Brain plasticity and ion channels

It is generally believed that spatio-temporal configurations of distributed activity in the brain contribute to the coding of neuronal information and that synaptic contacts between nerve cells could play a central role in the formation of privileged pathways of activity. Synaptic plasticity is not the only mode of regulation of information processing in the brain and persistent regulations of ionic conductances in some specialized neuronal areas such as the dendrites, the cell body and the axon could also modulate, in the short- and the long-term, the propagation of information in the brain. Persistent changes in intrinsic excitability have been reported in several brain areas in which activity is modified during a classical conditioning. The role of synaptic activity seems to be determinant in the induction but the learning rules and the underlying mechanisms remain to be defined. This review discusses the role of neuronal activity in the induction of intrinsic plasticity in cortical, hippocampal and cerebellar neurons. Activation and inactivation properties of ionic channels in the axon determine the short-term dynamics of axonal propagation and synaptic transmission. Activation of glutamate receptors initiates a long-term modification in neuronal excitability that may represent the substrate for the mnesic engram and for the stabilization of the epileptic state. Similarly to synaptic plasticity, long-lasting intrinsic plasticity appears to be reversible and to express a certain level of input or cellular specificity. These non-synaptic forms of plasticity affect the signal propagation in the axon, the dendrites and the soma. They not only share common learning rules and induction pathways with the better known synaptic plasticity such as NMDA receptor-dependent LTP and LTD but also contribute in synergy with these synaptic changes to the formation of a coherent mnesic engram.     

Integrated mechanisms of anticipation and rate-of-change computations in cortical circuits

Local neocortical circuits are characterized by stereotypical physiological and structural features that subserve generic computational operations. These basic computations of the cortical microcircuit emerge through the interplay of neuronal connectivity, cellular intrinsic properties, and synaptic plasticity dynamics. How these interacting mechanisms generate specific computational operations in the cortical circuit remains largely unknown. Here, we identify the neurophysiological basis of both the rate of change and anticipation computations on synaptic inputs in a cortical circuit. Through biophysically realistic computer simulations and neuronal recordings, we show that the rate-of-change computation is operated robustly in cortical networks through the combination of two ubiquitous brain mechanisms: short-term synaptic depression and spike-frequency adaptation. We then show how this rate-of-change circuit can be embedded in a convergently connected network to anticipate temporally incoming synaptic inputs, in quantitative agreement with experimental findings on anticipatory responses to moving stimuli in the primary visual cortex. Given the robustness of the mechanism and the widespread nature of the physiological machinery involved, we suggest that rate-of-change computation and temporal anticipation are principal, hard-wired functions of neural information processing in the cortical microcircuit.     

Consistency and Diversity of Spike Dynamics in the Neurons of Bed Nucleus of Stria Terminalis of the Rat: A Dynamic Clamp Study

Neurons display a high degree of variability and diversity in the expression and regulation of their voltage-dependent ionic channels. Under low level of synaptic background a number of physiologically distinct cell types can be identified in most brain areas that display different responses to standard forms of intracellular current stimulation. Nevertheless, it is not well understood how biophysically different neurons process synaptic inputs in natural conditions, i.e., when experiencing intense synaptic bombardment in vivo. While distinct cell types might process synaptic inputs into different patterns of action potentials representing specific “motifs” of network activity, standard methods of electrophysiology are not well suited to resolve such questions. In the current paper we performed dynamic clamp experiments with simulated synaptic inputs that were presented to three types of neurons in the juxtacapsular bed nucleus of stria terminalis (jcBNST) of the rat. Our analysis on the temporal structure of firing showed that the three types of jcBNST neurons did not produce qualitatively different spike responses under identical patterns of input. However, we observed consistent, cell type dependent variations in the fine structure of firing, at the level of single spikes. At the millisecond resolution structure of firing we found high degree of diversity across the entire spectrum of neurons irrespective of their type. Additionally, we identified a new cell type with intrinsic oscillatory properties that produced a rhythmic and regular firing under synaptic stimulation that distinguishes it from the previously described jcBNST cell types. Our findings suggest a sophisticated, cell type dependent regulation of spike dynamics of neurons when experiencing a complex synaptic background. The high degree of their dynamical diversity has implications to their cooperative dynamics and synchronization.     

From synapse to network: models of information storage and retrieval in neural circuits

The mechanisms of information storage and retrieval in brain circuits are still the subject of debate. It is widely believed that information is stored at least in part through changes in synaptic connectivity in networks that encode this information and that these changes lead in turn to modifications of network dynamics, such that the stored information can be retrieved at a later time. Here, we review recent progress in deriving synaptic plasticity rules from experimental data and in understanding how plasticity rules affect the dynamics of recurrent networks. We show that the dynamics generated by such networks exhibit a large degree of diversity, depending on parameters, similar to experimental observations in vivo during delayed response tasks.     

Task-Driven Activity Reduces the Cortical Activity Space of the Brain: Experiment and Whole-Brain Modeling

How a stimulus or a task alters the spontaneous dynamics of the brain remains a fundamental open question in neuroscience. One of the most robust hallmarks of task/stimulus-driven brain dynamics is the decrease of variability with respect to the spontaneous level, an effect seen across multiple experimental conditions and in brain signals observed at different spatiotemporal scales. Recently, it was observed that the trial-to-trial variability and temporal variance of functional magnetic resonance imaging (fMRI) signals decrease in the task-driven activity. Here we examined the dynamics of a large-scale model of the human cortex to provide a mechanistic understanding of these observations. The model allows computing the statistics of synaptic activity in the spontaneous condition and in putative tasks determined by external inputs to a given subset of brain regions. We demonstrated that external inputs decrease the variance, increase the covariances, and decrease the autocovariance of synaptic activity as a consequence of single node and large-scale network dynamics. Altogether, these changes in network statistics imply a reduction of entropy, meaning that the spontaneous synaptic activity outlines a larger multidimensional activity space than does the task-driven activity. We tested this model’s prediction on fMRI signals from healthy humans acquired during rest and task conditions and found a significant decrease of entropy in the stimulus-driven activity. Altogether, our study proposes a mechanism for increasing the information capacity of brain networks by enlarging the volume of possible activity configurations at rest and reliably settling into a confined stimulus-driven state to allow better transmission of stimulus-related information.     

Long-term potentiation in rat hippocampal neurons is accompanied by spatially widespread changes in intrinsic oscillatory dynamics and excitability

Oscillations in neural activity are a prominent feature of many brain states. Individual hippocampal neurons exhibit intrinsic membrane potential oscillations and intrinsic resonance in the theta frequency range. We found that the subthreshold resonance frequency of CA1 pyramidal neurons was location dependent, varying more than 3-fold between the soma and the distal dendrites. Furthermore, activity- and NMDA-receptor-dependent long-term plasticity increased this resonance frequency through changes in h channel properties. The increase in resonance frequency and an associated reduction in excitability were nearly identical in the soma and the first 300 mum of the apical dendrites. These spatially widespread changes accompanying long-term synaptic potentiation also reduced the neuron's ability to elicit spikes evoked through a nonpotentiated synaptic pathway. Our results suggest that the frequency response of these neurons depends on the dendritic location of their inputs and that activity can regulate their response dynamics within an oscillating neural network.     

Synaptic Efficacy as a Function of Ionotropic Receptor Distribution: A Computational Study

Glutamatergic synapses are the most prevalent functional elements of information processing in the brain. Changes in pre-synaptic activity and in the function of various post-synaptic elements contribute to generate a large variety of synaptic responses. Previous studies have explored postsynaptic factors responsible for regulating synaptic strength variations, but have given far less importance to synaptic geometry, and more specifically to the subcellular distribution of ionotropic receptors. We analyzed the functional effects resulting from changing the subsynaptic localization of ionotropic receptors by using a hippocampal synaptic computational framework. The present study was performed using the EONS (Elementary Objects of the Nervous System) synaptic modeling platform, which was specifically developed to explore the roles of subsynaptic elements as well as their interactions, and that of synaptic geometry. More specifically, we determined the effects of changing the localization of ionotropic receptors relative to the presynaptic glutamate release site, on synaptic efficacy and its variations following single pulse and paired-pulse stimulation protocols. The results indicate that changes in synaptic geometry do have consequences on synaptic efficacy and its dynamics.     

A working memory model for serial order that stores information in the intrinsic excitability properties of neurons

Models for temporary information storage in neuronal populations are dominated by mechanisms directly dependent on synaptic plasticity. There are nevertheless other mechanisms available that are well suited for creating short-term memories. Here we present a model for working memory which relies on the modulation of the intrinsic excitability properties of neurons, instead of synaptic plasticity, to retain novel information for periods of seconds to minutes. We show that it is possible to effectively use this mechanism to store the serial order in a sequence of patterns of activity. For this we introduce a functional class of neurons, named gate interneurons, which can store information in their membrane dynamics and can literally act as gates routing the flow of activations in the principal neurons population. The presented model exhibits properties which are in close agreement with experimental results in working memory. Namely, the recall process plays an important role in stabilizing and prolonging the memory trace. This means that the stored information is correctly maintained as long as it is being used. Moreover, the working memory model is adequate for storing completely new information, in time windows compatible with the notion of “one-shot” learning (hundreds of milliseconds).     

Shift in the intrinsic excitability of medial prefrontal cortex neurons following training in impulse control and cued-responding tasks

Impulse control is an executive process that allows animals to inhibit their actions until an appropriate time. Previously, we reported that learning a simple response inhibition task increases AMPA currents at excitatory synapses in the prelimbic region of the medial prefrontal cortex (mPFC). Here, we examined whether modifications to intrinsic excitability occurred alongside the synaptic changes. To that end, we trained rats to obtain a food reward in a response inhibition task by withhold responding on a lever until they were signaled to respond. We then measured excitability, using whole-cell patch clamp recordings in brain slices, by quantifying action potentials generated by the injection of depolarizing current steps. Training in this task depressed the excitability of layer V pyramidal neurons of the prelimbic, but not infralimbic, region of the mPFC relative to behavioral controls. This decrease in maximum spiking frequency was significantly correlated with performance on the final session of the task. This change in intrinsic excitability may represent a homeostatic mechanism counterbalancing increased excitatory synaptic inputs onto those neurons in trained rats. Interestingly, subjects trained with a cue that predicted imminent reward availability had increased excitability in infralimbic, but not the prelimbic, pyramidal neurons. This dissociation suggests that both prelimbic and infralimbic neurons are involved in directing action, but specialized for different types of information, inhibitory or anticipatory, respectively.     

Network Self-Organization Explains the Statistics and Dynamics of Synaptic Connection Strengths in Cortex

The information processing abilities of neural circuits arise from their synaptic connection patterns. Understanding the laws governing these connectivity patterns is essential for understanding brain function. The overall distribution of synaptic strengths of local excitatory connections in cortex and hippocampus is long-tailed, exhibiting a small number of synaptic connections of very large efficacy. At the same time, new synaptic connections are constantly being created and individual synaptic connection strengths show substantial fluctuations across time. It remains unclear through what mechanisms these properties of neural circuits arise and how they contribute to learning and memory. In this study we show that fundamental characteristics of excitatory synaptic connections in cortex and hippocampus can be explained as a consequence of self-organization in a recurrent network combining spike-timing-dependent plasticity (STDP), structural plasticity and different forms of homeostatic plasticity. In the network, associative synaptic plasticity in the form of STDP induces a rich-get-richer dynamics among synapses, while homeostatic mechanisms induce competition. Under distinctly different initial conditions, the ensuing self-organization produces long-tailed synaptic strength distributions matching experimental findings. We show that this self-organization can take place with a purely additive STDP mechanism and that multiplicative weight dynamics emerge as a consequence of network interactions. The observed patterns of fluctuation of synaptic strengths, including elimination and generation of synaptic connections and long-term persistence of strong connections, are consistent with the dynamics of dendritic spines found in rat hippocampus. Beyond this, the model predicts an approximately power-law scaling of the lifetimes of newly established synaptic connection strengths during development. Our results suggest that the combined action of multiple forms of neuronal plasticity plays an essential role in the formation and maintenance of cortical circuits.     

Neuromodulation by Glutamate and Acetylcholine can Change Circuit Dynamics by Regulating the Relative Influence of Afferent Input and Excitatory Feedback

Substances such as acetylcholine and glutamate act as both neurotransmitters and neuromodulators. As neuromodulators, they change neural information processing by regulating synaptic transmitter release, altering baseline membrane potential and spiking activity, and modifying long-term synaptic plasticity. Slice physiology research has demonstrated that many neuromodulators differentially modulate afferent, incoming information compared to intrinsic and recurrent processing in cortical structures such as piriform cortex, neocortex, and the hippocampus. The enhancement of afferent (external) pathways versus the suppression at recurrent (internal) pathways could cause cortical dynamics to switch between a predominant influence of external stimulation to a predominant influence of internal recall. Modulation of afferent versus intrinsic processing could contribute to the role of neuromodulators in regulating attention, learning, and memory effects in behavior.     

Clustered dynamics of inhibitory synapses and dendritic spines in the adult neocortex

A key feature of the mammalian brain is its capacity to adapt in response to experience, in part by remodeling of synaptic connections between neurons. Excitatory synapse rearrangements have been monitored in vivo by observation of dendritic spine dynamics, but lack of a vital marker for inhibitory synapses has precluded their observation. Here, we simultaneously monitor in vivo inhibitory synapse and dendritic spine dynamics across the entire dendritic arbor of pyramidal neurons in the adult mammalian cortex using large-volume, high-resolution dual-color two-photon microscopy. We find that inhibitory synapses on dendritic shafts and spines differ in their distribution across the arbor and in their remodeling kinetics during normal and altered sensory experience. Further, we find inhibitory synapse and dendritic spine remodeling to be spatially clustered and that clustering is influenced by sensory input. Our findings provide in vivo evidence for local coordination of inhibitory and excitatory synaptic rearrangements.     

Inhibitory transmission, activity-dependent ionic changes and neuronal network oscillations

Oscillatory network activity arises from interactions between synaptic and intrinsic membrane properties of neurons. In this review, we summarize general mechanisms of synchronous neuronal oscillations. In addition, we focus on recent experimental and computational studies which suggest that activity-dependent changes of ionic environment can affect both the synaptic and intrinsic neuronal properties and influence the network behavior. GABA(A) receptor (GABA(A)R)-mediated signaling, that is based on Cl(-) and HCO(3)(-) permeability, is thought to be important for the oscillogenesis and synchronization in cortical networks. A remarkable feature of GABAergic synapses is that prolonged GABA(A)R activation may lead to switching from a hyperpolarizing to a depolarizing response. This is partly due to a positive shift of the GABA(A) R reversal potential (E(GABA)) that is generated by GABA-induced Cl(-) accumulation in neurons. Recent studies suggest that activity-dependent E(GABA) changes may have important implications for the mechanisms of gamma oscillations and seizure-like discharges. Thus, a better understanding of the impact of intracellular Cl(-) dynamics on network behavior may provide insights into the mechanisms of physiological and pathological brain rhythms. Combination of experiments and simulations is a promising approach for elucidating which properties of the time-varying ionic environment can shape the dynamics of a given circuit.     

Neurobiologically realistic determinants of self-organized criticality in networks of spiking neurons

Self-organized criticality refers to the spontaneous emergence of self-similar dynamics in complex systems poised between order and randomness. The presence of self-organized critical dynamics in the brain is theoretically appealing and is supported by recent neurophysiological studies. Despite this, the neurobiological determinants of these dynamics have not been previously sought. Here, we systematically examined the influence of such determinants in hierarchically modular networks of leaky integrate-and-fire neurons with spike-timing-dependent synaptic plasticity and axonal conduction delays. We characterized emergent dynamics in our networks by distributions of active neuronal ensemble modules (neuronal avalanches) and rigorously assessed these distributions for power-law scaling. We found that spike-timing-dependent synaptic plasticity enabled a rapid phase transition from random subcritical dynamics to ordered supercritical dynamics. Importantly, modular connectivity and low wiring cost broadened this transition, and enabled a regime indicative of self-organized criticality. The regime only occurred when modular connectivity, low wiring cost and synaptic plasticity were simultaneously present, and the regime was most evident when between-module connection density scaled as a power-law. The regime was robust to variations in other neurobiologically relevant parameters and favored systems with low external drive and strong internal interactions. Increases in system size and connectivity facilitated internal interactions, permitting reductions in external drive and facilitating convergence of postsynaptic-response magnitude and synaptic-plasticity learning rate parameter values towards neurobiologically realistic levels. We hence infer a novel association between self-organized critical neuronal dynamics and several neurobiologically realistic features of structural connectivity. The central role of these features in our model may reflect their importance for neuronal information processing.     

Linear-nonlinear cascades capture synaptic dynamics

Short-term synaptic dynamics differ markedly across connections and strongly regulate how action potentials communicate information. To model the range of synaptic dynamics observed in experiments, we have developed a flexible mathematical framework based on a linear-nonlinear operation. This model can capture various experimentally observed features of synaptic dynamics and different types of heteroskedasticity. Despite its conceptual simplicity, we show that it is more adaptable than previous models. Combined with a standard maximum likelihood approach, synaptic dynamics can be accurately and efficiently characterized using naturalistic stimulation patterns. These results make explicit that synaptic processing bears algorithmic similarities with information processing in convolutional neural networks.     

Nitric oxide is an activity-dependent regulator of target neuron intrinsic excitability

Activity-dependent changes in synaptic strength are well established as mediating long-term plasticity underlying learning and memory, but modulation of?target neuron excitability could complement changes in synaptic strength and regulate network activity. It is thought that homeostatic mechanisms match intrinsic excitability to the incoming synaptic drive, but evidence for involvement of voltage-gated conductances is sparse. Here, we show that glutamatergic synaptic activity modulates target neuron excitability and switches the basis of action potential repolarization from Kv3 to Kv2 potassium channel dominance, thereby adjusting neuronal signaling between low and high activity states, respectively. This nitric oxide-mediated signaling dramatically increases Kv2 currents in both the auditory brain stem and hippocampus (>3-fold) transforming synaptic integration and information transmission but with only modest changes in action potential waveform. We conclude that nitric oxide is a homeostatic regulator, tuning neuronal excitability to the recent history of excitatory synaptic inputs over intervals of minutes to hours.     

Modulating the Intrinsic Disorder in the Cytoplasmic Domain Alters the Biological Activity of the N-Methyl-d-aspartate-sensitive Glutamate Receptor

The NMDA-sensitive glutamate receptor is a ligand-gated ion channel that mediates excitatory synaptic transmission in the nervous system. Extracellular zinc allosterically regulates the NMDA receptor by binding to the extracellular N-terminal domain, which inhibits channel gating. Phosphorylation of the intrinsically disordered intracellular C-terminal domain alleviates inhibition by extracellular zinc. The mechanism for this functional effect is largely unknown. Proline is a hallmark of intrinsic disorder, so we used proline mutagenesis to modulate disorder in the cytoplasmic domain. Proline depletion selectively uncoupled zinc inhibition with little effect on receptor biogenesis, surface trafficking, or ligand-activated gating. Proline depletion also reduced the affinity for a PDZ domain involved in synaptic trafficking and affected small molecule binding. To understand the origin of these phenomena, we used single molecule fluorescence and ensemble biophysical methods to characterize the structural effects of proline mutagenesis. Proline depletion did not eliminate intrinsic disorder, but the underlying conformational dynamics were changed. Thus, we altered the form of intrinsic disorder, which appears sufficient to affect the biological activity. These findings suggest that conformational dynamics within the intrinsically disordered cytoplasmic domain are important for the allosteric regulation of NMDA receptor gating.     

Single vesicle tracking for studying synaptic vesicle dynamics in small central synapses

Sustained neurotransmission is driven by a continuous supply of synaptic vesicles to the release sites and modulated by synaptic vesicle dynamics. However, synaptic vesicle dynamics in synapses remain elusive because of technical limitations. Recent advances in fluorescence imaging techniques have enabled the tracking of single synaptic vesicles in small central synapses in living neurons. Single vesicle tracking has uncovered a wealth of new information about synaptic vesicle dynamics both within and outside presynaptic terminals, showing that single vesicle tracking is an effective tool for studying synaptic vesicle dynamics. Particularly, single vesicle tracking with high spatiotemporal resolution has revealed the dependence of synaptic vesicle dynamics on the location, stages of recycling, and neuronal activity. This review summarizes the recent findings from single synaptic vesicle tracking in small central synapses and their implications in synaptic transmission and pathogenic mechanisms of neurodegenerative diseases.     

Profilin2 contributes to synaptic vesicle exocytosis, neuronal excitability, and novelty-seeking behavior

Profilins are actin binding proteins essential for regulating cytoskeletal dynamics, however, their function in the mammalian nervous system is unknown. Here, we provide evidence that in mouse brain profilin1 and profilin2 have distinct roles in regulating synaptic actin polymerization with profilin2 preferring a WAVE-complex-mediated pathway. Mice lacking profilin2 show a block in synaptic actin polymerization in response to depolarization, which is accompanied by increased synaptic excitability of glutamatergic neurons due to higher vesicle exocytosis. These alterations in neurotransmitter release correlate with a hyperactivation of the striatum and enhanced novelty-seeking behavior in profilin2 mutant mice. Our results highlight a novel, profilin2-dependent pathway, regulating synaptic physiology, neuronal excitability, and complex behavior.     

Regulation of AMPA receptor trafficking and synaptic plasticity

AMPA receptors (AMPARs) mediate the majority of fast excitatory synaptic transmission in the brain. Dynamic changes in neuronal synaptic efficacy, termed synaptic plasticity, are thought to underlie information coding and storage in learning and memory. One major mechanism that regulates synaptic strength involves the tightly regulated trafficking of AMPARs into and out of synapses. The life cycle of AMPARs from their biosynthesis, membrane trafficking, and synaptic targeting to their degradation are controlled by a series of orchestrated interactions with numerous intracellular regulatory proteins. Here we review recent progress made toward the understanding the regulation of AMPAR trafficking, focusing on the roles of several key intracellular AMPAR interacting proteins.