Activity-dependent properties of synaptic transmission at two classes of connections made by rat neocortical pyramidal axons in vitro

Inter and intralaminar excitation of parvalbumin interneurons in mouse barrel cortex

Parvalbumin (PV) interneurons are inhibitory fast-spiking cells with essential roles in directing the flow of information through cortical circuits. These neurons set the balance between excitation and inhibition and control rhythmic activity. PV interneurons differ between cortical layers in their morphology, circuitry, and function, but how their electrophysiological properties vary has received little attention. Here we investigate responses of PV interneurons in different layers of primary somatosensory barrel cortex (BC) to different excitatory inputs. With the genetically-encoded hybrid voltage sensor, hVOS, we recorded voltage changes in many L2/3 and L4 PV interneurons simultaneously, with stimulation applied to either L2/3 or L4. A semi-automated procedure was developed to identify small regions of interest corresponding to single responsive PV interneurons. Amplitude, half-width, and rise-time were greater for PV interneurons residing in L2/3 compared to L4. Stimulation in L2/3 elicited responses in both L2/3 and L4 with longer latency compared to stimulation in L4. These differences in latency between layers could influence their windows for temporal integration. Thus, PV interneurons in different cortical layers of BC respond in a layer specific and input specific manner, and these differences have potential roles in cortical computations.

Short-latency prepulse inhibition of the trigeminal blink reflex

Prepulse inhibition (PPI) is a well-established phenomenon wherein a weak sensory stimulus attenuates the startle reflex triggered by a subsequent strong stimulus. Within the circuit, variations in target responses observed for PPI paradigms represent prepulse-induced excitability changes. However, little is known about the mechanism of PPI. Here, we focused on short-latency PPI of the trigeminal blink reflex R1 signal with an oligosynaptic reflex arc through the principal sensory trigeminal nucleus and the facial nucleus. As the facial nucleus is facilitatory to any input, R1 PPI is the phenomenon in the former nucleus. Considering that GABAergic modulation may be involved in PPI, this study investigated whether the PPI mechanism includes GABA-A equivalent inhibition, which peaks at approximately 30 ms in humans. In 12 healthy volunteers, the reflex was elicited by electrical stimulation of the supraorbital nerve, and recorded at the ipsilateral lower eyelid by accelerometer. Stimulus intensity was 1.5 times the R1 threshold for test stimulus and 0.9 times for the prepulse. The prepulse-test interval (PTI) was 5-150 ms. Results showed significant inhibition at 40-and 80-150-ms PTIs but not at 20-, 30-, 50-, 60-, and 70-ms PTIs, yielding two distinct inhibitions of different time scales. This corresponds well to the early and late components of inhibitory post synaptic potentials by GABA-A and GABA-B receptor activation. Thus, the data support the contribution of inhibitory post synaptic potentials elicited by the prepulse to the observed PPI. As inhibitory function-related diseases may impair the different inhibition components to varying degrees, methods deconvoluting each inhibitory component contribution are of clinical importance.

Inter and Intralaminar Excitation of Parvalbumin Interneurons in Mouse Barrel Cortex

Parvalbumin (PV) interneurons are inhibitory fast-spiking cells with essential roles in directing the flow of information through cortical circuits. These neurons set the balance between excitation and inhibition, control rhythmic activity, and have been linked to disorders including autism spectrum and schizophrenia. PV interneurons differ between cortical layers in their morphology, circuitry, and function, but how their electrophysiological properties vary has received little attention. Here we investigate responses of PV interneurons in different layers of primary somatosensory barrel cortex (BC) to different excitatory inputs. With the genetically-encoded hybrid voltage sensor, hVOS, we recorded voltage changes simultaneously in many L2/3 and L4 PV interneurons to stimulation in either L2/3 or L4. Decay-times were consistent across L2/3 and L4. Amplitude, half-width, and rise-time were greater for PV interneurons residing in L2/3 compared to L4. Stimulation in L2/3 elicited responses in both L2/3 and L4 with longer latency compared to stimulation in L4. These differences in latency between layers could influence their windows for temporal integration. Thus PV interneurons in different cortical layers of BC show differences in response properties with potential roles in cortical computations.

Mechanisms of Short- and Long-Latency Sensory Suppression: Magnetoencephalography Study

Prepulse inhibition (PPI) is sensory suppression whose mechanism (i.e., whether PPI originates from specific inhibitory mechanisms) remains unclear. In this study, we applied the combination of short-latency PPI and long-latency paired pulse suppression in 17 healthy subjects using magnetoencephalography to investigate the mechanisms of sensory suppression. Repeats of a 25-ms pure tone without a blank at 800 Hz and 70 dB were used for a total duration of 1600 ms. To elicit change-related cortical responses, the sound pressure of two consecutive tones in this series at 1300 ms was increased to 80 dB (Test). For the conditioning stimuli, the sound pressure was increased to 73 dB at 1250 ms (Pre 1) and 80 dB at 700 ms (Pre 2). Six stimuli were randomly presented as follows: (1) Test alone, (2) Pre 1 alone, (3) Pre 1 + Test, (4) Pre 2 + Test, (5) Pre 2 + Pre 1, and (6) Pre 2 + Pre 1 + Test. The inhibitory effects of the conditioning stimuli were evaluated using N100m/P200m components. The results showed that both Pre 1 and Pre 2 significantly suppressed the Test response. Moreover, the inhibitory effects of Pre 1 and Pre 2 were additive. However, when both prepulses were present, Pre 2 significantly suppressed the Pre 1 response, suggesting that the Pre 1 response amplitude was not a determining factor for the degree of suppression. These results suggested that the suppression originated from a specific inhibitory circuit independent of the excitatory pathway.

Regulation of Presynaptic Release Machinery by Cell Adhesion Molecules

The synapse is a highly specialized asymmetric structure that transmits and stores information in the brain. The size of pre- and postsynaptic structures and function is well coordinated at the individual synapse level. For example, large postsynaptic dendritic spines have a larger postsynaptic density with higher α-amino-3-hydroxy-5-methyl-4-isoxazole propionic acid receptor (AMPAR) number on their surface, while juxtaposing presynaptic terminals have a larger active zone and higher release probability. This indicates that pre- and postsynaptic domains bidirectionally communicate to coordinate assembly of specific molecules on both sides of the synaptic cleft. Cell adhesion molecules (CAMs) that localize at synapses form transsynaptic protein interactions across the synaptic cleft and play important roles in synapse formation and regulation. The extracellular domain of CAMs is essential for specific synapse formation and function. In contrast, the intracellular domain is necessary for binding with synaptic molecules and signal transduction. Therefore, CAMs play an essential role on synapse function and structure. In fact, ample evidence indicates that transsynaptic CAMs instruct and modulate functions at presynaptic sites. This chapter focuses on transsynaptic protein interactions that regulate presynaptic functions emphasizing the role of neuronal CAMs and the intracellular mechanism of their regulation.

Local connectivity and synaptic dynamics in mouse and human neocortex

We present a unique, extensive, and open synaptic physiology analysis platform and dataset. Through its application, we reveal principles that relate cell type to synaptic properties and intralaminar circuit organization in the mouse and human cortex. The dynamics of excitatory synapses align with the postsynaptic cell subclass, whereas inhibitory synapse dynamics partly align with presynaptic cell subclass but with considerable overlap. Synaptic properties are heterogeneous in most subclass-to-subclass connections. The two main axes of heterogeneity are strength and variability. Cell subclasses divide along the variability axis, whereas the strength axis accounts for substantial heterogeneity within the subclass. In the human cortex, excitatory-to-excitatory synaptic dynamics are distinct from those in the mouse cortex and vary with depth across layers 2 and 3.

Deep-layer motif method for estimating information flow between EEG signals

Accurate identification for the information flow between epileptic seizure signals is the key to construct the directional epileptic brain network which can be used to localize epileptic focus. In this paper, our concern is on how to improve the direction identification of information flow and also investigate how it can be cut off or weakened. In view of this, we propose the deep-layer motif method. Based on the directional index (DI) estimation using permutation conditional mutual information, the effectiveness of the proposed deep-layer motif method is numerically assessed with the coupled mass neural model. Furthermore, we investigate the robustness of this method in considering the interference of autaptic coupling, time delay and short-term plasticity. Results show that compared to the simple 1-layer motif method, the 2nd- and 3rd-layer motif methods have the dominant enhancement effects for the direction identification. In particular, deep-layer motif method possesses good anti-jamming performance and good robustness in calculating DI. In addition, we investigate the effect of deep brain stimulation (DBS) on the information flow. It is found that this deep-layer motif method is still superior to the single-layer motif method in direction identification and is robust to weak DBS. However, the high-frequency strong DBS can effectively decrease the DI suggesting the weakened information flow. These results may give new insights into the seizure detection and control.

Variability in the Munc13-1 content of excitatory release sites

The molecular mechanisms underlying the diversity of cortical glutamatergic synapses are still incompletely understood. Here, we tested the hypothesis that presynaptic active zones (AZs) are constructed from molecularly uniform, independent release sites (RSs), the number of which scales linearly with the AZ size. Paired recordings between hippocampal CA1 pyramidal cells and fast-spiking interneurons in acute slices from adult mice followed by quantal analysis demonstrate large variability in the number of RSs (N) at these connections. High-resolution molecular analysis of functionally characterized synapses reveals variability in the content of one of the key vesicle priming factors – Munc13-1 – in AZs that possess the same N. Replica immunolabeling also shows a threefold variability in the total Munc13-1 content of AZs of identical size and a fourfold variability in the size and density of Munc13-1 clusters within the AZs. Our results provide evidence for quantitative molecular heterogeneity of RSs and support a model in which the AZ is built up from variable numbers of molecularly heterogeneous, but independent RSs.

The Functional Organization of Cortical and Thalamic Inputs onto Five Types of Striatal Neurons Is Determined by Source and Target Cell Identities

To understand striatal function, it is essential to know the functional organization of the numerous inputs targeting the diverse population of striatal neurons. Using optogenetics, we activated terminals from ipsi- or contralateral primary somatosensory cortex (S1) or primary motor cortex (M1), or thalamus while obtaining simultaneous whole-cell recordings from pairs or triplets of striatal medium spiny neurons (MSNs) and adjacent interneurons. Ipsilateral corticostriatal projections provided stronger excitation to fast-spiking interneurons (FSIs) than to MSNs and only sparse and weak excitation to low threshold-spiking interneurons (LTSIs) and cholinergic interneurons (ChINs). Projections from contralateral M1 evoked the strongest responses in LTSIs but none in ChINs, whereas thalamus provided the strongest excitation to ChINs but none to LTSIs. In addition, inputs varied in their glutamate receptor composition and their short-term plasticity. Our data revealed a highly selective organization of excitatory striatal afferents, which is determined by both pre- and postsynaptic neuronal identity.

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Whole-cell recordings are obtained from neighboring striatal neurons of different types

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FSIs receive the strongest inputs from S1, M1, and thalamic PF

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LTSIs are primarily excited by contralateral M1

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ChINs are primarily excited by PF and receive no input from contralateral M1 and S1

Epileptic seizures in a heterogeneous excitatory network with short-term plasticity

Epilepsy involves a diverse group of abnormalities, including molecular and cellular disorders. These abnormalities prove to be associated with the changes in local excitability and synaptic dynamics. Correspondingly, the epileptic processes including onset, propagation and generalized seizure may be related with the alterations of excitability and synapse. In this paper, three regions, epileptogenic zone (EZ), propagation area and normal region, were defined and represented by neuronal population model with heterogeneous excitability, respectively. In order to describe the synaptic behavior that the strength was enhanced and maintained at a high level for a short term under a high frequency spike train, a novel activity-dependent short-term plasticity model was proposed. Bifurcation analysis showed that the presence of hyperexcitability could increase the seizure susceptibility of local area, leading to epileptic discharges first seen in the EZ. Meanwhile, recurrent epileptic activities might result in the transition of synaptic strength from weak state to high level, augmenting synaptic depolarizations in non-epileptic neurons as the experimental findings. Numerical simulation based on a full-connected weighted network could qualitatively demonstrate the epileptic process that the propagation area and normal region were successively recruited by the EZ. Furthermore, cross recurrence plot was used to explore the synchronization between neuronal populations, and the global synchronization index was introduced to measure the global synchronization. Results suggested that the synchronization between the EZ and other region was significantly enhanced with the occurrence of seizure. Interestingly, the desynchronization phenomenon was also observed during seizure initiation and propagation as reported before. Therefore, heterogeneous excitability and short-term plasticity are believed to play an important role in the epileptic process. This study may provide novel insights into the mechanism of epileptogenesis.

Long-Term Activity Dynamics of Single Neurons and Networks

The firing rate of neuronal spiking in vitro and in vivo significantly varies over extended timescales, characterized by long-memory processes and complex statistics, and appears in spontaneous as well as evoked activity upon repeated stimulus presentation. These variations in response features and their statistics, in face of repeated instances of a given physical input, are ubiquitous in all levels of brain-behavior organization. They are expressed in single neuron and network response variability but even appear in variations of subjective percepts or psychophysical choices and have been described as stemming from history-dependent, stochastic, or rate-determined processes.But what are the sources underlying these temporally rich variations in firing rate? Are they determined by interactions of the nervous system as a whole, or do isolated, single neurons or neuronal networks already express these fluctuations independent of higher levels? These questions motivated the application of a method that allows for controlled and specific long-term activation of a single neuron or neuronal network, isolated from higher levels of cortical organization.This chapter highlights the research done in cultured cortical networks to study (1) the inherent non-stationarity of neuronal network activity, (2) single neuron response fluctuations and underlying processes, and (3) the interface layer between network and single cell, the non-stationary efficacy of the ensemble of synapses impinging onto the observed neuron.

Burst Detection Methods

'Bursting', defined as periods of high-frequency firing of a neuron separated by periods of quiescence, has been observed in various neuronal systems, both in vitro and in vivo. It has been associated with a range of neuronal processes, including efficient information transfer and the formation of functional networks during development, and has been shown to be sensitive to genetic and pharmacological manipulations. Accurate detection of periods of bursting activity is thus an important aspect of characterising both spontaneous and evoked neuronal network activity. A wide variety of computational methods have been developed to detect periods of bursting in spike trains recorded from neuronal networks. In this chapter, we review several of the most popular and successful of these methods.

Synapse Type-Dependent Expression of Calcium-Permeable AMPA Receptors

Calcium-permeable (CP) AMPA-type glutamate receptors (AMPARs) are known to mediate synaptic plasticity in several different interneuron (IN) types. Recent evidence suggests that CP-AMPARs are synapse-specifically expressed at excitatory connections onto a subset of IN types in hippocampus and neocortex. For example, CP-AMPARs are found at connections from pyramidal cells (PCs) to basket cells (BCs), but not to Martinotti cells (MCs). This synapse type-specific expression of CP-AMPARs suggests that synaptic dynamics as well as learning rules are differentially implemented in local circuits and has important implications not just in health but also in disease states such as epilepsy.

Layer-Dependent Short-Term Synaptic Plasticity Between Excitatory Neurons in the C2 Barrel Column of Mouse Primary Somatosensory Cortex

Neurons process information through spatiotemporal integration of synaptic input. Synaptic transmission between any given pair of neurons is typically a dynamic process with presynaptic action potentials (APs) evoking depressing or facilitating postsynaptic potentials when presynaptic APs occur within hundreds of milliseconds of each other. In order to understand neocortical function, it is therefore important to investigate such short-term synaptic plasticity at synapses between different types of neocortical neurons. Here, we examine short-term synaptic dynamics between excitatory neurons in different layers of the mouse C2 barrel column through in vitro whole-cell recordings. We find layer-dependent short-term plasticity, with depression being dominant at many synaptic connections. Interestingly, however, presynaptic layer 2 neurons predominantly give rise to facilitating excitatory synaptic output at short interspike intervals of 10 and 30 ms. Previous studies have found prominent burst firing of excitatory neurons in supragranular layers of awake mice. The facilitation we observed in the synaptic output of layer 2 may, therefore, be functionally relevant, possibly serving to enhance the postsynaptic impact of burst firing.

Predicting seizure by modeling synaptic plasticity based on EEG signals - a case study of inherited epilepsy

This paper explores the internal dynamical mechanisms of epileptic seizures through quantitative modeling based on full brain electroencephalogram (EEG) signals. Our goal is to provide seizure prediction and facilitate treatment for epileptic patients. Motivated by an earlier mathematical model with incorporated synaptic plasticity, we studied the nonlinear dynamics of inherited seizures through a differential equation model. First, driven by a set of clinical inherited electroencephalogram data recorded from a patient with diagnosed Glucose Transporter Deficiency, we developed a dynamic seizure model on a system of ordinary differential equations. The model was reduced in complexity after considering and removing redundancy of each EEG channel. Then we verified that the proposed model produces qualitatively relevant behavior which matches the basic experimental observations of inherited seizure, including synchronization index and frequency. Meanwhile, the rationality of the connectivity structure hypothesis in the modeling process was verified. Further, through varying the threshold condition and excitation strength of synaptic plasticity, we elucidated the effect of synaptic plasticity to our seizure model. Results suggest that synaptic plasticity has great effect on the duration of seizure activities, which support the plausibility of therapeutic interventions for seizure control.

Simultaneously Excitatory and Inhibitory Effects of Transcranial Alternating Current Stimulation Revealed Using Selective Pulse-Train Stimulation in the Rat Motor Cortex

Transcranial alternating current stimulation (tACS) uses sinusoidal, subthreshold, electric fields to modulate cortical processing. Cortical processing depends on a fine balance between excitation and inhibition and tACS acts on both excitatory and inhibitory cortical neurons. Given this, it is not clear whether tACS should increase or decrease cortical excitability. We investigated this using transcranial current stimulation of the rat (all males) motor cortex consisting of a continuous subthreshold sine wave with short bursts of suprathreshold pulse-trains inserted at different phases to probe cortical excitability. We found that when a low-rate, long-duration, suprathreshold pulse-train was used, subthreshold cathodal tACS decreased cortical excitability and anodal tACS increased excitability. However, when a high-rate, short-duration, suprathreshold pulse-train was used this pattern was inverted. An integrate-and-fire model incorporating biophysical differences between cortical excitatory and inhibitory neurons could predict the experimental data and helped interpret these results. The model indicated that low-rate suprathreshold pulse-trains preferentially stimulate excitatory cortical neurons, whereas high-rate suprathreshold pulse-trains stimulate both excitatory and inhibitory neurons. If correct, this indicates that suprathreshold pulse-train stimulation may be able to selectively control the excitation-inhibition balance within a cortical network. The excitation-inhibition balance then likely plays an important role in determining whether subthreshold tACS will increase or decrease cortical excitability.SIGNIFICANCE STATEMENT Transcranial alternating current stimulation (tACS) is a noninvasive neuromodulation method that uses weak sinusoidal electric fields to modulate cortical activity. In healthy volunteers tACS can modulate perception, cognition, and motor function but the underlying neural mechanism is poorly understood. In this study, using rat motor cortex, we found that tACS effects are highly variable: applying the same tACS waveform to the same cortical area does not always give the same change in cortical excitability. An integrate-and-fire model incorporating excitatory pyramidal and inhibitory interneurons indicated that tACS effects likely depend on the cortical excitation-inhibition balance. When cortical activity is excitation dominated one particular tACS phase increases excitability, but when the cortical activity is inhibition dominated the same tACS phase actually decreases excitability.

Memory Takes Time

Memory is an adaptation to particular temporal properties of past events, such as the frequency of occurrence of a stimulus or the coincidence of multiple stimuli. In neurons, this adaptation can be understood in terms of a hierarchical system of molecular and cellular time windows, which collectively retain information from the past. We propose that this system makes various timescales of past experience simultaneously available for future adjustment of behavior. More generally, we propose that the ability to detect and respond to temporally structured information underlies the nervous system's capacity to encode and store a memory at molecular, cellular, synaptic, and circuit levels.

Target Cell Type-Dependent Differences in Ca2+ Channel Function Underlie Distinct Release Probabilities at Hippocampal Glutamatergic Terminals

Target cell type-dependent differences in presynaptic release probability (P_r ) and short-term plasticity are intriguing features of cortical microcircuits that increase the computational power of neuronal networks. Here, we tested the hypothesis that different voltage-gated Ca²⁺ channel densities in presynaptic active zones (AZs) underlie different P_r values. Two-photon Ca²⁺ imaging, triple immunofluorescent labeling, and 3D electron microscopic (EM) reconstruction of rat CA3 pyramidal cell axon terminals revealed ∼1.7-1.9 times higher Ca²⁺ inflow per AZ area in high P_r boutons synapsing onto parvalbumin-positive interneurons (INs) than in low P_r boutons synapsing onto mGluR1α-positive INs. EM replica immunogold labeling, however, demonstrated only 1.15 times larger Cav2.1 and Cav2.2 subunit densities in high P_r AZs. Our results indicate target cell type-specific modulation of voltage-gated Ca²⁺ channel function or different subunit composition as possible mechanisms underlying the functional differences. In addition, high P_r synapses are also characterized by a higher density of docked vesicles, suggesting that a concerted action of these mechanisms underlies the functional differences.SIGNIFICANCE STATEMENT Target cell type-dependent variability in presynaptic properties is an intriguing feature of cortical synapses. When a single cortical pyramidal cell establishes a synapse onto a somatostatin-expressing interneuron (IN), the synapse releases glutamate with low probability, whereas the next bouton of the same axon has high release probability when its postsynaptic target is a parvalbumin-expressing IN. Here, we used combined molecular, imaging, and anatomical approaches to investigate the mechanisms underlying these differences. Our functional experiments implied an approximately twofold larger Ca²⁺ channel density in high release probability boutons, whereas freeze-fracture immunolocalization demonstrated only a 15% difference in Ca²⁺ channel subunit densities. Our results point toward a postsynaptic target cell type-dependent regulation of Ca²⁺ channel function or different subunit composition as the underlying mechanism.

Somatostatin-Expressing Inhibitory Interneurons in Cortical Circuits

Cortical inhibitory neurons exhibit remarkable diversity in their morphology, connectivity, and synaptic properties. Here, we review the function of somatostatin-expressing (SOM) inhibitory interneurons, focusing largely on sensory cortex. SOM neurons also comprise a number of subpopulations that can be distinguished by their morphology, input and output connectivity, laminar location, firing properties, and expression of molecular markers. Several of these classes of SOM neurons show unique dynamics and characteristics, such as facilitating synapses, specific axonal projections, intralaminar input, and top-down modulation, which suggest possible computational roles. SOM cells can be differentially modulated by behavioral state depending on their class, sensory system, and behavioral paradigm. The functional effects of such modulation have been studied with optogenetic manipulation of SOM cells, which produces effects on learning and memory, task performance, and the integration of cortical activity. Different classes of SOM cells participate in distinct disinhibitory circuits with different inhibitory partners and in different cortical layers. Through these disinhibitory circuits, SOM cells help encode the behavioral relevance of sensory stimuli by regulating the activity of cortical neurons based on subcortical and intracortical modulatory input. Associative learning leads to long-term changes in the strength of connectivity of SOM cells with other neurons, often influencing the strength of inhibitory input they receive. Thus despite their heterogeneity and variability across cortical areas, current evidence shows that SOM neurons perform unique neural computations, forming not only distinct molecular but also functional subclasses of cortical inhibitory interneurons.

Selective Maturation of Temporal Dynamics of Intracortical Excitatory Transmission at the Critical Period Onset

Although the developmental maturation of cortical inhibitory synapses is known to be a critical factor in gating the onset of critical period (CP) for experience-dependent cortical plasticity, how synaptic transmission dynamics of other cortical synapses are regulated during the transition to CP remains unknown. Here, by systematically examining various intracortical synapses within layer 4 of the mouse visual cortex, we demonstrate that synaptic temporal dynamics of intracortical excitatory synapses on principal cells (PCs) and inhibitory parvalbumin- or somatostatin-expressing cells are selectively regulated before the CP onset, whereas those of intracortical inhibitory synapses and long-range thalamocortical excitatory synapses remain unchanged. This selective maturation of synaptic dynamics results from a ubiquitous reduction of presynaptic release and is dependent on visual experience. These findings provide an additional essential circuit mechanism for regulating CP timing in the developing visual cortex.

Inhibition in the Human Auditory Cortex

Despite their indispensable roles in sensory processing, little is known about inhibitory interneurons in humans. Inhibitory postsynaptic potentials cannot be recorded non-invasively, at least in a pure form, in humans. We herein sought to clarify whether prepulse inhibition (PPI) in the auditory cortex reflected inhibition via interneurons using magnetoencephalography. An abrupt increase in sound pressure by 10 dB in a continuous sound was used to evoke the test response, and PPI was observed by inserting a weak (5 dB increase for 1 ms) prepulse. The time course of the inhibition evaluated by prepulses presented at 10-800 ms before the test stimulus showed at least two temporally distinct inhibitions peaking at approximately 20-60 and 600 ms that presumably reflected IPSPs by fast spiking, parvalbumin-positive cells and somatostatin-positive, Martinotti cells, respectively. In another experiment, we confirmed that the degree of the inhibition depended on the strength of the prepulse, but not on the amplitude of the prepulse-evoked cortical response, indicating that the prepulse-evoked excitatory response and prepulse-evoked inhibition reflected activation in two different pathways. Although many diseases such as schizophrenia may involve deficits in the inhibitory system, we do not have appropriate methods to evaluate them; therefore, the easy and non-invasive method described herein may be clinically useful.

Physiologically based arousal state estimation and dynamics

A neural field model of the brain is used to represent brain states using physiologically based parameters rather than arbitrary, discrete sleep stages. Each brain state is represented as a point in a physiologically parametrized space. Over time, changes in brain state cause these points to trace continuous trajectories, unlike the artificial discrete jumps in sleep stage that occur with traditional sleep staging. The discrete Rechtschaffen and Kales sleep stages are associated with regions in the physiological parameter space based on their electroencephalographic features, which enables interpretation of traditional sleep stages in terms of physiological trajectories. Wake states are found to be associated with strong positive corticothalamic feedback compared to sleep. The existence of physiologically valid trajectories between brain states in the model is demonstrated. Actual trajectories for an individual can be determined by fitting the model using EEG alone, and enable analysis of the physiological differences between subjects.

Cortical Specializations Underlying Fast Computations

The time course of behaviorally relevant environmental events sets temporal constraints on neuronal processing. How does the mammalian brain make use of the increasingly complex networks of the neocortex, while making decisions and executing behavioral reactions within a reasonable time? The key parameter determining the speed of computations in neuronal networks is a time interval that neuronal ensembles need to process changes at their input and communicate results of this processing to downstream neurons. Theoretical analysis identified basic requirements for fast processing: use of neuronal populations for encoding, background activity, and fast onset dynamics of action potentials in neurons. Experimental evidence shows that populations of neocortical neurons fulfil these requirements. Indeed, they can change firing rate in response to input perturbations very quickly, within 1 to 3 ms, and encode high-frequency components of the input by phase-locking their spiking to frequencies up to 300 to 1000 Hz. This implies that time unit of computations by cortical ensembles is only few, 1 to 3 ms, which is considerably faster than the membrane time constant of individual neurons. The ability of cortical neuronal ensembles to communicate on a millisecond time scale allows for complex, multiple-step processing and precise coordination of neuronal activity in parallel processing streams, while keeping the speed of behavioral reactions within environmentally set temporal constraints.

Synaptic dynamics contribute to long-term single neuron response fluctuations

Firing rate variability at the single neuron level is characterized by long-memory processes and complex statistics over a wide range of time scales (from milliseconds up to several hours). Here, we focus on the contribution of non-stationary efficacy of the ensemble of synapses–activated in response to a given stimulus–on single neuron response variability. We present and validate a method tailored for controlled and specific long-term activation of a single cortical neuron in vitro via synaptic or antidromic stimulation, enabling a clear separation between two determinants of neuronal response variability: membrane excitability dynamics vs. synaptic dynamics. Applying this method we show that, within the range of physiological activation frequencies, the synaptic ensemble of a given neuron is a key contributor to the neuronal response variability, long-memory processes and complex statistics observed over extended time scales. Synaptic transmission dynamics impact on response variability in stimulation rates that are substantially lower compared to stimulation rates that drive excitability resources to fluctuate. Implications to network embedded neurons are discussed.

Investigation of synapse formation and function in a glutamatergic-GABAergic two-neuron microcircuit

Neural circuits are composed of mainly glutamatergic and GABAergic neurons, which communicate through synaptic connections. Many factors instruct the formation and function of these synapses; however, it is difficult to dissect the contribution of intrinsic cell programs from that of extrinsic environmental effects in an intact network. Here, we perform paired recordings from two-neuron microculture preparations of mouse hippocampal glutamatergic and GABAergic neurons to investigate how synaptic input and output of these two principal cells develop. In our reduced preparation, we found that glutamatergic neurons showed no change in synaptic output or input regardless of partner neuron cell type or neuronal activity level. In contrast, we found that glutamatergic input caused the GABAergic neuron to modify its output by way of an increase in synapse formation and a decrease in synaptic release efficiency. These findings are consistent with aspects of GABAergic synapse maturation observed in many brain regions. In addition, changes in GABAergic output are cell wide and not target-cell specific. We also found that glutamatergic neuronal activity determined the AMPA receptor properties of synapses on the partner GABAergic neuron. All modifications of GABAergic input and output required activity of the glutamatergic neuron. Because our system has reduced extrinsic factors, the changes we saw in the GABAergic neuron due to glutamatergic input may reflect initiation of maturation programs that underlie the formation and function of in vivo neural circuits.

Diverse thalamocortical short-term plasticity elicited by ongoing stimulation

To produce sensation, neuronal pathways must transmit and process stimulus patterns that unfold over time. This behavior is determined by short-term synaptic plasticity (STP), which shapes the temporal filtering properties of synapses in a pathway. We explored STP variability across thalamocortical (TC) synapses, measuring whole-cell responses to stimulation of TC fibers in layer 4 neurons of mouse barrel cortex in vitro. As expected, STP during stimulation from rest was dominated by depression. However, STP during ongoing stimulation was strikingly diverse across TC connections. Diversity took the form of variable tuning to the latest interstimulus interval: some connections responded weakly to shorter intervals, while other connections were facilitated. These behaviors did not cluster into categories but formed a continuum. Diverse tuning did not require disynaptic inhibition. Hence, monosynaptic excitatory lemniscal TC connections onto layer 4 do not behave uniformly during ongoing stimulation. Each connection responds differentially to particular stimulation intervals, enriching the ability of the pathway to convey complex, temporally fluctuating information.

Attention-deficit/hyperactivity disorder: neurophysiology, information processing, arousal and drug development

In this review, we draw on literature from both animal and human neurophysiological studies to consider the neurochemical mechanisms underlying attention-deficit/ hyperactivity disorder (ADHD). Psychophysiological and neuropsychological research is used to propose possible etiological endophenotypes of ADHD. These are conceptualized as patients with distinct cortical-arousal, information-processing or maturational abnormalities, or a combination thereof, and how the endophenotypes can be used to help drug development and optimize treatment and management. To illustrate, the paper focuses on neuro- and psychophysiological evidence that suggests cholinergic mechanisms may underlie specific information-processing abnormalities that occur in ADHD. The clinical implications for a cholinergic hypothesis of ADHD are considered, along with its possible implications for treatment and pharmacological development.

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

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

Hearing loss differentially affects thalamic drive to two cortical interneuron subtypes

Sensory deprivation, such as developmental hearing loss, leads to an adjustment of synaptic and membrane properties throughout the central nervous system. These changes are thought to compensate for diminished sound-evoked activity. This model predicts that compensatory changes should be synergistic with one another along each functional pathway. To test this idea, we examined the excitatory thalamic drive to two types of cortical inhibitory interneurons that display differential effects in response to developmental hearing loss. The inhibitory synapses made by fast-spiking (FS) cells are weakened by hearing loss, whereas those made by low threshold-spiking (LTS) cells remain strong but display greater short-term depression (Takesian et al. 2010). Whole-cell recordings were made from FS or LTS interneurons in a thalamocortical brain slice, and medial geniculate (MG)-evoked postsynaptic potentials were analyzed. Following hearing loss, MG-evoked net excitatory potentials were smaller than normal at FS cells but larger than normal at LTS cells. Furthermore, MG-evoked excitatory potentials displayed less short-term depression at FS cells and greater short-term depression at LTS cells. Thus deprivation-induced adjustments of excitatory synapses onto inhibitory interneurons are cell-type specific and parallel the changes made by the inhibitory afferents.

Cellular and synaptic architecture of multisensory integration in the mouse neocortex

Multisensory integration (MI) is crucial for sensory processing, but it is unclear how MI is organized in cortical microcircuits. Whole-cell recordings in a mouse visuotactile area located between primary visual and somatosensory cortices revealed that spike responses were less bimodal than synaptic responses but displayed larger multisensory enhancement. MI was layer and cell type specific, with multisensory enhancement being rare in the major class of inhibitory interneurons and in the output infragranular layers. Optogenetic manipulation of parvalbumin-positive interneuron activity revealed that the scarce MI of interneurons enables MI in neighboring pyramids. Finally, single-cell resolution calcium imaging revealed a gradual merging of modalities: unisensory neurons had higher densities toward the borders of the primary cortices, but were located in unimodal clusters in the middle of the cortical area. These findings reveal the role of different neuronal subcircuits in the synaptic process of MI in the rodent parietal cortex.

Local connections of excitatory neurons in motor-associated cortical areas of the rat

In spite of recent progress in brain sciences, the local circuit of the cerebral neocortex, including motor areas, still remains elusive. Morphological works on excitatory cortical circuitry from thalamocortical (TC) afferents to corticospinal neurons (CSNs) in motor-associated areas are reviewed here. First, TC axons of motor thalamic nuclei have been re-examined by the single-neuron labeling method. There are middle layer (ML)-targeting and layer (L) 1-preferring TC axon types in motor-associated areas, being analogous to core and matrix types, respectively, of Jones (1998) in sensory areas. However, the arborization of core-like motor TC axons spreads widely and disregards the columnar structure that is the basis of information processing in sensory areas, suggesting that motor areas adopt a different information-processing framework such as area-wide laminar organization. Second, L5 CSNs receive local excitatory inputs not only from L2/3 pyramidal neurons but also from ML spiny neurons, the latter directly processing cerebellar information of core-like TC neurons (TCNs). In contrast, basal ganglia information is targeted to apical dendrites of L2/3 and L5 pyramidal neurons through matrix TCNs. Third, L6 corticothalamic neurons (CTNs) are most densely innervated by ML spiny neurons located just above CTNs. Since CTNs receive only weak connections from L2/3 and L5 pyramidal neurons, the TC recurrent circuit composed of TCNs, ML spiny neurons and CTNs appears relatively independent of the results of processing in L2/3 and L5. It is proposed that two circuits sharing the same TC projection and ML neurons are embedded in the neocortex: one includes L2/3 and L5 neurons, processes afferent information in a feedforward way and sends the processed information to other cortical areas and subcortical regions; and the other circuit participates in a dynamical system of the TC recurrent circuit and may serve as the basis of autonomous activity of the neocortex.

Playing “Duck Duck Goose” With Neurons

Reduced connectivity between sending and receiving neurons (i.e., synaptic depression) may facilitate change detection by reducing responses for recently viewed objects so new objects can be highlighted. In the experiment reported here, we investigated high-level change detection following semantic satiation, which is the loss of meaning following repetition of a word. A computer simulation of a word-reading neural network with synaptic depression identified key predictions of connectivity reduction. A dynamic-causal-modeling analysis of magnetoencephalography (MEG) responses collected during a category-matching task identified connectivity reduction between a cortical region related to orthography and a cortical region related to semantics as the cause of the reduced MEG response to a repeated word. As predicted, prior repetitions of a category-matching word presented immediately after the repeated word enhanced semantic novelty, as measured with the M400 component. These results demonstrate that a combination of neural-network modeling and connectivity analyses can reveal the manner in which connectivity fluctuations underlie cognitive functions.

Short-term plasticity explains irregular persistent activity in working memory tasks

Persistent activity in cortex is the neural correlate of working memory (WM). In persistent activity, spike trains are highly irregular, even more than in baseline. This seemingly innocuous feature challenges our current understanding of the synaptic mechanisms underlying WM. Here we argue that in WM the prefrontal cortex (PFC) operates in a regime of balanced excitation and inhibition and that the observed temporal irregularity reflects this regime. We show that this requires that nonlinearities underlying the persistent activity are primarily in the neuronal interactions between PFC neurons. We also show that short-term synaptic facilitation can be the physiological substrate of these nonlinearities and that the resulting mechanism of balanced persistent activity is robust, in particular with respect to changes in the connectivity. As an example, we put forward a computational model of the PFC circuit involved in oculomotor delayed response task. The novelty of this model is that recurrent excitatory synapses are facilitating. We demonstrate that this model displays direction-selective persistent activity. We find that, even though the memory eventually degrades because of the heterogeneities, it can be stored for several seconds for plausible network size and connectivity. This model accounts for a large number of experimental findings, such as the findings that have shown that firing is more irregular during the persistent state than during baseline, that the neuronal responses are very diverse, and that the preferred directions during cue and delay periods are strongly correlated but tuning widths are not.

The cell-type specific cortical microcircuit: relating structure and activity in a full-scale spiking network model

In the past decade, the cell-type specific connectivity and activity of local cortical networks have been characterized experimentally to some detail. In parallel, modeling has been established as a tool to relate network structure to activity dynamics. While available comprehensive connectivity maps ( Thomson, West, et al. 2002; Binzegger et al. 2004) have been used in various computational studies, prominent features of the simulated activity such as the spontaneous firing rates do not match the experimental findings. Here, we analyze the properties of these maps to compile an integrated connectivity map, which additionally incorporates insights on the specific selection of target types. Based on this integrated map, we build a full-scale spiking network model of the local cortical microcircuit. The simulated spontaneous activity is asynchronous irregular and cell-type specific firing rates are in agreement with in vivo recordings in awake animals, including the low rate of layer 2/3 excitatory cells. The interplay of excitation and inhibition captures the flow of activity through cortical layers after transient thalamic stimulation. In conclusion, the integration of a large body of the available connectivity data enables us to expose the dynamical consequences of the cortical microcircuitry.

Rates and rhythms: a synergistic view of frequency and temporal coding in neuronal networks

In the CNS, activity of individual neurons has a small but quantifiable relationship to sensory representations and motor outputs. Coactivation of a few 10s to 100s of neurons can code sensory inputs and behavioral task performance within psychophysical limits. However, in a sea of sensory inputs and demand for complex motor outputs how is the activity of such small subpopulations of neurons organized? Two theories dominate in this respect: increases in spike rate (rate coding) and sharpening of the coincidence of spiking in active neurons (temporal coding). Both have computational advantages and are far from mutually exclusive. Here, we review evidence for a bias in neuronal circuits toward temporal coding and the coexistence of rate and temporal coding during population rhythm generation. The coincident expression of multiple types of gamma rhythm in sensory cortex suggests a mechanistic substrate for combining rate and temporal codes on the basis of stimulus strength.

The laminar cortex model: a new continuum cortex model incorporating laminar architecture

Local field potentials (LFPs) are widely used to study the function of local networks in the brain. They are also closely correlated with the blood-oxygen-level-dependent signal, the predominant contrast mechanism in functional magnetic resonance imaging. We developed a new laminar cortex model (LCM) to simulate the amplitude and frequency of LFPs. Our model combines the laminar architecture of the cerebral cortex and multiple continuum models to simulate the collective activity of cortical neurons. The five cortical layers (layer I, II/III, IV, V, and VI) are simulated as separate continuum models between which there are synaptic connections. The LCM was used to simulate the dynamics of the visual cortex under different conditions of visual stimulation. LFPs are reported for two kinds of visual stimulation: general visual stimulation and intermittent light stimulation. The power spectra of LFPs were calculated and compared with existing empirical data. The LCM was able to produce spontaneous LFPs exhibiting frequency-inverse (1/ƒ) power spectrum behaviour. Laminar profiles of current source density showed similarities to experimental data. General stimulation enhanced the oscillation of LFPs corresponding to gamma frequencies. During simulated intermittent light stimulation, the LCM captured the fundamental as well as high order harmonics as previously reported. The power spectrum expected with a reduction in layer IV neurons, often observed with focal cortical dysplasias associated with epilepsy was also simulated.

Local field potentials (LFPs) are low-frequency fluctuations of the electric fields produced by the brain. They have been widely studied to understand brain function and activity. LFPs reflect the activity of neurons within a few square millimeters of the cerebral cortex, an area containing more than 10,000 neurons. To avoid the complexity of simulating such a large number of individual neurons, the continuum cortex model was devised to simulate the collective activity of groups of neurons generating cortical LFPs. However, the continuum cortex model assumes that the cortex is two-dimensional and does not take into account the laminar architecture of the cerebral cortex. We developed a three-dimensional laminar cortex model (LCM) by combining laminar architecture with the continuum cortex model. This expansion enables the LCM to simulate the detailed three-dimensional distribution of the LFP within the cortex. We used the LCM to simulate LFPs within the visual cortex under different conditions of visual stimulation. The LCM reproduced the key features of LFPs observed in electrophysiological experiments. We conclude that the LCM is a potentially useful tool to investigate the underlying mechanism of LFPs.

A model of the differential representation of signal novelty in the local field potentials and spiking activity of the ventrolateral prefrontal cortex

Local field potentials (LFPs) and spiking activity reflect different types of information procssing. For example, neurophysiological studies indicate that signal novelty in the ventrolateral prefrontal cortex is differentially represented by LFPs and spiking activity: LFPs habituate to repeated stimulus presentations, whereas spiking activity does not. The neural mechanisms that allow for this differential representation between LFPs and spiking activity are not clear. Here, we model and simulate LFPs and spiking activity of neurons in the ventrolateral prefrontal cortex in order to elucidate potential mechanisms underlying this differential representation. We demonstrate that dynamic negative-feedback loops cause LFPs to habituate in response to repeated presentations of the same stimulus while spiking activity is maintained. This disassociation between LFPs and spiking activity may be a mechanism by which LFPs code stimulus novelty, whereas spiking activity carries abstract information, such as category membership and decision-related activity.

Synaptic depression and slow oscillatory activity in a biophysical network model of the cerebral cortex

Short-term synaptic depression (STD) is a form of synaptic plasticity that has a large impact on network computations. Experimental results suggest that STD is modulated by cortical activity, decreasing with activity in the network and increasing during silent states. Here, we explored different activity-modulation protocols in a biophysical network model for which the model displayed less STD when the network was active than when it was silent, in agreement with experimental results. Furthermore, we studied how trains of synaptic potentials had lesser decay during periods of activity (UP states) than during silent periods (DOWN states), providing new experimental predictions. We next tackled the inverse question of what is the impact of modifying STD parameters on the emergent activity of the network, a question difficult to answer experimentally. We found that synaptic depression of cortical connections had a critical role to determine the regime of rhythmic cortical activity. While low STD resulted in an emergent rhythmic activity with short UP states and long DOWN states, increasing STD resulted in longer and more frequent UP states interleaved with short silent periods. A still higher synaptic depression set the network into a non-oscillatory firing regime where DOWN states no longer occurred. The speed of propagation of UP states along the network was not found to be modulated by STD during the oscillatory regime; it remained relatively stable over a range of values of STD. Overall, we found that the mutual interactions between synaptic depression and ongoing network activity are critical to determine the mechanisms that modulate cortical emergent patterns.

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.

Release probability of hippocampal glutamatergic terminals scales with the size of the active zone

Cortical synapses have structural, molecular and functional heterogeneity; our knowledge regarding the relationship between their ultrastructural and functional parameters is still fragmented. Here we asked how the neurotransmitter release probability and presynaptic [Ca(2+)] transients relate to the ultrastructure of rat hippocampal glutamatergic axon terminals. Two-photon Ca(2+) imaging-derived optical quantal analysis and correlated electron microscopic reconstructions revealed a tight correlation between the release probability and the active-zone area. Peak amplitude of [Ca(2+)] transients in single boutons also positively correlated with the active-zone area. Freeze-fracture immunogold labeling revealed that the voltage-gated calcium channel subunit Cav2.1 and the presynaptic protein Rim1/2 are confined to the active zone and their numbers scale linearly with the active-zone area. Gold particles labeling Cav2.1 were nonrandomly distributed in the active zones. Our results demonstrate that the numbers of several active-zone proteins, including presynaptic calcium channels, as well as the number of docked vesicles and the release probability, scale linearly with the active-zone area.

Striatal dopamine transmission is subtly modified in human A53Tα-synuclein overexpressing mice

Mutations in, or elevated dosage of, SNCA, the gene for α-synuclein (α-syn), cause familial Parkinson's disease (PD). Mouse lines overexpressing the mutant human A53Tα-syn may represent a model of early PD. They display progressive motor deficits, abnormal cellular accumulation of α-syn, and deficits in dopamine-dependent corticostriatal plasticity, which, in the absence of overt nigrostriatal degeneration, suggest there are age-related deficits in striatal dopamine (DA) signalling. In addition A53Tα-syn overexpression in cultured rodent neurons has been reported to inhibit transmitter release. Therefore here we have characterized for the first time DA release in the striatum of mice overexpressing human A53Tα-syn, and explored whether A53Tα-syn overexpression causes deficits in the release of DA. We used fast-scan cyclic voltammetry to detect DA release at carbon-fibre microelectrodes in acute striatal slices from two different lines of A53Tα-syn-overexpressing mice, at up to 24 months. In A53Tα-syn overexpressors, mean DA release evoked by a single stimulus pulse was not different from wild-types, in either dorsal striatum or nucleus accumbens. However the frequency responsiveness of DA release was slightly modified in A53Tα-syn overexpressors, and in particular showed slight deficiency when the confounding effects of striatal ACh acting at presynaptic nicotinic receptors (nAChRs) were antagonized. The re-release of DA was unmodified after single-pulse stimuli, but after prolonged stimulation trains, A53Tα-syn overexpressors showed enhanced recovery of DA release at old age, in keeping with elevated striatal DA content. In summary, A53Tα-syn overexpression in mice causes subtle changes in the regulation of DA release in the striatum. While modest, these modifications may indicate or contribute to striatal dysfunction.

Short-term plasticity of unitary inhibitory-to-inhibitory synapses depends on the presynaptic interneuron subtype

Excitatory-to-inhibitory cortical synapses exhibit either short-term facilitation or depression, depending on the subtype identity of the postsynaptic interneuron, while the short-term plasticity (STP) of inhibitory-to-excitatory synapses depends on the presynaptic interneuron. However, the rules governing STP of inhibitory-to-inhibitory synapses have not yet been determined. We recorded 109 unitary connections made by the two major inhibitory interneuron subtypes in layer 4 of mouse somatosensory cortex, fast-spiking (FS) and somatostatin-containing (SOM) interneurons, on each other and on excitatory, regular-spiking (RS) neurons. In all pairs, we measured dynamic changes in the postsynaptic response to a 20 Hz train of presynaptic action potentials. In half of our dataset, we also measured kinetic properties of the unitary IPSC: latency, rise time, and decay time constant. We found a pronounced dependency of STP on the presynaptic, but not the postsynaptic, identity: FS interneurons made strongly depressing connections on FS, SOM, and RS targets, while in synapses made by SOM interneurons on FS and RS targets, weak early depression was followed by weak late facilitation. IPSC latency and rise time were also strongly dependent on the presynaptic interneuron subtype, being 1.5-2× slower in output synapses of SOM compared with FS interneurons. In contrast, the IPSC decay time constant depended only on the postsynaptic class, with 1.5× slower decay on excitatory compared with inhibitory targets. The properties of the inhibitory outputs of FS and SOM interneurons reciprocate the properties of their excitatory inputs and imply a dynamic spatiotemporal division of labor between these two major inhibitory subsystems.

Contrast-dependent variations in the excitatory classical receptive field and suppressive nonclassical receptive field of cat primary visual cortex

In area V1 of cat and monkey, there is a surround region beyond the classical receptive field (CRF) which alone is unresponsive but may modulate the cell's response. This field is referred to as the "nonclassical receptive field" (nCRF). It has been reported in monkey that the extent of CRF and/or nCRF of V1 neurons is not fixed but varies with stimulus contrast. We reexamined the contrast dependence of V1 neurons in cat to determine whether this differs from previous studies in macaque. By fitting the spatial summation curves obtained at different contrasts with a difference of Gaussians model, we estimated quantitatively the effect of contrast on the spatial extent of the CRF and nCRF as well as the strength of surround suppression. Our results showed that both the CRF and nCRF expanded at low contrast, but the expansion is more marked for the CRF than for the nCRF. Although the effect of contrast on surround suppression was varied, the overall suppression increased significantly at high contrast. Moreover, the contrast-dependent change in the extent of CRF is independent of the change in suppression strength. Overall, our results in cat are in agreement with those obtained in macaque money.

The role of glutamatergic inputs onto parvalbumin-positive interneurons: relevance for schizophrenia

Cognitive impairment, a core feature of schizophrenia, has been suggested to arise from a disturbance of gamma oscillations that is due to decreased neurotransmission from the parvalbumin (PV) subtype of interneurons. Indeed, PV interneurons have uniquely fast membrane and synaptic properties that are crucially important for network functions such as feedforward inhibition or gamma oscillations. The causes leading to impairment of PV neurotransmission in schizophrenia are still under investigation. Interestingly, NMDA receptors (NMDARs) antagonism results in schizophrenia-like symptoms in healthy adults. Additionally, systemic NMDAR antagonist administration increases prefrontal cortex pyramidal cell firing, apparently by producing disinhibition, and repeated exposure to NMDA antagonists leads to changes in the GABAergic markers that mimic the impairments found in schizophrenia. Based on these findings, PV neuron deficits in schizophrenia have been proposed to be secondary to (NMDAR) hypofunction at glutamatergic synapses onto these cells. However, NMDARs generate long-lasting postsynaptic currents that result in prolonged depolarization of the postsynaptic cells, a property inconsistent with the role of PV cells in network dynamics. Here, we review evidence leading to the conclusion that cortical disinhibition and GABAergic impairment produced by NMDAR antagonists are unlikely to be mediated via NMDARs at glutamatergic synapses onto mature cortical PV neurons.

Explorative Data Analysis of In-Vitro Neuronal Network Behavior Based on an Unsupervised Learning Approach

In the present chapter authors want to expose new insights in the field of Computational Neuroscience at regard to the study of neuronal networks grown in vitro. Such kind of analyses can exploit the availability of a huge amount of data thanks to the use of Multi Electrode Arrays (MEA), a multi-channel technology which allows capturing the activity of several different neuronal cells for long time recordings. Given the possibility of simultaneous targeting of various sites, neuroscientists are so applying such recent technology for various researches. The chapter begins by giving a brief presentation of MEA technology and of the data produced in output, punctuating some of the pros and cons of MEA recordings. Then we present an overview of the analytical techniques applied in order to extrapolate the hidden information from available data. Then we shall explain the approach we developed and applied on MEAs prepared in our cell culture laboratory, consisting of statistical methods capturing the main features of the spiking, in particular bursting, activity of various neuron, and performing data dimensionality reduction and clustering, in order to classify neurons according to their spiking properties having showed correlated features. Finally the chapter wants to furnish to neuroscientists an overview about the quantitative analysis of in-vitro spiking activity data recorded via MEA technology and to give an example of explorative analysis applied on MEA data. Such study is based on methods from Statistics and Machine Learning or Computer Science but at the same time strictly related to neurophysiological interpretations of the putative pharmacological manipulation of synaptic connections and mode of firing, with the final aim to extract new information and knowledge about neuronal networks behavior and organization.