Synaptic depression and cortical gain control

Axonal and synaptic failure suppress the transfer of firing rate oscillations, synchrony and information during high frequency deep brain stimulation

High frequency deep brain stimulation (DBS) of the subthalamic nucleus (STN) is a widely used treatment for Parkinson's disease, but its effects on neural activity in basal ganglia circuits are not fully understood. DBS increases the excitation of STN efferents yet decouples STN spiking patterns from the spiking patterns of STN synaptic targets. We propose that this apparent paradox is resolved by recent studies showing an increased rate of axonal and synaptic failures in STN projections during DBS. To investigate this hypothesis, we combine in vitro and in vivo recordings to derive a computational model of axonal and synaptic failure during DBS. Our model shows that these failures induce a short term depression that suppresses the synaptic transfer of firing rate oscillations, synchrony and rate-coded information from STN to its synaptic targets. In particular, our computational model reproduces the widely reported suppression of parkinsonian β oscillations and synchrony during DBS. Our results support the idea that short term depression is a therapeutic mechanism of STN DBS that works as a functional lesion by decoupling the somatic spiking patterns of STN neurons from spiking activity in basal ganglia output nuclei.

Learning contrast-invariant cancellation of redundant signals in neural systems

Cancellation of redundant information is a highly desirable feature of sensory systems, since it would potentially lead to a more efficient detection of novel information. However, biologically plausible mechanisms responsible for such selective cancellation, and especially those robust to realistic variations in the intensity of the redundant signals, are mostly unknown. In this work, we study, via in vivo experimental recordings and computational models, the behavior of a cerebellar-like circuit in the weakly electric fish which is known to perform cancellation of redundant stimuli. We experimentally observe contrast invariance in the cancellation of spatially and temporally redundant stimuli in such a system. Our model, which incorporates heterogeneously-delayed feedback, bursting dynamics and burst-induced STDP, is in agreement with our in vivo observations. In addition, the model gives insight on the activity of granule cells and parallel fibers involved in the feedback pathway, and provides a strong prediction on the parallel fiber potentiation time scale. Finally, our model predicts the existence of an optimal learning contrast around 15% contrast levels, which are commonly experienced by interacting fish.

Information and efficiency in the nervous system--a synthesis

In systems biology, questions concerning the molecular and cellular makeup of an organism are of utmost importance, especially when trying to understand how unreliable components—like genetic circuits, biochemical cascades, and ion channels, among others—enable reliable and adaptive behaviour. The repertoire and speed of biological computations are limited by thermodynamic or metabolic constraints: an example can be found in neurons, where fluctuations in biophysical states limit the information they can encode—with almost 20–60% of the total energy allocated for the brain used for signalling purposes, either via action potentials or by synaptic transmission. Here, we consider the imperatives for neurons to optimise computational and metabolic efficiency, wherein benefits and costs trade-off against each other in the context of self-organised and adaptive behaviour. In particular, we try to link information theoretic (variational) and thermodynamic (Helmholtz) free-energy formulations of neuronal processing and show how they are related in a fundamental way through a complexity minimisation lemma.

Gain control network conditions in early sensory coding

Gain control is essential for the proper function of any sensory system. However, the precise mechanisms for achieving effective gain control in the brain are unknown. Based on our understanding of the existence and strength of connections in the insect olfactory system, we analyze the conditions that lead to controlled gain in a randomly connected network of excitatory and inhibitory neurons. We consider two scenarios for the variation of input into the system. In the first case, the intensity of the sensory input controls the input currents to a fixed proportion of neurons of the excitatory and inhibitory populations. In the second case, increasing intensity of the sensory stimulus will both, recruit an increasing number of neurons that receive input and change the input current that they receive. Using a mean field approximation for the network activity we derive relationships between the parameters of the network that ensure that the overall level of activity of the excitatory population remains unchanged for increasing intensity of the external stimulation. We find that, first, the main parameters that regulate network gain are the probabilities of connections from the inhibitory population to the excitatory population and of the connections within the inhibitory population. Second, we show that strict gain control is not achievable in a random network in the second case, when the input recruits an increasing number of neurons. Finally, we confirm that the gain control conditions derived from the mean field approximation are valid in simulations of firing rate models and Hodgkin-Huxley conductance based models.

Dynamic excitatory and inhibitory gain modulation can produce flexible, robust and optimal decision-making

Behavioural and neurophysiological studies in primates have increasingly shown the involvement of urgency signals during the temporal integration of sensory evidence in perceptual decision-making. Neuronal correlates of such signals have been found in the parietal cortex, and in separate studies, demonstrated attention-induced gain modulation of both excitatory and inhibitory neurons. Although previous computational models of decision-making have incorporated gain modulation, their abstract forms do not permit an understanding of the contribution of inhibitory gain modulation. Thus, the effects of co-modulating both excitatory and inhibitory neuronal gains on decision-making dynamics and behavioural performance remain unclear. In this work, we incorporate time-dependent co-modulation of the gains of both excitatory and inhibitory neurons into our previous biologically based decision circuit model. We base our computational study in the context of two classic motion-discrimination tasks performed in animals. Our model shows that by simultaneously increasing the gains of both excitatory and inhibitory neurons, a variety of the observed dynamic neuronal firing activities can be replicated. In particular, the model can exhibit winner-take-all decision-making behaviour with higher firing rates and within a significantly more robust model parameter range. It also exhibits short-tailed reaction time distributions even when operating near a dynamical bifurcation point. The model further shows that neuronal gain modulation can compensate for weaker recurrent excitation in a decision neural circuit, and support decision formation and storage. Higher neuronal gain is also suggested in the more cognitively demanding reaction time than in the fixed delay version of the task. Using the exact temporal delays from the animal experiments, fast recruitment of gain co-modulation is shown to maximize reward rate, with a timescale that is surprisingly near the experimentally fitted value. Our work provides insights into the simultaneous and rapid modulation of excitatory and inhibitory neuronal gains, which enables flexible, robust, and optimal decision-making.

Perceptual decision-making involves not only simple transformation of sensory information to a motor decision, but can also be modulated by high-level cognition. For example, the latter may include strategic allocation of limited attentional resources over time in a decision task to improve performance. At the neurophysiological level, there is evidence supporting attention-induced neuronal gain modulation of both excitatory and inhibitory cortical neurons. In the context of perceptual discrimination tasks performed by animals, we make use of a biologically inspired computational model of decision-making to understand the computational capabilities of such co-modulation of neuronal gains. We find that dynamic co-modulation of both excitatory and inhibitory neurons is important for flexible, and cognitively demanding decision-making while also enhancing robustness in the decision circuit's functions. Our model captures the neuronal activity and behavioural data in the animal experiments remarkably well. Decision performance in a reaction time task can be optimized, maximizing the rate of receiving reward by using fast gain recruitment. Our experimentally fitted timescale is near the optimal one, suggesting that the animals performed almost optimally. By providing both computational simulations and theoretical analyses, our computational model sheds light into the multiple functions of rapid co-modulation of neuronal gains during decision-making.

Resolution enhancement in neural networks with dynamical synapses

Conventionally, information is represented by spike rates in the neural system. Here, we consider the ability of temporally modulated activities in neuronal networks to carry information extra to spike rates. These temporal modulations, commonly known as population spikes, are due to the presence of synaptic depression in a neuronal network model. We discuss its relevance to an experiment on transparent motions in macaque monkeys by Treue et al. in 2000. They found that if the moving directions of objects are too close, the firing rate profile will be very similar to that with one direction. As the difference in the moving directions of objects is large enough, the neuronal system would respond in such a way that the network enhances the resolution in the moving directions of the objects. In this paper, we propose that this behavior can be reproduced by neural networks with dynamical synapses when there are multiple external inputs. We will demonstrate how resolution enhancement can be achieved, and discuss the conditions under which temporally modulated activities are able to enhance information processing performances in general.

Probabilistic inference of short-term synaptic plasticity in neocortical microcircuits

Short-term synaptic plasticity is highly diverse across brain area, cortical layer, cell type, and developmental stage. Since short-term plasticity (STP) strongly shapes neural dynamics, this diversity suggests a specific and essential role in neural information processing. Therefore, a correct characterization of short-term synaptic plasticity is an important step towards understanding and modeling neural systems. Phenomenological models have been developed, but they are usually fitted to experimental data using least-mean-square methods. We demonstrate that for typical synaptic dynamics such fitting may give unreliable results. As a solution, we introduce a Bayesian formulation, which yields the posterior distribution over the model parameters given the data. First, we show that common STP protocols yield broad distributions over some model parameters. Using our result we propose a experimental protocol to more accurately determine synaptic dynamics parameters. Next, we infer the model parameters using experimental data from three different neocortical excitatory connection types. This reveals connection-specific distributions, which we use to classify synaptic dynamics. Our approach to demarcate connection-specific synaptic dynamics is an important improvement on the state of the art and reveals novel features from existing data.

Matched pre- and post-synaptic changes underlie synaptic plasticity over long time scales

Modifications of synaptic efficacies are considered essential for learning and memory. However, it is not known how the underlying functional components of synaptic transmission change over long time scales. To address this question, we studied cortical synapses from young Wistar rats before and after 12 h intervals of spontaneous or glutamate-induced spiking activity. We found that, under these conditions, synaptic efficacies can increase or decrease by up to 10-fold. Statistical analyses reveal that these changes reflect modifications in the number of presynaptic release sites, together with postsynaptic changes that maintain the quantal size per release site. The quantitative relation between the presynaptic and postsynaptic transmission components was not affected when synaptic plasticity was enhanced or reduced using a broad range of pharmacological agents. These findings suggest that ongoing synaptic plasticity results in matched presynaptic and postsynaptic modifications, in which elementary modules that span the synaptic cleft are added or removed as a function of experience.

A model of microsaccade-related neural responses induced by short-term depression in thalamocortical synapses

Microsaccades during fixation have been suggested to counteract visual fading. Recent experiments have also observed microsaccade-related neural responses from cellular record, scalp electroencephalogram (EEG), and functional magnetic resonance imaging (fMRI). The underlying mechanism, however, is not yet understood and highly debated. It has been proposed that the neural activity of primary visual cortex (V1) is a crucial component for counteracting visual adaptation. In this paper, we use computational modeling to investigate how short-term depression (STD) in thalamocortical synapses might affect the neural responses of V1 in the presence of microsaccades. Our model not only gives a possible synaptic explanation for microsaccades in counteracting visual fading, but also reproduces several features in experimental findings. These modeling results suggest that STD in thalamocortical synapses plays an important role in microsaccade-related neural responses and the model may be useful for further investigation of behavioral properties and functional roles of microsaccades.

Alteration of synaptic plasticity by neonatal seizures in rat somatosensory cortex

Seizures in newborns are associated with a high risk for subsequent epilepsy and adverse neurodevelopmental consequences. Understanding the mechanisms by which neonatal seizures adversely disturb the immature brain is important in developing therapeutic strategies. Using the convulsant agent flurothyl to mimic repetitive neonatal seizures we show that early-life seizures result in long-term alteration in the maintenance phase of long-term potentiation (LTP) in layer IV to layer II/III synapses of the somatosensory cortex without alteration of basal synaptic transmission, the induction phase of LTP and short-term depression. Such alterations may have a role in functional deficits seen following neonatal seizures.

Theoretical models of synaptic short term plasticity

Short term plasticity is a highly abundant form of rapid, activity-dependent modulation of synaptic efficacy. A shared set of mechanisms can cause both depression and enhancement of the postsynaptic response at different synapses, with important consequences for information processing. Mathematical models have been extensively used to study the mechanisms and roles of short term plasticity. This review provides an overview of existing models and their biological basis, and of their main properties. Special attention will be given to slow processes such as calcium channel inactivation and the effect of activation of presynaptic autoreceptors.

All layers of medial entorhinal cortex receive presubicular and parasubicular inputs

The medial entorhinal cortex (MEC), presubiculum (PrS), and parasubiculum (PaS) are interconnected components of the hippocampal-parahippocampal spatial-representation system. Principal cells in all layers of MEC show signs of directional tuning, overt in head direction cells present in all layers except for layer II, and covert in grid cells, which are the major spatially modulated cell type in layer II. Directional information likely originates in the head direction-vestibular system and PrS and PaS are thought to provide this information to MEC. Efferents from PaS and PrS show a selective laminar terminal distribution in MEC superficial layers II and III, respectively. We hypothesized that this anatomically determined laminar distribution does not preclude monosynaptic interaction with neurons located in deeper layers of MEC in view of the extensive apical dendrites from deeper cells reaching layers II and III. This hypothesis was tested in the rat using tilted in vitro slices in which origins and terminations of PrS and PaS fibers were maintained, as assessed using anterograde anatomical tracing. Based on voltage-sensitive dye imaging, multipatch single-cell recordings, and scanning photostimulation of caged glutamate, we report first that principal neurons in all layers of MEC receive convergent monosynaptic inputs from PrS and PaS and second, that elicited responses show layer-specific decay times and frequency-dependent facilitation. These results indicate that regardless of their selective laminar terminal distribution, PrS and PaS inputs may monosynaptically convey directional information to principal neurons in all layers of MEC through synapses on their extensive dendritic arbors.

Emerging phenomena in neural networks with dynamic synapses and their computational implications

In this paper we review our research on the effect and computational role of dynamical synapses on feed-forward and recurrent neural networks. Among others, we report on the appearance of a new class of dynamical memories which result from the destabilization of learned memory attractors. This has important consequences for dynamic information processing allowing the system to sequentially access the information stored in the memories under changing stimuli. Although storage capacity of stable memories also decreases, our study demonstrated the positive effect of synaptic facilitation to recover maximum storage capacity and to enlarge the capacity of the system for memory recall in noisy conditions. Possibly, the new dynamical behavior can be associated with the voltage transitions between up and down states observed in cortical areas in the brain. We investigated the conditions for which the permanence times in the up state are power-law distributed, which is a sign for criticality, and concluded that the experimentally observed large variability of permanence times could be explained as the result of noisy dynamic synapses with large recovery times. Finally, we report how short-term synaptic processes can transmit weak signals throughout more than one frequency range in noisy neural networks, displaying a kind of stochastic multi-resonance. This effect is due to competition between activity-dependent synaptic fluctuations (due to dynamic synapses) and the existence of neuron firing threshold which adapts to the incoming mean synaptic input.

A saliency-based bottom-up visual attention model for dynamic scenes analysis

This work proposes a model of visual bottom-up attention for dynamic scene analysis. Our work adds motion saliency calculations to a neural network model with realistic temporal dynamics [(e.g., building motion salience on top of De Brecht and Saiki Neural Networks 19:1467-1474, (2006)]. The resulting network elicits strong transient responses to moving objects and reaches stability within a biologically plausible time interval. The responses are statistically different comparing between earlier and later motion neural activity; and between moving and non-moving objects. We demonstrate the network on a number of synthetic and real dynamical movie examples. We show that the model captures the motion saliency asymmetry phenomenon. In addition, the motion salience computation enables sudden-onset moving objects that are less salient in the static scene to rise above others. Finally, we include strong consideration for the neural latencies, the Lyapunov stability, and the neural properties being reproduced by the model.

The application of nonlinear Dynamic Causal Modelling for fMRI in subjects at high genetic risk of schizophrenia

Nonlinear Dynamic Causal Modelling (DCM) for fMRI provides computational modelling of gating mechanisms at the neuronal population level. It allows for estimations of connection strengths with nonlinear modulation within task-dependent networks. This paper presents an application of nonlinear DCM in subjects at high familial risk of schizophrenia performing the Hayling Sentence Completion Task (HSCT). We analysed scans of 19 healthy controls and 46 subjects at high familial risk of schizophrenia, which included 26 high risk subjects without psychotic symptoms and 20 subjects with psychotic symptoms. The activity-dependent network consists of the intra parietal cortex (IPS), inferior frontal gyrus (IFG), middle temporal gyrus (MTG), anterior cingulate cortex (ACC) and the mediodorsal (MD) thalamus. The connections between the MD thalamus and the IFG were gated by the MD thalamus. We used DCM to investigate altered connection strength of these connections. Bayesian Model Selection (BMS) at the group and family level was used to compare the optimal bilinear and nonlinear models. Bayesian Model Averaging (BMA) was used to assess the connection strengths with the gating from the MD thalamus and the IFG. The nonlinear models provided the better explanation of the data. Furthermore, the BMA analysis showed significantly lower connection strength of the thalamocortical connection with nonlinear modulation from the MD thalamus in high risk subjects with psychotic symptoms and those who subsequently developed schizophrenia. These findings demonstrate that nonlinear DCM provides a method to investigate altered connectivity at the level of neural circuits. The reduced connection strength with thalamic gating may be a neurobiomarker implicated in the development of psychotic symptoms. This study suggests that nonlinear DCM could lead to new insights into functional and effective dysconnection at the network level in subjects at high familial risk.

Pavlov's Dog Associative Learning Demonstrated on Synaptic-Like Organic Transistors

In this letter, we present an original demonstration of an associative learning neural network inspired by the famous Pavlov's dogs experiment. A single nanoparticle organic memory field effect transistor (NOMFET) is used to implement each synapse. We show how the physical properties of this dynamic memristive device can be used to perform low-power write operations for the learning and implement short-term association using temporal coding and spike-timing-dependent plasticity–based learning. An electronic circuit was built to validate the proposed learning scheme with packaged devices, with good reproducibility despite the complex synaptic-like dynamic of the NOMFET in pulse regime.

The response clamp: functional characterization of neural systems using closed-loop control

The voltage clamp method, pioneered by Hodgkin, Huxley, and Katz, laid the foundations to neurophysiological research. Its core rationale is the use of closed-loop control as a tool for system characterization. A recently introduced method, the response clamp, extends the voltage clamp rationale to the functional, phenomenological level. The method consists of on-line estimation of a response variable of interest (e.g., the probability of response or its latency) and a simple feedback control mechanism designed to tightly converge this variable toward a desired trajectory. In the present contribution I offer a perspective on this novel method and its applications in the broader context of system identification and characterization. First, I demonstrate how internal state variables are exposed using the method, and how the use of several controllers may allow for a detailed, multi-variable characterization of the system. Second, I discuss three different categories of applications of the method: (1) exploration of intrinsically generated dynamics, (2) exploration of extrinsically generated dynamics, and (3) generation of input–output trajectories. The relation of these categories to similar uses in the voltage clamp and other techniques is also discussed. Finally, I discuss the method's limitations, as well as its possible synthesis with existing complementary approaches.

The impact of short term synaptic depression and stochastic vesicle dynamics on neuronal variability

Neuronal variability plays a central role in neural coding and impacts the dynamics of neuronal networks. Unreliability of synaptic transmission is a major source of neural variability: synaptic neurotransmitter vesicles are released probabilistically in response to presynaptic action potentials and are recovered stochastically in time. The dynamics of this process of vesicle release and recovery interacts with variability in the arrival times of presynaptic spikes to shape the variability of the postsynaptic response. We use continuous time Markov chain methods to analyze a model of short term synaptic depression with stochastic vesicle dynamics coupled with three different models of presynaptic spiking: one model in which the timing of presynaptic action potentials are modeled as a Poisson process, one in which action potentials occur more regularly than a Poisson process (sub-Poisson) and one in which action potentials occur more irregularly (super-Poisson). We use this analysis to investigate how variability in a presynaptic spike train is transformed by short term depression and stochastic vesicle dynamics to determine the variability of the postsynaptic response. We find that sub-Poisson presynaptic spiking increases the average rate at which vesicles are released, that the number of vesicles released over a time window is more variable for smaller time windows than larger time windows and that fast presynaptic spiking gives rise to Poisson-like variability of the postsynaptic response even when presynaptic spike times are non-Poisson. Our results complement and extend previously reported theoretical results and provide possible explanations for some trends observed in recorded data.

Neuromodulatory control of neocortical microcircuits with activity-dependent short-term synaptic depression

A biophysical model of a neocortical microcircuit system is formulated and employed in studies of neuromodulatory control of dynamics and function. The model is based on recent observations of reciprocal connections between pyramidal cells and inhibitory interneurons and incorporates a new type of activity-dependent short-term depression of synaptic couplings recently observed. The model neurons are of a low-dimensional type also accounting for neuronal adaptation, i.e. the coupling between neuronal activity and excitability, which can be regulated by various neuromodulators in the brain. The results obtained demonstrate a capacity for neuromodulatory control of dynamical mode linked to functional mode. The functional aspects considered refer to the observed resolution of multiple objects in working memory as well as the binding of different features for the perception of an object. The effects of neuromodulators displayed by the model are in accordance with many observations on neuromodulatory influence on cognitive functions and brain disorders.

Functional and effective connectivity: a review

Over the past 20 years, neuroimaging has become a predominant technique in systems neuroscience. One might envisage that over the next 20 years the neuroimaging of distributed processing and connectivity will play a major role in disclosing the brain's functional architecture and operational principles. The inception of this journal has been foreshadowed by an ever-increasing number of publications on functional connectivity, causal modeling, connectomics, and multivariate analyses of distributed patterns of brain responses. I accepted the invitation to write this review with great pleasure and hope to celebrate and critique the achievements to date, while addressing the challenges ahead.

Modeling the generation of output by the cerebellar nuclei

Functional aspects of network integration in the cerebellar cortex have been studied experimentally and modeled in much detail ever since the early work by theoreticians such as Marr, Albus and Braitenberg more than 40 years ago. In contrast, much less is known about cerebellar processing at the output stage, namely in the cerebellar nuclei (CN). Here, input from Purkinje cells converges to control CN neuron spiking via GABAergic inhibition, before the output from the CN reaches cerebellar targets such as the brainstem and the motor thalamus. In this article we review modeling studies that address how the CN may integrate cerebellar cortical inputs, and what kind of signals may be transmitted. Specific hypotheses in the literature contrast rate coding and temporal coding of information in the spiking output from the CN. One popular hypothesis states that post-inhibitory rebound spiking may be an important mechanism by which Purkinje cell inhibition is turned into CN output spiking, but this hypothesis remains controversial. Rate coding clearly does take place, but in what way it may be augmented by temporal codes remains to be more clearly established. Several candidate mechanisms distinct from rebound spiking are discussed, such as the significance of spike time correlations between Purkinje cell pools to determine CN spike timing, irregularity of Purkinje cell spiking as a determinant of CN firing rate, and shared brief pauses between Purkinje cell pools that may trigger individual CN spikes precisely.

Increase in sodium conductance decreases firing rate and gain in model neurons

We studied the effects of increased sodium conductance on firing rate and gain in two populations of conductance-based, single-compartment model neurons. The first population consisted of 1000 model neurons with differing values of seven voltage-dependent conductances. In many of these models, increasing the sodium conductance threefold unexpectedly reduced the firing rate and divisively scaled the gain at high input current. In the second population, consisting of 1000 simplified model neurons, we found that enhanced sodium conductance changed the frequency-current (FI) curve in two computationally distinct ways, depending on the firing rate. In these models, increased sodium conductance produced a subtractive shift in the FI curve at low firing rates because the additional sodium conductance allowed the neuron to respond more strongly to equivalent input current. In contrast, at high input current, the increase in sodium conductance resulted in a divisive change in the gain because the increased conductance produced a proportionally larger activation of the delayed rectifier potassium conductance. The control and sodium-enhanced FI curves intersect at a point that delimits two regions in which the same biophysical manipulation produces two fundamentally different changes to the model neuron's computational properties. This suggests a potentially difficult problem for homeostatic regulation of intrinsic excitability.

Altered short-term plasticity in the prefrontal cortex after early life seizures

Seizures during development are a relatively common occurrence and are often associated with poor cognitive outcomes. Recent studies show that early life seizures alter the function of various brain structures and have long-term consequences on seizure susceptibility and behavioral regulation. While many neocortical functions could be disrupted by epileptic seizures, we have concentrated on studying the prefrontal cortex (PFC) as disturbance of PFC functions is involved in numerous co-morbid disorders associated with epilepsy. In the present work we report an alteration of short-term plasticity in the PFC in rats that have experienced early life seizures. The most robust alteration occurs in the layer II/III to layer V network of neurons. However short-term plasticity of layer V to layer V network was also affected, indicating that the PFC function is broadly influenced by early life seizures. These data strongly suggest that repetitive seizures early in development cause substantial alteration in PFC function, which may be an important component underlying cognitive deficits in individuals with a history of seizures during development.

Effects of time-dependent stimuli in a competitive neural network model of perceptual rivalry

We analyze a competitive neural network model of perceptual rivalry that receives time-varying inputs. Time-dependence of inputs can be discrete or smooth. Spike frequency adaptation provides negative feedback that generates network oscillations when inputs are constant in time. Oscillations that resemble perceptual rivalry involve only one population being “ON” at a time, which represents the dominance of a single percept at a time. As shown in Laing and Chow (J. Comput. Neurosci. 12(1):39–53, 2002), for sufficiently high contrast, one can derive relationships between dominance times and contrast that agree with Levelt’s propositions (Levelt in On binocular rivalry, 1965). Time-dependent stimuli give rise to novel network oscillations where both, one, or neither populations are “ON” at any given time. When a single population receives an interrupted stimulus, the fundamental mode of behavior we find is phase-locking, where the temporally driven population locks its state to the stimulus. Other behaviors are analyzed as bifurcations from this forced oscillation, using fast/slow analysis that exploits the slow timescale of adaptation. When both populations receive time-varying input, we find mixtures of fusion and sole population dominance, and we partition parameter space into particular oscillation types. Finally, when a single population’s input contrast is smoothly varied in time, 1:n mode-locked states arise through period-adding bifurcations beyond phase-locking. Our results provide several testable predictions for future psychophysical experiments on perceptual rivalry.

Nonconserved Ca(2+)/calmodulin binding sites in Munc13s differentially control synaptic short-term plasticity

Munc13s are presynaptic proteins that mediate synaptic vesicle priming and thereby control the size of the readily releasable pool of vesicles. During high synaptic activity, Munc13-1 and its closely related homolog, ubMunc13-2, bind Ca(2+)/calmodulin, resulting in enhanced priming activity and in changes of short-term synaptic plasticity characteristics. Here, we studied whether bMunc13-2 and Munc13-3, two remote isoforms of Munc13-1 with a neuronal subtype-specific expression pattern, mediate synaptic vesicle priming and regulate short-term synaptic plasticity in a Ca(2+)/calmodulin-dependent manner. We identified a single functional Ca(2+)/calmodulin binding site in these isoforms and provide structural evidence that all Munc13s employ a common mode of interaction with calmodulin despite the lack of sequence homology between their Ca(2+)/calmodulin binding sites. Electrophysiological analysis showed that, during high-frequency activity, Ca(2+)/calmodulin binding positively regulates the priming activity of bMunc13-2 and Munc13-3, resulting in an increase in the size of the readily releasable pool of vesicles and subsequently in strong short-term synaptic enhancement of neurotransmission. We conclude that Ca(2+)/calmodulin-dependent regulation of priming activity is structurally and functionally conserved in all Munc13 proteins, and that the composition of Munc13 isoforms in a neuron differentially controls its short-term synaptic plasticity characteristics.

Carbon nanotubes: artificial nanomaterials to engineer single neurons and neuronal networks

In the past decade, nanotechnology applications to the nervous system have often involved the study and the use of novel nanomaterials to improve the diagnosis and therapy of neurological diseases. In the field of nanomedicine, carbon nanotubes are evaluated as promising materials for diverse therapeutic and diagnostic applications. Besides, carbon nanotubes are increasingly employed in basic neuroscience approaches, and they have been used in the design of neuronal interfaces or in that of scaffolds promoting neuronal growth in vitro. Ultimately, carbon nanotubes are thought to hold the potential for the development of innovative neurological implants. In this framework, it is particularly relevant to document the impact of interfacing such materials with nerve cells. Carbon nanotubes were shown, when modified with biologically active compounds or functionalized in order to alter their charge, to affect neurite outgrowth and branching. Notably, purified carbon nanotubes used as scaffolds can promote the formation of nanotube-neuron hybrid networks, able per se to affect neuron integrative abilities, network connectivity, and synaptic plasticity. We focus this review on our work over several years directed to investigate the ability of carbon nanotube platforms in providing a new tool for nongenetic manipulations of neuronal performance and network signaling.

STD-dependent and independent encoding of input irregularity as spike rate in a computational model of a cerebellar nucleus neuron

Neurons in the cerebellar nuclei (CN) receive inhibitory inputs from Purkinje cells in the cerebellar cortex and provide the major output from the cerebellum, but their computational function is not well understood. It has recently been shown that the spike activity of Purkinje cells is more regular than previously assumed and that this regularity can affect motor behaviour. We use a conductance-based model of a CN neuron to study the effect of the regularity of Purkinje cell spiking on CN neuron activity. We find that increasing the irregularity of Purkinje cell activity accelerates the CN neuron spike rate and that the mechanism of this recoding of input irregularity as output spike rate depends on the number of Purkinje cells converging onto a CN neuron. For high convergence ratios, the irregularity induced spike rate acceleration depends on short-term depression (STD) at the Purkinje cell synapses. At low convergence ratios, or for synchronised Purkinje cell input, the firing rate increase is independent of STD. The transformation of input irregularity into output spike rate occurs in response to artificial input spike trains as well as to spike trains recorded from Purkinje cells in tottering mice, which show highly irregular spiking patterns. Our results suggest that STD may contribute to the accelerated CN spike rate in tottering mice and they raise the possibility that the deficits in motor control in these mutants partly result as a pathological consequence of this natural form of plasticity.

Emergence of coordinated plasticity in the cochlear nucleus and cerebellum

Synapses formed by one cell type onto another cell type tend to show characteristic short-term plasticity, which varies from facilitating to depressing depending on the particular system. Within a population of synapses, plasticity can also be variable, and it is unknown how this plasticity is determined on a cell-by-cell level. We have investigated this in the mouse cochlear nucleus, where auditory nerve (AN) fibers contact bushy cells (BCs) at synapses called "endbulbs of Held." Synapses formed by different AN fibers onto one BC had plasticity that was more similar than would be expected at random. Experiments using MK-801 indicated that this resulted in part from similarity in the presynaptic probability of release. The similarity was not present in immature synapses but emerged after the onset of hearing. In addition, the phenomenon occurred at excitatory synapses in the cerebellum. This indicates that postsynaptic cells coordinate the plasticity of their inputs, which suggests that plasticity is of fundamental importance to synaptic function.

Synaptic vesicle pools and dynamics

Synaptic vesicles release neurotransmitter at chemical synapses, thus initiating the flow of information in neural networks. To achieve this, vesicles undergo a dynamic cycle of fusion and retrieval to maintain the structural and functional integrity of the presynaptic terminals in which they reside. Moreover, compelling evidence indicates these vesicles differ in their availability for release and mobilization in response to stimuli, prompting classification into at least three different functional pools. Ongoing studies of the molecular and cellular bases for this heterogeneity attempt to link structure to physiology and clarify how regulation of vesicle pools influences synaptic strength and presynaptic plasticity. We discuss prevailing perspectives on vesicle pools, the role they play in shaping synaptic transmission, and the open questions that challenge current understanding.

Short-term presynaptic plasticity

Different types of synapses are specialized to interpret spike trains in their own way by virtue of the complement of short-term synaptic plasticity mechanisms they possess. Numerous types of short-term, use-dependent synaptic plasticity regulate neurotransmitter release. Short-term depression is prominent after a single conditioning stimulus and recovers in seconds. Sustained presynaptic activation can result in more profound depression that recovers more slowly. An enhancement of release known as facilitation is prominent after single conditioning stimuli and lasts for hundreds of milliseconds. Finally, tetanic activation can enhance synaptic strength for tens of seconds to minutes through processes known as augmentation and posttetantic potentiation. Progress in clarifying the properties, mechanisms, and functional roles of these forms of short-term plasticity is reviewed here.

Developmental shift of short-term synaptic plasticity in cortical organotypic slices

Although short-term synaptic plasticity (STP) is ubiquitous in neocortical synapses its functional role in neural computations is not well understood. Critical to elucidating the function of STP will be to understand how STP itself changes with development and experience. Previous studies have reported developmental changes in STP using acute slices. It is not clear, however, to what extent the changes in STP are a function of local ontogenetic programs or the result of the many different sensory and experience-dependent changes that accompany development in vivo. To address this question we examined the in vitro development of STP in organotypic slices cultured for up to 4 weeks. Paired recordings were performed in L5 pyramidal neurons at different stages of in vitro development. We observed a shift in STP in the form of a decrease in the paired-pulse ratio (PPR) (less depression) from the second to fourth week in vitro. This shift in STP was not accompanied by a change in initial excitatory postsynaptic potential (EPSP) amplitude. Fitting STP to a quantitative model indicated that the developmental shift is consistent with presynaptic changes. Importantly, despite the change in the PPR we did not observe changes in the time constant governing STP. Since these experiments were conducted in vitro our results indicate that the shift in STP does not depend on in vivo sensory experience. Although sensory experience may shape STP, we suggest that developmental shifts in STP are at least in part ontogenetically determined.

Activity-dependent energy budget for neocortical signaling: effect of short-term synaptic plasticity on the energy expended by spiking and synaptic activity

The available estimate of the energy expended for signaling in rat neocortex is refined to examine the separate contribution of spiking and synaptic activity as a function of average neuronal firing rate. By taking into account a phenomenological model of short-term synaptic plasticity, we show that the transition from low to high cortical activity is accompanied by a substantial increase in relative energy consumed by action potentials vs. synaptic potentials. This consideration might be important for a deeper understanding of how information is represented in the cortex and which metabolic pathways are upregulated to sustain cortical activity.

Interactions between behaviorally relevant rhythms and synaptic plasticity alter coding in the piriform cortex

Understanding how neural and behavioral timescales interact to influence cortical activity and stimulus coding is an important issue in sensory neuroscience. In air-breathing animals, voluntary changes in respiratory frequency alter the temporal patterning olfactory input. In the olfactory bulb, these behavioral timescales are reflected in the temporal properties of mitral/tufted (M/T) cell spike trains. As the odor information contained in these spike trains is relayed from the bulb to the cortex, interactions between presynaptic spike timing and short-term synaptic plasticity dictate how stimulus features are represented in cortical spike trains. Here, we demonstrate how the timescales associated with respiratory frequency, spike timing, and short-term synaptic plasticity interact to shape cortical responses. Specifically, we quantified the timescales of short-term synaptic facilitation and depression at excitatory synapses between bulbar M/T cells and cortical neurons in slices of mouse olfactory cortex. We then used these results to generate simulated M/T population synaptic currents that were injected into real cortical neurons. M/T population inputs were modulated at frequencies consistent with passive respiration or active sniffing. We show how the differential recruitment of short-term plasticity at breathing versus sniffing frequencies alters cortical spike responses. For inputs at sniffing frequencies, cortical neurons linearly encoded increases in presynaptic firing rates with increased phase-locked, firing rates. In contrast, at passive breathing frequencies, cortical responses saturated with changes in presynaptic rate. Our results suggest that changes in respiratory behavior can gate the transfer of stimulus information between the olfactory bulb and cortex.

Short term synaptic depression imposes a frequency dependent filter on synaptic information transfer

Depletion of synaptic neurotransmitter vesicles induces a form of short term depression in synapses throughout the nervous system. This plasticity affects how synapses filter presynaptic spike trains. The filtering properties of short term depression are often studied using a deterministic synapse model that predicts the mean synaptic response to a presynaptic spike train, but ignores variability introduced by the probabilistic nature of vesicle release and stochasticity in synaptic recovery time. We show that this additional variability has important consequences for the synaptic filtering of presynaptic information. In particular, a synapse model with stochastic vesicle dynamics suppresses information encoded at lower frequencies more than information encoded at higher frequencies, while a model that ignores this stochasticity transfers information encoded at any frequency equally well. This distinction between the two models persists even when large numbers of synaptic contacts are considered. Our study provides strong evidence that the stochastic nature neurotransmitter vesicle dynamics must be considered when analyzing the information flow across a synapse.

Neuromodulatory changes in short-term synaptic dynamics may be mediated by two distinct mechanisms of presynaptic calcium entry

Although synaptic output is known to be modulated by changes in presynaptic calcium channels, additional pathways for calcium entry into the presynaptic terminal, such as non-selective channels, could contribute to modulation of short term synaptic dynamics. We address this issue using computational modeling. The neuropeptide proctolin modulates the inhibitory synapse from the lateral pyloric (LP) to the pyloric dilator (PD) neuron, two slow-wave bursting neurons in the pyloric network of the crab Cancer borealis. Proctolin enhances the strength of this synapse and also changes its dynamics. Whereas in control saline the synapse shows depression independent of the amplitude of the presynaptic LP signal, in proctolin, with high-amplitude presynaptic LP stimulation the synapse remains depressing while low-amplitude stimulation causes facilitation. We use simple calcium-dependent release models to explore two alternative mechanisms underlying these modulatory effects. In the first model, proctolin directly targets calcium channels by changing their activation kinetics which results in gradual accumulation of calcium with low-amplitude presynaptic stimulation, leading to facilitation. The second model uses the fact that proctolin is known to activate a non-specific cation current I ( MI ). In this model, we assume that the MI channels have some permeability to calcium, modeled to be a result of slow conformation change after binding calcium. This generates a gradual increase in calcium influx into the presynaptic terminals through the modulatory channel similar to that described in the first model. Each of these models can explain the modulation of the synapse by proctolin but with different consequences for network activity.

Learned integration of visual, vestibular, and motor cues in multiple brain regions computes head direction during visually guided navigation

Effective navigation depends upon reliable estimates of head direction (HD). Visual, vestibular, and outflow motor signals combine for this purpose in a brain system that includes dorsal tegmental nucleus, lateral mammillary nuclei, anterior dorsal thalamic nucleus, and the postsubiculum. Learning is needed to combine such different cues to provide reliable estimates of HD. A neural model is developed to explain how these three types of signals combine adaptively within the above brain regions to generate a consistent and reliable HD estimate, in both light and darkness, which explains the following experimental facts. Each HD cell is tuned to a preferred head direction. The cell's firing rate is maximal at the preferred direction and decreases as the head turns from the preferred direction. The HD estimate is controlled by the vestibular system when visual cues are not available. A well-established visual cue anchors the cell's preferred direction when the cue is in the animal's field of view. Distal visual cues are more effective than proximal cues for anchoring the preferred direction. The introduction of novel cues in either a novel or familiar environment can gain control over a cell's preferred direction within minutes. Turning out the lights or removing all familiar cues does not change the cell's firing activity, but it may accumulate a drift in the cell's preferred direction. The anticipated time interval (ATI) of the HD estimate is greater in early processing stages of the HD system than at later stages. The model contributes to an emerging unified neural model of how multiple processing stages in spatial navigation, including postsubiculum head direction cells, entorhinal grid cells, and hippocampal place cells, are calibrated through learning in response to multiple types of signals as an animal navigates in the world.

Optimal heterogeneity for coding in spiking neural networks

The effect of cellular heterogeneity on the coding properties of neural populations is studied analytically and numerically. We find that heterogeneity decreases the threshold for synchronization, and its strength is nonlinearly related to the network mean firing rate. In addition, conditions are shown under which heterogeneity optimizes network information transmission for either temporal or rate coding, with high input frequencies leading to different effects for each coding strategy. The results are shown to be robust for more realistic conditions.

A comparative analysis of integrating visual information in local neuronal ensembles

Spike directivity, a new measure that quantifies the transient charge density dynamics within action potentials provides better results in discriminating different categories of visual object recognition. Specifically, intracranial recordings from medial temporal lobe (MTL) of epileptic patients have been analyzed using firing rate, interspike intervals and spike directivity. A comparative statistical analysis of the same spikes from a local ensemble of four selected neurons shows that electrical patterns in these neurons display higher separability to input images compared to spike timing features. If the observation vector includes data from all four neurons then the comparative analysis shows a highly significant separation between categories for spike directivity (p=0.0023) and does not display separability for interspike interval (p=0.3768) and firing rate (p=0.5492). Since electrical patterns in neuronal spikes provide information regarding different presented objects this result shows that related information is intracellularly processed in neurons and carried out within a millisecond-level time domain of action potential occurrence. This significant statistical outcome obtained from a local ensemble of four neurons suggests that meaningful information can be electrically inferred at the network level to generate a better discrimination of presented images.

Interneuron-mediated inhibition synchronizes neuronal activity during slow oscillation

The signature of slow-wave sleep in the electroencephalogram (EEG) is large-amplitude fluctuation of the field potential, which reflects synchronous alternation of activity and silence across cortical neurons. While initiation of the active cortical states during sleep slow oscillation has been intensively studied, the biological mechanisms which drive the network transition from an active state to silence remain poorly understood. In the current study, using a combination of in vivo electrophysiology and thalamocortical network simulation, we explored the impact of intrinsic and synaptic inhibition on state transition during sleep slow oscillation. We found that in normal physiological conditions, synaptic inhibition controls the duration and the synchrony of active state termination. The decline of interneuron-mediated inhibition led to asynchronous downward transition across the cortical network and broke the regular slow oscillation pattern. Furthermore, in both in vivo experiment and computational modelling, we revealed that when the level of synaptic inhibition was reduced significantly, it led to a recovery of synchronized oscillations in the form of seizure-like bursting activity. In this condition, the fast active state termination was mediated by intrinsic hyperpolarizing conductances. Our study highlights the significance of both intrinsic and synaptic inhibition in manipulating sleep slow rhythms.

A cortical attractor network with Martinotti cells driven by facilitating synapses

The population of pyramidal cells significantly outnumbers the inhibitory interneurons in the neocortex, while at the same time the diversity of interneuron types is much more pronounced. One acknowledged key role of inhibition is to control the rate and patterning of pyramidal cell firing via negative feedback, but most likely the diversity of inhibitory pathways is matched by a corresponding diversity of functional roles. An important distinguishing feature of cortical interneurons is the variability of the short-term plasticity properties of synapses received from pyramidal cells. The Martinotti cell type has recently come under scrutiny due to the distinctly facilitating nature of the synapses they receive from pyramidal cells. This distinguishes these neurons from basket cells and other inhibitory interneurons typically targeted by depressing synapses. A key aspect of the work reported here has been to pinpoint the role of this variability. We first set out to reproduce quantitatively based on in vitro data the di-synaptic inhibitory microcircuit connecting two pyramidal cells via one or a few Martinotti cells. In a second step, we embedded this microcircuit in a previously developed attractor memory network model of neocortical layers 2/3. This model network demonstrated that basket cells with their characteristic depressing synapses are the first to discharge when the network enters an attractor state and that Martinotti cells respond with a delay, thereby shifting the excitation-inhibition balance and acting to terminate the attractor state. A parameter sensitivity analysis suggested that Martinotti cells might, in fact, play a dominant role in setting the attractor dwell time and thus cortical speed of processing, with cellular adaptation and synaptic depression having a less prominent role than previously thought.

Tuned normalization explains the size of attention modulations

The effect of attention on firing rates varies considerably within a single cortical area. The firing rate of some neurons is greatly modulated by attention while others are hardly affected. The reason for this variability across neurons is unknown. We found that the variability in attention modulation across neurons in area MT of macaques can be well explained by variability in the strength of tuned normalization across neurons. The presence of tuned normalization also explains a striking asymmetry in attention effects within neurons: when two stimuli are in a neuron's receptive field, directing attention to the preferred stimulus modulates firing rates more than directing attention to the nonpreferred stimulus. These findings show that much of the neuron-to-neuron variability in modulation of responses by attention depends on variability in the way the neurons process multiple stimuli, rather than differences in the influence of top-down signals related to attention.

Thalamic activation modulates the responses of neurons in rat primary auditory cortex: an in vivo intracellular recording study

Auditory cortical plasticity can be induced through various approaches. The medial geniculate body (MGB) of the auditory thalamus gates the ascending auditory inputs to the cortex. The thalamocortical system has been proposed to play a critical role in the responses of the auditory cortex (AC). In the present study, we investigated the cellular mechanism of the cortical activity, adopting an in vivo intracellular recording technique, recording from the primary auditory cortex (AI) while presenting an acoustic stimulus to the rat and electrically stimulating its MGB. We found that low-frequency stimuli enhanced the amplitudes of sound-evoked excitatory postsynaptic potentials (EPSPs) in AI neurons, whereas high-frequency stimuli depressed these auditory responses. The degree of this modulation depended on the intensities of the train stimuli as well as the intervals between the electrical stimulations and their paired sound stimulations. These findings may have implications regarding the basic mechanisms of MGB activation of auditory cortical plasticity and cortical signal processing.

Electrophysiological classes of layer 2/3 pyramidal cells in monkey prefrontal cortex

The activity of supragranular pyramidal neurons in the dorsolateral prefrontal cortex (DLPFC) neurons is hypothesized to be a key contributor to the cellular basis of working memory in primates. Therefore, the intrinsic membrane properties, a crucial determinant of a neuron's functional properties, are important for the role of DLPFC pyramidal neurons in working memory. The present study aimed to investigate the biophysical properties of pyramidal cells in layer 2/3 of monkey DLPFC to create an unbiased electrophysiological classification of these cells. Whole cell voltage recordings in the slice preparation were performed in 77 pyramidal cells, and 24 electrophysiological measures of their passive and active intrinsic membrane properties were analyzed. Based on the results of cluster analysis of 16 independent electrophysiological variables, 4 distinct electrophysiological classes of monkey pyramidal cells were determined. Two classes contain regular-spiking neurons with low and high excitability and constitute 52% of the pyramidal cells sampled. These subclasses of regular-spiking neurons mostly differ in their input resistance, minimum current that evoked firing, and current-to-frequency transduction properties. A third class of pyramidal cells includes low-threshold spiking cells (17%), which fire a burst of three-five spikes followed by regular firing at all suprathreshold current intensities. The last class consists of cells with an intermediate firing pattern (31%). These cells have two modes of firing response, regular spiking and bursting discharge, depending on the strength of stimulation and resting membrane potential. Our results show that diversity in the functional properties of DLPFC pyramidal cells may contribute to heterogeneous modes of information processing during working memory and other cognitive operations that engage the activity of cortical circuits in the superficial layers of the DLPFC.

The influences of dark rearing on the transmission characteristics of layer II/III pyramidal cells during the critical period

The characteristics of synaptic plasticity on layer II/III pyramidal cells in different ages of rats have been studied extensively, and dark rearing is one of the important impact factors. To systematically analyze the influence of dark rearing on synaptic plasticity during the critical period of visual development, we studied the characteristics of short-term and long-term synaptic plasticities of layer II/III pyramidal cells of rats in three rearing conditions during P14 to P37. The paired-pulse ratio (PPR) of inhibitory postsynaptic currents (IPSCs) of layer II/III pyramidal cells was effected by both ages and rearing conditions, but the PPR of excitatory postsynaptic currents (EPSCs) did not change obviously. Moreover, long-term synaptic plasticity of rats in the dark rearing condition did not significantly change with age, while it was elevated during P16 and P21 for rats in the normal rearing condition. These results suggest that visual experience can affect the characteristics of short-term and long-term synaptic plasticities. The IPSC/EPSC ratio increased gradually with aging for NR rats, but the ratio slightly decreased for DR rats, which indicates the relative increase of inhibitory components during the critical period of visual development. The characteristics during P35 and P37 of the 30-day dark-reared (30D×N) group had similar trends with the normal-reared rats during P16 and P21, which emphasizes that dark rearing can postpone the timing of the critical period.

Quantifying impacts of short-term plasticity on neuronal information transfer

Short-term changes in efficacy have been postulated to enhance the ability of synapses to transmit information between neurons, and within neuronal networks. Even at the level of connections between single neurons, direct confirmation of this simple conjecture has proven elusive. By combining paired-cell recordings, realistic synaptic modeling, and information theory, we provide evidence that short-term plasticity can not only improve, but also reduce information transfer between neurons. We focus on a concrete example in rat neocortex, but our results may generalize to other systems. When information is contained in the timings of individual spikes, we find that facilitation, depression, and recovery affect information transmission in proportion to their impacts upon the probability of neurotransmitter release. When information is instead conveyed by mean spike rate only, the influences of short-term plasticity critically depend on the range of spike frequencies that the target network can distinguish (its effective dynamic range). Our results suggest that to efficiently transmit information, the brain must match synaptic type, coding strategy, and network connectivity during development and behavior.