcortical Visual Areas

Visual plasticity is classically considered to occur essentially in the primary and secondary cortical areas. Subcortical visual areas such as the dorsal lateral geniculate nucleus (dLGN) or the superior colliculus (SC) have long been held as basic structures responsible for a stable and defined function. In this model, the dLGN was considered as a relay of visual information travelling from the retina to cortical areas and the SC as a sensory integrator orienting body movements towards visual targets. However, recent findings suggest that both dLGN and SC neurons express functional plasticity, adding unexplored layers of complexity to their previously attributed functions. The existence of neuronal plasticity at the level of visual subcortical areas redefines our approach of the visual system. The aim of this paper is therefore to review the cellular and molecular mechanisms for activity-dependent plasticity of both synaptic transmission and cellular properties in subcortical visual areas.

Short-Term Epileptiform Activity Potentiates Excitatory Synapses but Does Not Affect Intrinsic Membrane Properties of Pyramidal Neurons in the Rat Hippocampus In Vitro

Even brief epileptic seizures can lead to activity-dependent structural remodeling of neural circuitry. Animal models show that the functional plasticity of synapses and changes in the intrinsic excitability of neurons can be crucial for epileptogenesis. However, the exact mechanisms underlying epileptogenesis remain unclear. We induced epileptiform activity in rat hippocampal slices for 15 min using a 4-aminopyridine (4-AP) in vitro model and observed hippocampal hyperexcitability for at least 1 h. We tested several possible mechanisms of this hyperexcitability, including changes in intrinsic membrane properties of neurons and presynaptic and postsynaptic alterations. Neither input resistance nor other essential biophysical properties of hippocampal CA1 pyramidal neurons were affected by epileptiform activity. The glutamate release probability also remained unchanged, as the frequency of miniature EPSCs and the paired amplitude ratio of evoked responses did not change after epileptiform activity. However, we found an increase in the AMPA/NMDA ratio, suggesting alterations in the properties of postsynaptic glutamatergic receptors. Thus, the increase in excitability of hippocampal neural networks is realized through postsynaptic mechanisms. In contrast, the intrinsic membrane properties of neurons and the probability of glutamate release from presynaptic terminals are not affected in a 4-AP model.

Conventional measures of intrinsic excitability are poor estimators of neuronal activity under realistic synaptic inputs

Activity-dependent regulation of intrinsic excitability has been shown to greatly contribute to the overall plasticity of neuronal circuits. Such neuroadaptations are commonly investigated in patch clamp experiments using current step stimulation and the resulting input-output functions are analyzed to quantify alterations in intrinsic excitability. However, it is rarely addressed, how such changes translate to the function of neurons when they operate under natural synaptic inputs. Still, it is reasonable to expect that a strong correlation and near proportional relationship exist between static firing responses and those evoked by synaptic drive. We challenge this view by performing a high-yield electrophysiological analysis of cultured mouse hippocampal neurons using both standard protocols and simulated synaptic inputs via dynamic clamp. We find that under these conditions the neurons exhibit vastly different firing responses with surprisingly weak correlation between static and dynamic firing intensities. These contrasting responses are regulated by two intrinsic K-currents mediated by Kv1 and Kir channels, respectively. Pharmacological manipulation of the K-currents produces differential regulation of the firing output of neurons. Static firing responses are greatly increased in stuttering type neurons under blocking their Kv1 channels, while the synaptic responses of the same neurons are less affected. Pharmacological blocking of Kir-channels in delayed firing type neurons, on the other hand, exhibit the opposite effects. Our subsequent computational model simulations confirm the findings in the electrophysiological experiments and also show that adaptive changes in the kinetic properties of such currents can even produce paradoxical regulation of the firing output.

A Model of the Early Visual System Based on Parallel Spike-Sequence Detection, Showing Orientation Selectivity

Simple Summary

A computational model of primates’ early visual processing, showing orientation selectivity, is presented. The system importantly integrates two key elements: (1) a neuromorphic spike-decoding structure that considerably resembles the circuitry between layers IV and II/III of the primary visual cortex, both in topology and operation; (2) the plasticity of intrinsic excitability, to embed recent findings about the operation of the same area. The model is proposed as a tool for the analysis and reproduction of the orientation selectivity phenomenon, whose underlying neuronal-level computational mechanisms are today the subject of intense scrutiny. In response to rotated Gabor patches the model is able to exhibit realistic orientation tuning curves and to reproduce responses similar to those found in neurophysiological recordings from the primary visual cortex obtained under the same task, considering different stages of the network. This demonstrates its aptness to capture the mechanisms underlying the evoked response in the primary visual cortex. Our tool is available online, and can be expanded to other experiments using a dedicated software library developed by the authors, to elucidate the computational mechanisms underlying orientation selectivity.

Abstract

Since the first half of the twentieth century, numerous studies have been conducted on how the visual cortex encodes basic image features. One of the hallmarks of basic feature extraction is the phenomenon of orientation selectivity, of which the underlying neuronal-level computational mechanisms remain partially unclear despite being intensively investigated. In this work we present a reduced visual system model (RVSM) of the first level of scene analysis, involving the retina, the lateral geniculate nucleus and the primary visual cortex (V1), showing orientation selectivity. The detection core of the RVSM is the neuromorphic spike-decoding structure MNSD, which is able to learn and recognize parallel spike sequences and considerably resembles the neuronal microcircuits of V1 in both topology and operation. This structure is equipped with plasticity of intrinsic excitability to embed recent findings about V1 operation. The RVSM, which embeds 81 groups of MNSD arranged in 4 oriented columns, is tested using sets of rotated Gabor patches as input. Finally, synthetic visual evoked activity generated by the RVSM is compared with real neurophysiological signal from V1 area: (1) postsynaptic activity of human subjects obtained by magnetoencephalography and (2) spiking activity of macaques obtained by multi-tetrode arrays. The system is implemented using the NEST simulator. The results attest to a good level of resemblance between the model response and real neurophysiological recordings. As the RVSM is available online, and the model parameters can be customized by the user, we propose it as a tool to elucidate the computational mechanisms underlying orientation selectivity.

Intrinsic Plasticity-Based Neuroadptive Control With Both Weights and Excitability Tuning

This brief presents an intrinsic plasticity (IP)-driven neural-network-based tracking control approach for a class of nonlinear uncertain systems. Inspired by the neural plasticity mechanism of individual neuron in nervous systems, a learning rule referred to as IP is employed for adjusting the radial basis functions (RBFs), resulting in a neural network (NN) with both weights and excitability tuning, based on which neuroadaptive tracking control algorithms for multiple-input-multiple-output (MIMO) uncertain systems are derived. Both theoretical analysis and numerical simulation confirm the effectiveness of the proposed method.

Genetic and activity dependent-mechanisms wiring the cortex: Two sides of the same coin

The cerebral cortex is responsible for the higher-order functions of the brain such as planning, cognition, or social behaviour. It provides us with the capacity to interact with and transform our world. The substrates of cortical functions are complex neural circuits that arise during development from the dynamic remodelling and progressive specialization of immature undefined networks. Here, we review the genetic and activity-dependent mechanisms of cortical wiring focussing on the importance of their interaction. Cortical circuits emerge from an initial set of neuronal types that engage in sequential forms of embryonic and postnatal activity. Such activities further complement the cells' genetic programs, increasing neuronal diversity and modifying the electrical properties while promoting selective connectivity. After a temporal window of enhanced plasticity, the main features of mature circuits are established. Failures in these processes can lead to neurodevelopmental disorders whose treatment remains elusive. However, a deeper dissection of cortical wiring will pave the way for innovative therapies.

Intrinsic plasticity and birdsong learning

Although information processing and storage in the brain is thought to be primarily orchestrated by synaptic plasticity, other neural mechanisms such as intrinsic plasticity are available. While a number of recent studies have described the plasticity of intrinsic excitability in several types of neurons, the significance of non-synaptic mechanisms in memory and learning remains elusive. After reviewing plasticity of intrinsic excitation in relation to learning and homeostatic mechanisms, we focus on the intrinsic properties of a class of basal-ganglia projecting song system neurons in zebra finch, how these related to each bird's unique learned song, how these properties change over development, and how they are maintained dynamically to rapidly change in response to auditory feedback perturbations. We place these results in the broader theme of learning and changes in intrinsic properties, emphasizing the computational implications of this form of plasticity, which are distinct from synaptic plasticity. The results suggest that exploring reciprocal interactions between intrinsic and network properties will be a fruitful avenue for understanding mechanisms of birdsong learning.

Operant conditioning of motor cortex neurons reveals neuron-subtype-specific responses in a brain-machine interface task

Operant conditioning is implemented in brain-machine interfaces (BMI) to induce rapid volitional modulation of single neuron activity to control arbitrary mappings with an external actuator. However, intrinsic factors of the volitional controller (i.e. the brain) or the output stage (i.e. individual neurons) might hinder performance of BMIs with more complex mappings between hundreds of neurons and actuators with multiple degrees of freedom. Improved performance might be achieved by studying these intrinsic factors in the context of BMI control. In this study, we investigated how neuron subtypes respond and adapt to a given BMI task. We conditioned single cortical neurons in a BMI task. Recorded neurons were classified into bursting and non-bursting subtypes based on their spike-train autocorrelation. Both neuron subtypes had similar improvement in performance and change in average firing rate. However, in bursting neurons, the activity leading up to a reward increased progressively throughout conditioning, while the response of non-bursting neurons did not change during conditioning. These results highlight the need to characterize neuron-subtype-specific responses in a variety of tasks, which might ultimately inform the design and implementation of BMIs.

Rapid adaptation to elevated extracellular potassium in the pyloric circuit of the crab, Cancer borealis

Elevated potassium concentration ([K⁺]) is often used to alter excitability in neurons and networks by shifting the potassium equilibrium potential (E_K) and, consequently, the resting membrane potential. We studied the effects of increased extracellular [K⁺] on the well-described pyloric circuit of the crab Cancer borealis. A 2.5-fold increase in extracellular [K⁺] (2.5×[K⁺]) depolarized pyloric dilator (PD) neurons and resulted in short-term loss of their normal bursting activity. This period of silence was followed within 5-10 min by the recovery of spiking and/or bursting activity during continued superfusion of 2.5×[K⁺] saline. In contrast, when PD neurons were pharmacologically isolated from pyloric presynaptic inputs, they exhibited no transient loss of spiking activity in 2.5×[K⁺], suggesting the presence of an acute inhibitory effect mediated by circuit interactions. Action potential threshold in PD neurons hyperpolarized during an hour-long exposure to 2.5×[K⁺] concurrent with the recovery of spiking and/or bursting activity. Thus the initial loss of activity appears to be mediated by synaptic interactions within the network, but the secondary adaptation depends on changes in the intrinsic excitability of the pacemaker neurons. The complex sequence of events in the responses of pyloric neurons to elevated [K⁺] demonstrates that electrophysiological recordings are necessary to determine both the transient and longer term effects of even modest alterations of K⁺ concentrations on neuronal activity.NEW & NOTEWORTHY Solutions with elevated extracellular potassium are commonly used as a depolarizing stimulus. We studied the effects of high potassium concentration ([K⁺]) on the pyloric circuit of the crab stomatogastric ganglion. A 2.5-fold increase in extracellular [K⁺] caused a transient loss of activity that was not due to depolarization block, followed by a rapid increase in excitability and recovery of spiking within minutes. This suggests that changing extracellular potassium can have complex and nonstationary effects on neuronal circuits.

The contribution of ion channels in input-output plasticity

Persistent changes that occur in brain circuits are classically thought to be mediated by long-term modifications in synaptic efficacy. Yet, many studies have shown that voltage-gated ion channels located at the input and output side of the neurons are also the subject to persistent modifications. These channels are thus responsible for intrinsic plasticity that is expressed in many different neuronal types including glutamatergic principal neurons and GABAergic interneurons. As for synaptic plasticity, activation of synaptic glutamate receptors initiate persistent modification in neuronal excitability. We review here how synaptic input can be efficiently altered by activity-dependent modulation of ion channels that control EPSP amplification, spike threshold or resting membrane potential. We discuss the nature of the learning rules shared by intrinsic and synaptic plasticity, the mechanisms of ion channel regulation and the impact of intrinsic plasticity on induction of synaptic modifications.

Myocyte enhancer factor-2 and p300 interact to regulate the expression of homeostatic regulator Pumilio in Drosophila

Pumilio (Pum), an RNA-binding protein, is a key component of neuron firing-rate homeostasis that likely maintains stability of neural circuit activity in all animals, from flies to mammals. While Pum is ubiquitously expressed, we understand little about how synaptic excitation regulates its expression in the CNS. Here, we characterized the Drosophila dpum promoter and identified multiple myocyte enhancer factor-2 (Mef2)-binding elements. We cloned 12 dmef2 splice variants and used a luciferase-based assay to monitor dpum promoter activity. While all 12 dMef2 splice variants enhance dpum promoter activity, exon 10-containing variants induce greater transactivation. Previous work shows dPum expression increases with synaptic excitation. However, we observe no change in dmef2 transcript in larval CNS, of both sexes, exposed to the proconvulsant picrotoxin. The lack of activity dependence is indicative of additional regulation. We identified p300 as a potential candidate. We show that by binding to dMef2, p300 represses dpum transactivation. Significantly, p300 transcript is downregulated by enhanced synaptic excitation (picrotoxin) which, in turn, increases transcription of dpum through derepression of dMef2. These results advance our understanding of dpum by showing the activity-dependent expression is regulated by an interaction between p300 and dMef2.

An Optogenetic Kindling Model of Neocortical Epilepsy

Epileptogenesis is the gradual process by which the healthy brain develops epilepsy. However, the neuronal circuit changes that underlie epileptogenesis are not well understood. Unfortunately, current chemically or electrically induced epilepsy models suffer from lack of cell specificity, so it is seldom known which cells were activated during epileptogenesis. We therefore sought to develop an optogenetic variant of the classical kindling model of epilepsy in which activatable cells are both genetically defined and fluorescently tagged. We briefly optogenetically activated pyramidal cells (PCs) in awake behaving mice every two days and conducted a series of experiments to validate the effectiveness of the model. Although initially inert, brief optogenetic stimuli eventually elicited seizures that increased in number and severity with additional stimulation sessions. Seizures were associated with long-lasting plasticity, but not with tissue damage or astrocyte reactivity. Once optokindled, mice retained an elevated seizure susceptibility for several weeks in the absence of additional stimulation, indicating a form of long-term sensitization. We conclude that optokindling shares many features with classical kindling, with the added benefit that the role of specific neuronal populations in epileptogenesis can be studied. Links between long-term plasticity and epilepsy can thus be elucidated.

Changes in Functional Properties of Rat Hippocampal Neurons Following Pentylenetetrazole-induced Status Epilepticus

Pathophysiological remodeling processes following status epilepticus (SE) play a critical role in the pathophysiology of epilepsy but have not yet been not fully investigated. In the present study, we examined changes in intrinsic properties of pyramidal neurons, basal excitatory synaptic transmission, and short-term synaptic plasticity in hippocampal slices of rats after SE. Seizures were induced in 3-week-old rats by an intraperitoneal pentylenetetrazole (PTZ) injection. Only animals with generalized seizures lasting more than 30 min were included in the experiments. We found that CA1 pyramidal neurons became more excitable and started firing at a lower excitatory input due to a significant increase in input resistance. However, basal excitatory synaptic transmission was reduced in CA3-CA1 synapses, thus preventing the propagation of excitation through neural networks. A significant increase in paired-pulse facilitation 1 d after SE pointed to a decrease in the probability of glutamate release. Increased intrinsic excitability of neurons and decreased synaptic transmission differentially affected the excitability of a neural network. In terms of changes in seizure susceptibility after SE, we observed a significant increase in the maximal electroshock threshold 1 day after SE, suggesting a decrease in seizure susceptibility. However, after 1 week, there was no difference in seizure susceptibility between control and post-SE rats. The effects of SE on functional properties of hippocampal neurons were transient in the PTZ model, and most of them had recovered 1 week after SE. However, some minor alterations, such as smaller amplitude field potentials, were observed 1 month after SE.

Epileptic pilocarpine-treated rats exhibit aberrant hippocampal EPSP-spike potentiation but retain long-term potentiation

Hippocampal neuron plasticity is strongly associated with learning, memory, and cognition. In addition to modification of synaptic function and connectivity, the capacity of hippocampal neurons to undergo plasticity involves the ability to change nonsynaptic excitability. This includes altering the probability that EPSPs will generate action potentials (E‐S plasticity). Epilepsy is a prevalent neurological disorder commonly associated with neuronal hyperexcitability and cognitive dysfunction. We examined E‐S plasticity in chronically epileptic Sprague–Dawley rats 3–10 weeks after pilocarpine‐induced status epilepticus. CA1 neurons in hippocampal slices were assayed by whole‐cell current clamp to measure EPSPs evoked by Schaffer collateral stimulation. Using a weak spike‐timing‐dependent protocol to induce plasticity, we found robust E‐S potentiation in conjunction with weak long‐term potentiation (LTP) in saline‐treated rats. In pilocarpine‐treated rats, a similar degree of LTP was found, but E‐S potentiation was reduced. Additionally, the degree of E‐S potentiation was not correlated with the degree of LTP for either group, suggesting that they independently contribute to neuronal plasticity. E‐S potentiation also differed from LTP in that E‐S plasticity could be induced solely from action potentials generated by postsynaptic current injection. The calcium chelating agent BAPTA in the intracellular solution blocked LTP and E‐S potentiation, revealing the calcium dependence of both processes. These findings suggest that LTP and E‐S potentiation have overlapping but nonidentical mechanisms of inducing neuronal plasticity that may independently contribute to cognitive disruptions observed in the chronic epileptic state.

Optimal neural inference of stimulus intensities

In natural data, the class and intensity of stimuli are correlated. Current machine learning algorithms ignore this ubiquitous statistical property of stimuli, usually by requiring normalized inputs. From a biological perspective, it remains unclear how neural circuits may account for these dependencies in inference and learning. Here, we use a probabilistic framework to model class-specific intensity variations, and we derive approximate inference and online learning rules which reflect common hallmarks of neural computation. Concretely, we show that a neural circuit equipped with specific forms of synaptic and intrinsic plasticity (IP) can learn the class-specific features and intensities of stimuli simultaneously. Our model provides a normative interpretation of IP as a critical part of sensory learning and predicts that neurons can represent nontrivial input statistics in their excitabilities. Computationally, our approach yields improved statistical representations for realistic datasets in the visual and auditory domains. In particular, we demonstrate the utility of the model in estimating the contrastive stress of speech.

The Emerging Concept of Intrinsic Plasticity: Activity-dependent Modulation of Intrinsic Excitability in Cerebellar Purkinje Cells and Motor Learning

What is memory? How does the brain process the sensory information and modify an organism's behavior? Many neuroscientists have focused on the activity- and experience-dependent modifications of synaptic functions in order to solve these fundamental questions in neuroscience. Recently, the plasticity of intrinsic excitability (called intrinsic plasticity) has emerged as an important element for information processing and storage in the brain. As the cerebellar Purkinje cells are the sole output neurons in the cerebellar cortex and the information is conveyed from a neuron to its relay neurons by forms of action potential firing, the modulation of the intrinsic firing activity may play a critical role in the cerebellar learning. Many voltage-gated and/or Ca2+-activated ion channels are involved in shaping the spiking output as well as integrating synaptic inputs to finely tune the cerebellar output. Recent studies suggested that the modulation of the intrinsic excitability and its plasticity in the cerebellar Purkinje cells might function as an integrator for information processing and memory formation. Moreover, the intrinsic plasticity might also determine the strength of connectivity to the sub-cortical areas such as deep cerebellar nuclei and vestibular nuclei to trigger the consolidation of the cerebellar-dependent memory by transferring the information.

Differences in the Electrophysiological Properties of Mouse Somatosensory Layer 2/3 Neurons In Vivo and Slice Stem from Intrinsic Sources Rather than a Network-Generated High Conductance State

Synaptic activity in vivo can potentially alter the integration properties of neurons. Using recordings in awake mice, we targeted somatosensory layer 2/3 pyramidal neurons and compared neuronal properties with those from slices. Pyramidal cells in vivo had lower resistance and gain values, as well as broader spikes and increased spike frequency adaptation compared to the same cells in slices. Increasing conductance in neurons using dynamic clamp to levels observed in vivo, however, did not lessen the differences between in vivo and slice conditions. Further, local application of tetrodotoxin (TTX) in vivo blocked synaptic-mediated membrane voltage fluctuations but had little impact on pyramidal cell membrane input resistance and time constant values. Differences in electrophysiological properties of layer 2/3 neurons in mouse somatosensory cortex, therefore, stem from intrinsic sources separate from synaptic-mediated membrane voltage fluctuations.

Interplay between global and pathway-specific synaptic plasticity in CA1 pyramidal cells

Mechanisms underlying information storage have been depicted for global cell-wide and pathway-specific synaptic plasticity. Yet, little is known how these forms of plasticity interact to enhance synaptic competition and network stability. We examined synaptic interactions between apical and basal dendrites of CA1 pyramidal neurons in mouse hippocampal slices. Bursts (50 Hz) of three action potentials (AP-bursts) paired with preceding presynaptic stimulation in stratum radiatum specifically led to LTP of the paired pathway in adult mice (P75). At adolescence (P28), an increase in burst frequency (>50 Hz) was required to gain timing-dependent LTP. Surprisingly, paired radiatum and unpaired oriens pathway potentiated, unless the pre-post delay was shortened from 10 to 5 ms, which selectively potentiated paired radiatum pathway, since unpaired oriens pathway decreased back to baseline. Conversely, the exact same 5 ms pairing in stratum oriens potentiated both pathways, as did AP-bursts alone, which potentiated synaptic efficacy as well as current-evoked postsynaptic spiking. L-type voltage-gated Ca²⁺ channels were involved in mediating synaptic potentiation in oriens, whereas NMDA and adenosine receptors counteracted unpaired stratum oriens potentiation following pairing in stratum radiatum. This asymmetric plasticity uncovers important insights into alterations of synaptic efficacy and intrinsic neuronal excitability for pathways that convey hippocampal and extra-hippocampal information.

Versatility and Flexibility of Cortical Circuits

Cortical circuits are known to be plastic and adaptable, as shown by an impressive body of evidence demonstrating the ability of cortical circuits to adapt to changes in environmental stimuli, development, learning, and insults. In this review, we will discuss some of the features of cortical circuits that are thought to facilitate cortical circuit versatility and flexibility. Throughout life, cortical circuits can be extensively shaped and refined by experience while preserving their overall organization, suggesting that mechanisms are in place to favor change but also to stabilize some aspects of the circuit. First, we will describe the basic organization and some of the common features of cortical circuits. We will then discuss how this underlying cortical structure provides a substrate for the experience- and learning-dependent processes that contribute to cortical flexibility.

Endoplasmic reticulum stress mediates distinct impacts of sevoflurane on different subfields of immature hippocampus

Sevoflurane, a typical inhaled anesthetic, is widely used in patients of all ages during surgery. The negative effects, such as inducing cell death and damaging spatial memory, of sevoflurane on neurodevelopment have raised increasing concerns in recent years. However, the molecular mechanism remains unclear. This study focused on the crucial role of endoplasmic reticulum (ER) stress in sevoflurane-induced hippocampal injury. Three-week-old rats were exposed to sevoflurane or control air for 5 h with or without ER stress inhibitor (4-phenylbutyric acid, 4-PBA) injection. The hippocampus was harvested to measure the ER stress sensors by western immunoblotting. Terminal deoxynucleotidyl transferase-mediated dUTP nick-end-labeling staining was used to detect cell apoptosis and electrophysiology was used to measure the intrinsic excitability of neurons in hippocampus. We measured learning and memory ability by Morris water maze tests 5 weeks after sevoflurane exposure. Interestingly, persistent sevoflurane exposure significantly increased the levels of ER stress sensors in hippocampus. But it resulted in different effects in CA1 and dentate gyrus. Greatly increased caspase-12-mediated apoptotic cells, which were proved to be the neural stem cells, were detected in the dentate gyrus. Meanwhile, CA1 pyramidal neurons exhibited significantly reduced intrinsic excitability. Furthermore, the administration of ER stress inhibitor attenuated the above mentioned detrimental effects evidently and prevented the following relevant learning and memory deficits. In conclusion, sevoflurane-mediated ER stress performs distinct effects on the different subfields of the immature hippocampus and inhibiting ER stress during sevoflurane anesthesia will be a potential method to prevent the following learning and memory deficits in adulthood.

Long-Term Depression of Intrinsic Excitability Accompanied by Synaptic Depression in Cerebellar Purkinje Cells

Long-term depression (LTD) at the parallel fiber (PF)-to-cerebellar Purkinje cell (PC) synapse is implicated in the output of PCs, the sole output of the cerebellar cortex. In addition to synaptic plasticity, intrinsic excitability is also one of the components that determines PC output. Although long-term potentiation of intrinsic excitability (LTP-IE) has been suggested, it has yet to be investigated how PF-PC LTD modifies intrinsic excitability of PCs. Here, we show that pairing of the PF and climbing fiber (CF) for PF-PC LTD induction evokes LTD-IE in cerebellar PCs from male C57BL/6 mice. Interestingly, this intrinsic plasticity showed different kinetics from synaptic plasticity, but both forms of plasticity share Ca²⁺ signaling and protein kinase C pathway as their underlying mechanism. Although small-conductance Ca²⁺-activated K⁺ channels play important roles in LTP-IE, no direct implication has been found. After PF-PC LTD induction, neither the temporal summation of dendritic EPSP nor the power of spike frequency adaptation is changed, indicating that cerebellar LTD executes the information processing in a quantitative way without quality changes of synaptic integration and generation of output signals. Our results suggest that LTD-IE may have a synergistic effect with synaptic depression on the total net output of neurons by amplifying the modification of PF synaptic transmission.SIGNIFICANCE STATEMENT Although the output of Purkinje cells (PCs) is a critical component of cerebellum-dependent learning and memory, the changes of PC excitability when synaptic LTD occurs are unclear. Here, we show that the induction of PF-PC LTD evokes LTD-IE in PCs. Our observation complements previous intrinsic plasticity phenomenon of long-term potentiation of intrinsic excitability (LTP-IE), providing evidence for the idea that intrinsic plasticity has bidirectionality as synaptic plasticity. LTD-IE occurs together with synaptic LTD and both phenomena are dependent on the Ca²⁺ signaling pathway. Furthermore, our findings raise the prospect that this synaptic and intrinsic plasticity acts synergistically in PCs to modify neuronal activity in the same direction when learning occurs.

Time-course and mechanisms of homeostatic plasticity in layers 2/3 and 5 of the barrel cortex

Recent studies have shown that ocular dominance plasticity in layer 2/3 of the visual cortex exhibits a form of homeostatic plasticity that is related to synaptic scaling and depends on TNFα. In this study, we tested whether a similar form of plasticity was present in layer 2/3 of the barrel cortex and, therefore, whether the mechanism was likely to be a general property of cortical neurons. We found that whisker deprivation could induce homeostatic plasticity in layer 2/3 of barrel cortex, but not in a mouse strain lacking synaptic scaling. The time-course of homeostatic plasticity in layer 2/3 was similar to that of L5 regular spiking (RS) neurons (L5RS), but slower than that of L5 intrinsic bursting (IB) neurons (L5IB). In layer 5, the strength of evoked whisker responses and ex vivo miniature excitatory post-synaptic currents (mEPSCs) amplitudes showed an identical time-course for homeostatic plasticity, implying that plasticity at excitatory synapses contacting layer 5 neurons is sufficient to explain the changes in evoked responses. Spontaneous firing rate also showed homeostatic behaviour for L5IB cells, but was absent for L5RS cells over the time-course studied. Spontaneous firing rate homeostasis was found to be independent of evoked response homeostasis suggesting that the two depend on different mechanisms.This article is part of the themed issue 'Integrating Hebbian and homeostatic plasticity'.

Activity-dependent redistribution of Kv2.1 ion channels on rat spinal motoneurons

Homeostatic plasticity occurs through diverse cellular and synaptic mechanisms, and extensive investigations over the preceding decade have established Kv2.1 ion channels as key homeostatic regulatory elements in several central neuronal systems. As in these cellular systems, Kv2.1 channels in spinal motoneurons (MNs) localize within large somatic membrane clusters. However, their role in regulating motoneuron activity is not fully established in vivo. We have previously demonstrated marked Kv2.1 channel redistribution in MNs following in vitro glutamate application and in vivo peripheral nerve injury (Romer et al., 2014, Brain Research, 1547:1-15). Here, we extend these findings through the novel use of a fully intact, in vivo rat preparation to show that Kv2.1 ion channels in lumbar MNs rapidly and reversibly redistribute throughout the somatic membrane following 10 min of electrophysiological sensory and/or motor nerve stimulation. These data establish that Kv2.1 channels are remarkably responsive in vivo to electrically evoked and synaptically driven action potentials in MNs, and strongly implicate motoneuron Kv2.1 channels in the rapid homeostatic response to altered neuronal activity.

Learning Probabilistic Inference through Spike-Timing-Dependent Plasticity

Numerous experimental data show that the brain is able to extract information from complex, uncertain, and often ambiguous experiences. Furthermore, it can use such learnt information for decision making through probabilistic inference. Several models have been proposed that aim at explaining how probabilistic inference could be performed by networks of neurons in the brain. We propose here a model that can also explain how such neural network could acquire the necessary information for that from examples. We show that spike-timing-dependent plasticity in combination with intrinsic plasticity generates in ensembles of pyramidal cells with lateral inhibition a fundamental building block for that: probabilistic associations between neurons that represent through their firing current values of random variables. Furthermore, by combining such adaptive network motifs in a recursive manner the resulting network is enabled to extract statistical information from complex input streams, and to build an internal model for the distribution p^* that generates the examples it receives. This holds even if p^* contains higher-order moments. The analysis of this learning process is supported by a rigorous theoretical foundation. Furthermore, we show that the network can use the learnt internal model immediately for prediction, decision making, and other types of probabilistic inference.

Spike-Based Bayesian-Hebbian Learning of Temporal Sequences

Many cognitive and motor functions are enabled by the temporal representation and processing of stimuli, but it remains an open issue how neocortical microcircuits can reliably encode and replay such sequences of information. To better understand this, a modular attractor memory network is proposed in which meta-stable sequential attractor transitions are learned through changes to synaptic weights and intrinsic excitabilities via the spike-based Bayesian Confidence Propagation Neural Network (BCPNN) learning rule. We find that the formation of distributed memories, embodied by increased periods of firing in pools of excitatory neurons, together with asymmetrical associations between these distinct network states, can be acquired through plasticity. The model’s feasibility is demonstrated using simulations of adaptive exponential integrate-and-fire model neurons (AdEx). We show that the learning and speed of sequence replay depends on a confluence of biophysically relevant parameters including stimulus duration, level of background noise, ratio of synaptic currents, and strengths of short-term depression and adaptation. Moreover, sequence elements are shown to flexibly participate multiple times in the sequence, suggesting that spiking attractor networks of this type can support an efficient combinatorial code. The model provides a principled approach towards understanding how multiple interacting plasticity mechanisms can coordinate hetero-associative learning in unison.

From one moment to the next, in an ever-changing world, and awash in a deluge of sensory data, the brain fluidly guides our actions throughout an astonishing variety of tasks. Processing this ongoing bombardment of information is a fundamental problem faced by its underlying neural circuits. Given that the structure of our actions along with the organization of the environment in which they are performed can be intuitively decomposed into sequences of simpler patterns, an encoding strategy reflecting the temporal nature of these patterns should offer an efficient approach for assembling more complex memories and behaviors. We present a model that demonstrates how activity could propagate through recurrent cortical microcircuits as a result of a learning rule based on neurobiologically plausible time courses and dynamics. The model predicts that the interaction between several learning and dynamical processes constitute a compound mnemonic engram that can flexibly generate sequential step-wise increases of activity within neural populations.

QUANTITATIVE REEVALUATION OF THE EFFECTS OF SHORT- AND LONG-TERM REMOVAL OF DESCENDING MODULATORY INPUTS ON THE PYLORIC RHYTHM OF THE CRAB, CANCER BOREALIS

Neuromodulatory inputs are known to strongly influence the intrinsic excitability of individual neurons and the networks in which the targets of modulation are found. It is therefore important to understand how nervous systems respond to altered neuromodulatory environments.

The crustacean stomatogastric ganglion (STG) receives descending neuromodulatory inputs from three anterior ganglia: the paired commissural ganglia (CoGs), and the single esophageal ganglion (OG). In this paper, we provide the first detailed and quantitative analyses of the short- and long-term effects of removal of these descending inputs (decentralization) on the pyloric rhythm of the STG. Thirty minutes after decentralization, the mean frequency of the pyloric rhythm dropped from 1.20 Hz in control to 0.52 Hz. Whereas the relative phase of pyloric neuron activity was approximately constant across frequency in the controls, after decentralization this changed markedly. Nine control preparations kept for 5–6 d in vitro maintained pyloric rhythm frequencies close to their initial values. Nineteen decentralized preparations kept for 5–6 d dropped slightly in frequency from those seen at 30 min following decentralization, but then displayed stable activity over 6 d. Bouts of higher frequency activity were intermittently seen in both control and decentralized preparations, but the bouts began earlier and were more frequent in the decentralized preparations. Although the bouts may indicate that the removal of the modulatory inputs triggered changes in neuronal excitability, these changes did not produce obvious long-lasting changes in the frequency of the decentralized preparations.

Balancing plasticity/stability across brain development

The potency of the environment to shape brain function changes dramatically across the lifespan. Neural circuits exhibit profound plasticity during early life and are later stabilized. A focus on the cellular and molecular bases of these developmental trajectories has begun to unravel mechanisms, which control the onset and closure of such critical periods. Two important concepts have emerged from the study of critical periods in the visual cortex: (1) excitatory-inhibitory circuit balance is a trigger; and (2) molecular "brakes" limit adult plasticity. The onset of the critical period is determined by the maturation of specific GABA circuits. Targeting these circuits using pharmacological or genetic approaches can trigger premature onset or induce a delay. These manipulations are so powerful that animals of identical chronological age may be at the peak, before, or past their plastic window. Thus, critical period timing per se is plastic. Conversely, one of the outcomes of normal development is to stabilize the neural networks initially sculpted by experience. Rather than being passively lost, the brain's intrinsic potential for plasticity is actively dampened. This is demonstrated by the late expression of brake-like factors, which reversibly limit excessive circuit rewiring beyond a critical period. Interestingly, many of these plasticity regulators are found in the extracellular milieu. Understanding why so many regulators exist, how they interact and, ultimately, how to lift them in noninvasive ways may hold the key to novel therapies and lifelong learning.

Nerve injury-induced neuropathic pain causes disinhibition of the anterior cingulate cortex

Neuropathic pain caused by peripheral nerve injury is a debilitating neurological condition of high clinical relevance. On the cellular level, the elevated pain sensitivity is induced by plasticity of neuronal function along the pain pathway. Changes in cortical areas involved in pain processing contribute to the development of neuropathic pain. Yet, it remains elusive which plasticity mechanisms occur in cortical circuits. We investigated the properties of neural networks in the anterior cingulate cortex (ACC), a brain region mediating affective responses to noxious stimuli. We performed multiple whole-cell recordings from neurons in layer 5 (L5) of the ACC of adult mice after chronic constriction injury of the sciatic nerve of the left hindpaw and observed a striking loss of connections between excitatory and inhibitory neurons in both directions. In contrast, no significant changes in synaptic efficacy in the remaining connected pairs were found. These changes were reflected on the network level by a decrease in the mEPSC and mIPSC frequency. Additionally, nerve injury resulted in a potentiation of the intrinsic excitability of pyramidal neurons, whereas the cellular properties of interneurons were unchanged. Our set of experimental parameters allowed constructing a neuronal network model of L5 in the ACC, revealing that the modification of inhibitory connectivity had the most profound effect on increased network activity. Thus, our combined experimental and modeling approach suggests that cortical disinhibition is a fundamental pathological modification associated with peripheral nerve damage. These changes at the cortical network level might therefore contribute to the neuropathic pain condition.

Synaptic and nonsynaptic plasticity approximating probabilistic inference

Learning and memory operations in neural circuits are believed to involve molecular cascades of synaptic and nonsynaptic changes that lead to a diverse repertoire of dynamical phenomena at higher levels of processing. Hebbian and homeostatic plasticity, neuromodulation, and intrinsic excitability all conspire to form and maintain memories. But it is still unclear how these seemingly redundant mechanisms could jointly orchestrate learning in a more unified system. To this end, a Hebbian learning rule for spiking neurons inspired by Bayesian statistics is proposed. In this model, synaptic weights and intrinsic currents are adapted on-line upon arrival of single spikes, which initiate a cascade of temporally interacting memory traces that locally estimate probabilities associated with relative neuronal activation levels. Trace dynamics enable synaptic learning to readily demonstrate a spike-timing dependence, stably return to a set-point over long time scales, and remain competitive despite this stability. Beyond unsupervised learning, linking the traces with an external plasticity-modulating signal enables spike-based reinforcement learning. At the postsynaptic neuron, the traces are represented by an activity-dependent ion channel that is shown to regulate the input received by a postsynaptic cell and generate intrinsic graded persistent firing levels. We show how spike-based Hebbian-Bayesian learning can be performed in a simulated inference task using integrate-and-fire (IAF) neurons that are Poisson-firing and background-driven, similar to the preferred regime of cortical neurons. Our results support the view that neurons can represent information in the form of probability distributions, and that probabilistic inference could be a functional by-product of coupled synaptic and nonsynaptic mechanisms operating over several timescales. The model provides a biophysical realization of Bayesian computation by reconciling several observed neural phenomena whose functional effects are only partially understood in concert.

Redistribution of Kv2.1 ion channels on spinal motoneurons following peripheral nerve injury

Pathophysiological responses to peripheral nerve injury include alterations in the activity, intrinsic membrane properties and excitability of spinal neurons. The intrinsic excitability of α-motoneurons is controlled in part by the expression, regulation, and distribution of membrane-bound ion channels. Ion channels, such as Kv2.1 and SK, which underlie delayed rectifier potassium currents and afterhyperpolarization respectively, are localized in high-density clusters at specific postsynaptic sites (Deardorff et al., 2013; Muennich and Fyffe, 2004). Previous work has indicated that Kv2.1 channel clustering and kinetics are regulated by a variety of stimuli including ischemia, hypoxia, neuromodulator action and increased activity. Regulation occurs via channel dephosphorylation leading to both declustering and alterations in channel kinetics, thus normalizing activity (Misonou et al., 2004; Misonou et al., 2005; Misonou et al., 2008; Mohapatra et al., 2009; Park et al., 2006). Here we demonstrate using immunohistochemistry that peripheral nerve injury is also sufficient to alter the surface distribution of Kv2.1 channels on motoneurons. The dynamic changes in channel localization include a rapid progressive decline in cluster size, beginning immediately after axotomy, and reaching maximum within one week. With reinnervation, the organization and size of Kv2.1 clusters do not fully recover. However, in the absence of reinnervation Kv2.1 cluster sizes fully recover. Moreover, unilateral peripheral nerve injury evokes parallel, but smaller effects bilaterally. These results suggest that homeostatic regulation of motoneuron Kv2.1 membrane distribution after axon injury is largely independent of axon reinnervation.

Effects of cellular homeostatic intrinsic plasticity on dynamical and computational properties of biological recurrent neural networks

Homeostatic intrinsic plasticity (HIP) is a ubiquitous cellular mechanism regulating neuronal activity, cardinal for the proper functioning of nervous systems. In invertebrates, HIP is critical for orchestrating stereotyped activity patterns. The functional impact of HIP remains more obscure in vertebrate networks, where higher order cognitive processes rely on complex neural dynamics. The hypothesis has emerged that HIP might control the complexity of activity dynamics in recurrent networks, with important computational consequences. However, conflicting results about the causal relationships between cellular HIP, network dynamics, and computational performance have arisen from machine-learning studies. Here, we assess how cellular HIP effects translate into collective dynamics and computational properties in biological recurrent networks. We develop a realistic multiscale model including a generic HIP rule regulating the neuronal threshold with actual molecular signaling pathways kinetics, Dale's principle, sparse connectivity, synaptic balance, and Hebbian synaptic plasticity (SP). Dynamic mean-field analysis and simulations unravel that HIP sets a working point at which inputs are transduced by large derivative ranges of the transfer function. This cellular mechanism ensures increased network dynamics complexity, robust balance with SP at the edge of chaos, and improved input separability. Although critically dependent upon balanced excitatory and inhibitory drives, these effects display striking robustness to changes in network architecture, learning rates, and input features. Thus, the mechanism we unveil might represent a ubiquitous cellular basis for complex dynamics in neural networks. Understanding this robustness is an important challenge to unraveling principles underlying self-organization around criticality in biological recurrent neural networks.

Protein kinase A regulates the long-term potentiation of intrinsic excitability in neonatal trigeminal motoneurons

Although much is known about neuronal plasticity in the mammalian hippocampus and other cortical neurons, the subcellular mechanisms underlying plasticity at the level of motor pools are less well characterized. Protein kinase A (PKA) activation plays an essential role in long-term potentiation of intrinsic excitability (LTP-IE) in layer V (LV) visual cortical neurons and may be involved in other systems as well. Trigeminal motoneurons (TMNs) participate in rhythmical motor behaviors, such as suckling, chewing, and swallowing. Using the whole-cell patch clamp method and various kinase inhibitors and activators, we investigated the mechanism of LTP-IE in neonatal rat TMNs. Ca(2+) depletion using ACSF with 0mM Ca(2+) or the Ca(2+) chelator bis-(o-aminophenoxy)-N,N,N',N'-tetraacetic acid (BAPTA) blocked the long-lasting increase in intrinsic excitability in TMNs, showing that intracellular Ca(2+) during the induction protocol is necessary for the induction of LTP-IE. We next used specific inhibitors of PKA, protein kinase C, and calcium/calmodulin-dependent protein kinase II during the induction protocol. Only the PKA inhibitor H-89 blocked the increase in the firing rate induced by the induction protocol. In addition, forskolin, which activates PKA, induced a long-lasting increase in excitability that resembled the excitability produced by the induction protocol. Thus, we conclude that LTP-IE in TMNs is calcium-dependent, and PKA is the primary regulator of this process.

Activity-dependent downregulation of D-type K+ channel subunit Kv1.2 in rat hippocampal CA3 pyramidal neurons

The intrinsic excitability of neurons plays a critical role in the encoding of memory at Hebbian synapses and in the coupling of synaptic inputs to spike generation. It has not been studied whether somatic firing at a physiologically relevant frequency can induce intrinsic plasticity in hippocampal CA3 pyramidal cells (CA3-PCs). Here, we show that a conditioning train of 20 action potentials (APs) at 10 Hz causes a persistent reduction in the input conductance and an acceleration of the AP onset time in CA3-PCs, but not in CA1-PCs. Induction of such long-term potentiation of intrinsic excitability (LTP-IE) was accompanied by a reduction in the D-type K(+) current, and was abolished by the inhibition of endocytosis or protein tyrosine kinase (PTK). Consistently, the CA3-PCs from Kv1.2 knock-out mice displayed no LTP-IE with the same conditioning. Furthermore, the induction of LTP-IE depended on the back-propagating APs (bAPs) and intact distal apical dendrites. These results indicate that LTP-IE is mediated by the internalization of Kv1.2 channels from the distal regions of apical dendrites, which is triggered by bAP-induced dendritic Ca(2+) signalling and the consequent activation of PTK.

Pumilio-2 regulates translation of Nav1.6 to mediate homeostasis of membrane excitability

The ability to regulate intrinsic membrane excitability, to maintain consistency of action potential firing, is critical for stable neural circuit activity. Without such mechanisms, Hebbian-based synaptic plasticity could push circuits toward activity saturation or, alternatively, quiescence. Although now well documented, the underlying molecular components of these homeostatic mechanisms remain poorly understood. Recent work in the fruit fly, Drosophila melanogaster, has identified Pumilio (Pum), a translational repressor, as an essential component of one such mechanism. In response to changing synaptic excitation, Pum regulates the translation of the voltage-gated sodium conductance, leading to a concomitant adjustment in action potential firing. Although similar homeostatic mechanisms are operational in mammalian neurons, it is unknown whether Pum is similarly involved. In this study, we report that Pum2 is indeed central to the homeostatic mechanism regulating membrane excitability in rat visual cortical pyramidal neurons. Using RNA interference, we observed that loss of Pum2 leads to increased sodium current (I(Na)) and action potential firing, mimicking the response by these neurons to being deprived of synaptic depolarization. In contrast, increased synaptic depolarization results in increased Pum2 expression and subsequent reduction in INa and membrane excitability. We further show that Pum2 is able to directly bind the predominant voltage-gated sodium channel transcript (NaV1.6) expressed in these neurons and, through doing so, regulates translation of this key determinant of membrane excitability. Together, our results show that Pum2 forms part of a homeostatic mechanism that matches membrane excitability to synaptic depolarization in mammalian neurons.

Synergies between intrinsic and synaptic plasticity based on information theoretic learning

In experimental and theoretical neuroscience, synaptic plasticity has dominated the area of neural plasticity for a very long time. Recently, neuronal intrinsic plasticity (IP) has become a hot topic in this area. IP is sometimes thought to be an information-maximization mechanism. However, it is still unclear how IP affects the performance of artificial neural networks in supervised learning applications. From an information-theoretical perspective, the error-entropy minimization (MEE) algorithm has newly been proposed as an efficient training method. In this study, we propose a synergistic learning algorithm combining the MEE algorithm as the synaptic plasticity rule and an information-maximization algorithm as the intrinsic plasticity rule. We consider both feedforward and recurrent neural networks and study the interactions between intrinsic and synaptic plasticity. Simulations indicate that the intrinsic plasticity rule can improve the performance of artificial neural networks trained by the MEE algorithm.

Bayesian computation emerges in generic cortical microcircuits through spike-timing-dependent plasticity

The principles by which networks of neurons compute, and how spike-timing dependent plasticity (STDP) of synaptic weights generates and maintains their computational function, are unknown. Preceding work has shown that soft winner-take-all (WTA) circuits, where pyramidal neurons inhibit each other via interneurons, are a common motif of cortical microcircuits. We show through theoretical analysis and computer simulations that Bayesian computation is induced in these network motifs through STDP in combination with activity-dependent changes in the excitability of neurons. The fundamental components of this emergent Bayesian computation are priors that result from adaptation of neuronal excitability and implicit generative models for hidden causes that are created in the synaptic weights through STDP. In fact, a surprising result is that STDP is able to approximate a powerful principle for fitting such implicit generative models to high-dimensional spike inputs: Expectation Maximization. Our results suggest that the experimentally observed spontaneous activity and trial-to-trial variability of cortical neurons are essential features of their information processing capability, since their functional role is to represent probability distributions rather than static neural codes. Furthermore it suggests networks of Bayesian computation modules as a new model for distributed information processing in the cortex.

M-channels modulate the intrinsic excitability and synaptic responses of layer 2/3 pyramidal neurons in auditory cortex

Neurons in the auditory cortex are believed to utilize temporal patterns of neural activity to accurately process auditory information but the intrinsic neuronal mechanism underlying the control of auditory neural activity is not known. The slowly activating, persistent K(+) channel, also called M-channel that belongs to the Kv7 family, is already known to be important in regulating subthreshold neural excitability and synaptic summation in neocortical and hippocampal pyramidal neurons. However, its functional role in the primary auditory cortex (A1) has never been characterized. In this study, we investigated the roles of M-channels on neuronal excitability, short-term plasticity, and synaptic summation of A1 layer 2/3 regular spiking pyramidal neurons with whole-cell current-clamp recordings in vitro. We found that blocking M-channels with a selective M-channel blocker, XE991, significantly increased neural excitability of A1 layer 2/3 pyramidal neurons. Furthermore, M-channels controled synaptic responses of intralaminar-evoked excitatory postsynaptic potentials (EPSPs); XE991 significantly increased EPSP amplitude, decreased the rate of short-term depression, and increased the synaptic summation. These results suggest that M-channels are involved in controlling spike output patterns and synaptic responses of A1 layer 2/3 pyramidal neurons, which would have important implications in auditory information processing.

Bidirectional plasticity of intrinsic excitability controls sensory inputs efficiency in layer 5 barrel cortex neurons in vivo

Responsiveness of cortical neurons to sensory inputs can be altered by experience and learning. While synaptic plasticity is generally proposed as the underlying cellular mechanism, possible contributions of activity-dependent changes in intrinsic excitability remain poorly investigated. Here, we show that periods of rhythmic firing in rat barrel cortex layer 5 pyramidal neurons can trigger a long-lasting increase or decrease in their membrane excitability in vivo. Potentiation of cortical excitability consisted of an increased firing in response to intracellular stimulation and a reduction in threshold current for spike initiation. Conversely, depression of cortical excitability was evidenced by an augmented firing threshold leading to a reduced current-evoked spiking. The direction of plasticity depended on the baseline level of spontaneous firing rate and cell excitability. We also found that changes in intrinsic excitability were accompanied by corresponding modifications in the effectiveness of sensory inputs. Potentiation and depression of cortical neuron excitability resulted, respectively, in an increased or decreased firing probability on whisker-evoked synaptic responses, without modifications in the synaptic strength itself. These data suggest that bidirectional intrinsic plasticity could play an important role in experience-dependent refinement of sensory cortical networks.

Regional and temporal specificity of intrinsic plasticity mechanisms in rodent primary visual cortex

Different neocortical regions are functionally specialized, but whether this specialization is reflected in the forms of plasticity present during developmental critical periods (CPs) is largely unknown. In rodent visual cortex, we recently showed that a form of intrinsic plasticity [LTP of intrinsic excitability (LTP-IE)] in the monocular region of the primary visual cortex (V1M) plays an important role in modulating cortical responsiveness following visual deprivation. Here we ask whether LTP-IE is present and similarly regulated by visual experience in the binocular region of the primary visual cortex (V1B), where inputs from the two eyes compete during the CP. In contrast to V1M, where LTP-IE is present throughout the CP, in V1B LTP-IE was only transiently expressed at the onset of the CP. Also distinct from V1M, brief monocular deprivation (MD) was unable to modulate LTP-IE magnitude in V1B, and even binocular deprivation (the equivalent of MD in V1M) could only influence LTP-IE expression during a narrow time window at the peak of the CP. Finally, we asked whether these differences depend on differences in sensory activation of the two areas during development. MD of ipsilateral inputs from before eye opening (to reduce competitive interactions) did not affect the pattern of LTP-IE expression in V1B. Further, the differences in plasticity in the two cortical areas persisted when animals were reared in the dark to remove all patterned visual input. Thus neocortical LTP-IE expression shows dramatic regional and temporal differentiation, and these differences are not driven by differences in sensory experience.