Postsynaptic variability of firing in rat cortical neurons: the roles of input synchronization and synaptic NMDA receptor conductance

The type of inhibition provided by thalamic interneurons alters the input selectivity of thalamocortical neurons

A fundamental problem in neuroscience is how neurons select for their many inputs. A common assumption is that a neuron's selectivity is largely explained by differences in excitatory synaptic input weightings. Here we describe another solution to this important problem. We show that within the first order visual thalamus, the type of inhibition provided by thalamic interneurons has the potential to alter the input selectivity of thalamocortical neurons. To do this, we developed conductance injection protocols to compare how different types of synchronous and asynchronous GABA release influence thalamocortical excitability in response to realistic patterns of retinal ganglion cell input. We show that the asynchronous GABA release associated with tonic inhibition is particularly efficient at maintaining information content, ensuring that thalamocortical neurons can distinguish between their inputs. We propose a model where alterations in GABA release properties results in rapid changes in input selectivity without requiring structural changes in the network.

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

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

Variety Is the Spice of Life: Positive and Negative Effects of Noise in Electrical Stimulation of the Nervous System

Noisy stimuli may hold the key for optimal electrical stimulation of the nervous system. Possible mechanisms of noise’s impact upon neuronal function are discussed, including intracellular, extracellular, and systems-level mechanisms. Specifically, channel resonance, stochastic resonance, high conductance states, and network binding are investigated. These mechanisms are examined and possible directions of growth for the field are discussed, with examples of applications provided from the fields of deep brain stimulation or spinal cord injury. Together, this review highlights the theoretical basis and evidence base for the use of noise to enhance current stimulation paradigms of the nervous system.

L-type voltage-gated calcium channel regulation of in vitro human cortical neuronal networks

The combination of in vitro multi-electrode arrays (MEAs) and the neuronal differentiation of stem cells offers the capability to study human neuronal networks from patient or engineered human cell lines. Here, we use MEA-based assays to probe synaptic function and network interactions of hiPSC-derived neurons. Neuronal network behaviour first emerges at approximately 30 days of culture and is driven by glutamate neurotransmission. Over a further 30 days, inhibitory GABAergic signalling shapes network behaviour into a synchronous regular pattern of burst firing activity and low activity periods. Gene mutations in L-type voltage gated calcium channel subunit genes are strongly implicated as genetic risk factors for the development of schizophrenia and bipolar disorder. We find that, although basal neuronal firing rate is unaffected, there is a dose-dependent effect of L-type voltage gated calcium channel inhibitors on synchronous firing patterns of our hiPSC-derived neural networks. This demonstrates that MEA assays have sufficient sensitivity to detect changes in patterns of neuronal interaction that may arise from hypo-function of psychiatric risk genes. Our study highlights the utility of in vitro MEA based platforms for the study of hiPSC neural network activity and their potential use in novel compound screening.

Unique Features of Network Bursts Emerge From the Complex Interplay of Excitatory and Inhibitory Receptors in Rat Neocortical Networks

Spontaneous network activity plays a fundamental role in the formation of functional networks during early development. The landmark of this activity is the recurrent emergence of intensive time-limited network bursts (NBs) rapidly spreading across the entire dissociated culture in vitro. The main excitatory mediators of NBs are glutamatergic alpha-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptors (AMPARs) and N-Methyl-D-aspartic-acid receptors (NMDARs) that express fast and slow ion channel kinetics, respectively. The fast inhibition of the activity is mediated through gamma-aminobutyric acid type A receptors (GABA_ARs). Although the AMPAR, NMDAR and GABA_AR kinetics have been biophysically characterized in detail at the monosynaptic level in a variety of brain areas, the unique features of NBs emerging from the kinetics and the complex interplay of these receptors are not well understood. The goal of this study is to analyze the contribution of fast GABA_ARs on AMPAR- and NMDAR- mediated spontaneous NB activity in dissociated neonatal rat cortical cultures at 3 weeks in vitro. The networks were probed by both acute and gradual application of each excitatory receptor antagonist and combinations of acute excitatory and inhibitory receptor antagonists. At the same time, the extracellular network-wide activity was recorded with microelectrode arrays (MEAs). We analyzed the characteristic NB measures extracted from NB rate profiles and the distributions of interspike intervals, interburst intervals, and electrode recruitment time as well as the similarity of spatio-temporal patterns of network activity under different receptor antagonists. We show that NBs were rapidly initiated and recruited as well as diversely propagated by AMPARs and temporally and spatially maintained by NMDARs. GABA_ARs reduced the spiking frequency in AMPAR-mediated networks and dampened the termination of NBs in NMDAR-mediated networks as well as slowed down the recruitment of activity in all networks. Finally, we show characteristic super bursts composed of slow NBs with highly repetitive spatio-temporal patterns in gradually AMPAR blocked networks. To the best of our knowledge, this study is the first to unravel in detail how the three main mediators of synaptic transmission uniquely shape the NB characteristics, such as the initiation, maintenance, recruitment and termination of NBs in cortical cell cultures in vitro.

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.

Mutation of the Dyslexia-Associated Gene Dcdc2 Enhances Glutamatergic Synaptic Transmission Between Layer 4 Neurons in Mouse Neocortex

Variants in DCDC2 have been associated with reading disability in humans, and targeted mutation of Dcdc2 in mice causes impairments in both learning and sensory processing. In this study, we sought to determine whether Dcdc2 mutation affects functional synaptic circuitry in neocortex. We found mutation in Dcdc2 resulted in elevated spontaneous and evoked glutamate release from neurons in somatosensory cortex. The probability of release was decreased to wild-type level by acute application of N-methyl-d-aspartate receptor (NMDAR) antagonists when postsynaptic NMDARs were blocked by intracellular MK-801, and could not be explained by elevated ambient glutamate, suggesting altered, nonpostsynaptic NMDAR activation in the mutants. In addition, we determined that the increased excitatory transmission was present at layer 4-layer 4 but not thalamocortical connections in Dcdc2 mutants, and larger evoked synaptic release appeared to enhance the NMDAR-mediated effect. These results demonstrate an NMDAR activation-gated, increased functional excitatory connectivity between layer 4 lateral connections in somatosensory neocortex of the mutants, providing support for potential changes in cortical connectivity and activation resulting from mutation of dyslexia candidate gene Dcdc2.

Vesicles derived via AP-3-dependent recycling contribute to asynchronous release and influence information transfer

Action potentials trigger synchronous and asynchronous neurotransmitter release. Temporal properties of both types of release could be altered in an activity-dependent manner. While the effects of activity-dependent changes in synchronous release on postsynaptic signal integration have been studied, the contribution of asynchronous release to information transfer during natural stimulus patterns is unknown. Here we find that during trains of stimulations, asynchronous release contributes to the precision of action potential firing. Our data show that this form of release is selectively diminished in AP-3b2 KO animals, which lack functional neuronal AP-3, an adaptor protein regulating vesicle formation from endosomes generated during bulk endocytosis. We find that in the absence of neuronal AP-3, asynchronous release is attenuated and the activity-dependent increase in the precision of action potential timing is compromised. Lack of asynchronous release decreases the capacity of synaptic information transfer and renders synaptic communication less reliable in response to natural stimulus patterns.

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.

Command-line cellular electrophysiology for conventional and real-time closed-loop experiments

BACKGROUND

Current software tools for electrophysiological experiments are limited in flexibility and rarely offer adequate support for advanced techniques such as dynamic clamp and hybrid experiments, which are therefore limited to laboratories with a significant expertise in neuroinformatics.

NEW METHOD

We have developed lcg, a software suite based on a command-line interface (CLI) that allows performing both standard and advanced electrophysiological experiments. Stimulation protocols for classical voltage and current clamp experiments are defined by a concise and flexible meta description that allows representing complex waveforms as a piece-wise parametric decomposition of elementary sub-waveforms, abstracting the stimulation hardware. To perform complex experiments lcg provides a set of elementary building blocks that can be interconnected to yield a large variety of experimental paradigms.

RESULTS

We present various cellular electrophysiological experiments in which lcg has been employed, ranging from the automated application of current clamp protocols for characterizing basic electrophysiological properties of neurons, to dynamic clamp, response clamp, and hybrid experiments. We finally show how the scripting capabilities behind a CLI are suited for integrating experimental trials into complex workflows, where actual experiment, online data analysis and computational modeling seamlessly integrate.

COMPARISON WITH EXISTING METHODS

We compare lcg with two open source toolboxes, RTXI and RELACS.

CONCLUSIONS

We believe that lcg will greatly contribute to the standardization and reproducibility of both simple and complex experiments. Additionally, on the long run the increased efficiency due to a CLI will prove a great benefit for the experimental community.

Contribution of transcriptional and translational mechanisms to the recovery aspect of sleep regulation

Sleep parallels brain functioning and mental health. Neuronal activity during wakefulness leads to a subsequent increase in sleep intensity as measured using electroencephalographic slow-wave activity (SWA; index of neuronal synchrony in the low-frequency range). Wakefulness, and particularly prolonged wakefulness, also drives important changes in brain gene expression and changes in protein regulation. The role of these two cellular mechanisms in sleep-wake regulation has typically been studied independently, and their exact contribution to SWA remains poorly defined. In this review, we highlight that many transcriptional pathways driven by sleep deprivation are associated to protein regulation. We first describe the relationship between cytokines, clock genes, and markers of sleep need with an emphasis on transcriptional processes. Observations regarding the role of protein metabolism in sleep-wake regulation are then depicted while presenting interconnections between transcriptional and translational responses driven by sleep loss. Lastly, a manner by which this integrated response can feed back on neuronal network activity to determine sleep intensity is proposed. Overall, the literature supports that a complex cross-talk between transcriptional and translational regulation during prolonged wakefulness drives the changes in sleep intensity as a function of the sleep/wake history.

Decreased neuronal bursting and phase synchrony in the hippocampus of streptozotocin diabetic rats

Diabetic encephalopathy is one of the complications of diabetes. Cognitive dysfunction is the main consequence. Previous findings from neuroanatomical and in vitro electrophysiological studies showed that the structure and function of the hippocampus is impaired in diabetes, which may underlie the cognitive dysfunction induced by diabetes. However the study of electrophysiological abnormality of hippocampal neurons in intact networks is sparse. In the current study, we recorded the spontaneous firing of neurons in hippocampal CA1 area in anesthetized streptozotozin (STZ)-diabetic and age-matched control rats. Profound reduction in burst activity was found in diabetic rats. Compared to control rats, the intra-burst inter-spike intervals were prolonged significantly in diabetic rats, while the burst ratio and the mean number of spikes within a burst decreased significantly. Treatment with APP 17-mer peptide retarded the effects of diabetes on these parameters. In addition, the average PLV of diabetic rats was lower than that of control rats. These findings provide in vivo electrophysiological evidence for the impairment of hippocampal function in STZ-diabetic rats, and may have some implications in the mechanisms associated with cognitive deficits in diabetes.

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.

ENHANCING THE YIELD OF HIGH-DENSITY ELECTRODE ARRAYS THROUGH AUTOMATED ELECTRODE SELECTION

Recently developed CMOS-based microprobes contain hundreds of electrodes on a single shaft with inter-electrode distances as small as 30 μm. So far, neuroscientists needed to select electrodes manually from hundreds of electrodes. Here we present an electronic depth control algorithm that allows to select electrodes automatically, hereby allowing to reduce the amount of data and locating those electrodes that are close to neurons. The electrodes are selected according to a new penalized signal-to-noise ratio (PSNR) criterion that demotes electrodes from becoming selected if their signals are redundant with previously selected electrodes. It is shown that, using the PSNR, interneurons generating smaller spikes are also selected. We developed a model that aims to evaluate algorithms for electronic depth control, but also generates benchmark data for testing spike sorting and spike detection algorithms. The model comprises a realistic tufted pyramidal cell, non-tufted pyramidal cells and inhibitory interneurons. All neurons are synaptically activated by hundreds of fibers. This arrangement allows the algorithms to be tested in more realistic conditions, including backgrounds of synaptic potentials, varying spike rates with bursting and spike amplitude attenuation.

Neurophysiological and computational principles of cortical rhythms in cognition

Synchronous rhythms represent a core mechanism for sculpting temporal coordination of neural activity in the brain-wide network. This review focuses on oscillations in the cerebral cortex that occur during cognition, in alert behaving conditions. Over the last two decades, experimental and modeling work has made great strides in elucidating the detailed cellular and circuit basis of these rhythms, particularly gamma and theta rhythms. The underlying physiological mechanisms are diverse (ranging from resonance and pacemaker properties of single cells to multiple scenarios for population synchronization and wave propagation), but also exhibit unifying principles. A major conceptual advance was the realization that synaptic inhibition plays a fundamental role in rhythmogenesis, either in an interneuronal network or in a reciprocal excitatory-inhibitory loop. Computational functions of synchronous oscillations in cognition are still a matter of debate among systems neuroscientists, in part because the notion of regular oscillation seems to contradict the common observation that spiking discharges of individual neurons in the cortex are highly stochastic and far from being clocklike. However, recent findings have led to a framework that goes beyond the conventional theory of coupled oscillators and reconciles the apparent dichotomy between irregular single neuron activity and field potential oscillations. From this perspective, a plethora of studies will be reviewed on the involvement of long-distance neuronal coherence in cognitive functions such as multisensory integration, working memory, and selective attention. Finally, implications of abnormal neural synchronization are discussed as they relate to mental disorders like schizophrenia and autism.

Consistency and diversity of spike dynamics in the neurons of bed nucleus of stria terminalis of the rat: a dynamic clamp study

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

Neuronal arithmetic

The vast computational power of the brain has traditionally been viewed as arising from the complex connectivity of neural networks, in which an individual neuron acts as a simple linear summation and thresholding device. However, recent studies show that individual neurons utilize a wealth of nonlinear mechanisms to transform synaptic input into output firing. These mechanisms can arise from synaptic plasticity, synaptic noise, and somatic and dendritic conductances. This tool kit of nonlinear mechanisms confers considerable computational power on both morphologically simple and more complex neurons, enabling them to perform a range of arithmetic operations on signals encoded ina variety of different ways.

Uncovering bifurcation patterns in cortical synapses

Individual cortical synapses are known to exhibit a very complex short-time dynamic behaviour in response to simple "naturalistic" stimulation. We describe a computational study of the experimentally obtained excitatory post-synaptic potential trains of individual cortical synapses. By adopting a new nonlinear modelling scheme we construct robust and repeatable models of the underlying dynamics. These models suggest that cortical synapses exhibit a wide range of either periodic or chaotic dynamics. For stimulus at a fixed rate our models predict that the response of the individual synapse will vary from a fixed point to periodic and chaotic, depending on the frequency of stimulus. Dynamics for individual synapses vary widely, suggesting that the individual behaviour of synapses is highly tuned and that the dynamic behaviour of even a small network of synapse-coupled neurons could be extremely varied.

Context-dependent effects of NMDA receptors on precise timing information at the endbulb of Held in the cochlear nucleus

Many synapses contain both AMPA receptors (AMPAR) and N-methyl-d-aspartate receptors (NMDAR), but their different roles in synaptic computation are not clear. We address this issue at the auditory nerve fiber synapse (called the endbulb of Held), which is formed on bushy cells of the cochlear nucleus. The endbulb refines and relays precise temporal information to nuclei responsible for sound localization. The endbulb has a number of specializations that aid precise timing, including AMPAR-mediated excitatory postsynaptic currents (EPSCs) with fast kinetics. Voltage-clamp experiments in mouse brain slices revealed that slow NMDAR EPSCs are maintained at mature endbulbs, contributing a peak conductance of around 10% of the AMPAR-mediated EPSC. During repetitive synaptic activity, AMPAR EPSCs depressed and NMDAR EPSCs summated, thereby increasing the relative importance of NMDARs. This could impact temporal precision of bushy cells because of the slow kinetics of NMDARs. We tested this by blocking NMDARs and quantifying bushy cell spike timing in current clamp when single endbulbs were activated. These experiments showed that NMDARs contribute to an increased probability of firing, shorter latency, and reduced jitter. Dynamic-clamp experiments confirmed this effect and showed it was dose-dependent. Bushy cells can receive inputs from multiple endbulbs. When we applied multiple synaptic inputs in dynamic clamp, NMDARs had less impact on spike timing. NMDAR conductances much higher than mature levels could disrupt spiking, which may explain its downregulation during development. Thus mature NMDAR expression can support the conveying of precise temporal information at the endbulb, depending on the stimulus conditions.

Network-induced chaos in integrate-and-fire neuronal ensembles

It has been shown that a single standard linear integrate-and-fire (IF) neuron under a general time-dependent stimulus cannot possess chaotic dynamics despite the firing-reset discontinuity. Here we address the issue of whether conductance-based, pulsed-coupled network interactions can induce chaos in an IF neuronal ensemble. Using numerical methods, we demonstrate that all-to-all, homogeneously pulse-coupled IF neuronal networks can indeed give rise to chaotic dynamics under an external periodic current drive. We also provide a precise characterization of the largest Lyapunov exponent for these high dimensional nonsmooth dynamical systems. In addition, we present a stable and accurate numerical algorithm for evaluating the largest Lyapunov exponent, which can overcome difficulties encountered by traditional methods for these nonsmooth dynamical systems with degeneracy induced by, e.g., refractoriness of neurons.

Dynamical foundations of the neural circuit for bayesian decision making

On the basis of accumulating behavioral and neural evidences, it has recently been proposed that the brain neural circuits of humans and animals are equipped with several specific properties, which ensure that perceptual decision making implemented by the circuits can be nearly optimal in terms of Bayesian inference. Here, I introduce the basic ideas of such a proposal and discuss its implications from the standpoint of biophysical modeling developed in the framework of dynamical systems.

Robust microcircuit synchronization by inhibitory connections

Microcircuits in different brain areas share similar architectural and biophysical properties with compact motor networks known as central pattern generators (CPGs). Consequently, CPGs have been suggested as valuable biological models for understanding of microcircuit dynamics and particularly, their synchronization. We use a well known compact motor network, the lobster pyloric CPG to study principles of intercircuit synchronization. We couple separate pyloric circuits obtained from two animals via artificial synapses and observe how their synchronization depends on the topology and kinetic parameters of the computer-generated synapses. Stable in-phase synchronization appears when electrically coupling the pacemaker groups of the two networks, but reciprocal inhibitory connections produce more robust and regular cooperative activity. Contralateral inhibitory connections offer effective synchronization and flexible setting of the burst phases of the interacting networks. We also show that a conductance-based mathematical model of the coupled circuits correctly reproduces the observed dynamics illustrating the generality of the phenomena.

Electrical Synapses Between AII Amacrine Cells: Dynamic Range and Functional Consequences of Variation in Junctional Conductance

AII amacrine cells form a network of electrically coupled interneurons in the mammalian retina and tracer coupling studies suggest that the junctional conductance ( Gj) can be modulated. However, the dynamic range of Gjand the functional consequences of varying Gjover the dynamic range are unknown. Here we use whole cell recordings from pairs of coupled AII amacrine cells in rat retinal slices to provide direct evidence for physiological modulation of Gj, appearing as a time-dependent increase from about 500 pS to a maximum of about 3,000 pS after 30–90 min of recording. The increase occurred in recordings with low- but not high-resistance pipettes, suggesting that it was related to intracellular washout and perturbation of a modulatory system. Computer simulations of a network of electrically coupled cells verified that our recordings were able to detect and quantify changes in Gjover a large range. Dynamic-clamp electrophysiology, with insertion of electrical synapses between AII amacrine cells, allowed us to finely and reversibly control Gjwithin the same range observed for physiologically coupled cells and to examine the quantitative relationship between Gjand steady-state coupling coefficient, synchronization of subthreshold membrane potential fluctuations, synchronization and transmission of action potentials, and low-pass filter characteristics. The range of Gjvalues over which signal transmission was modulated depended strongly on the specific functional parameter examined, with the largest range observed for action potential transmission and synchronization, suggesting that the full range of Gjvalues observed during spontaneous run-up of coupling could represent a physiologically relevant dynamic range.

Spontaneous neuronal burst discharges as dependent and independent variables in the maturation of cerebral cortex tissue cultured in vitro: a review of activity-dependent studies in live 'model' systems for the development of intrinsically generated bioelectric slow-wave sleep patterns

A survey is presented of recent experiments which utilize spontaneous neuronal spike trains as dependent and/or independent variables in developing cerebral cortex cultures when synaptic transmission is interfered with for varying periods of time. Special attention is given to current difficulties in selecting suitable preparations for carrying out biologically relevant developmental studies, and in applying spike-train analysis methods with sufficient resolution to detect activity-dependent age and treatment effects. A hierarchy of synchronized nested burst discharges which approximate early slow-wave sleep patterns in the intact organism is established as a stable basis for isolated cortex function. The complexity of reported long- and short-term homeostatic responses to experimental interference with synaptic transmission is reviewed, and the crucial role played by intrinsically generated bioelectric activity in the maturation of cortical networks is emphasized.

Noise in the nervous system

Noise--random disturbances of signals--poses a fundamental problem for information processing and affects all aspects of nervous-system function. However, the nature, amount and impact of noise in the nervous system have only recently been addressed in a quantitative manner. Experimental and computational methods have shown that multiple noise sources contribute to cellular and behavioural trial-to-trial variability. We review the sources of noise in the nervous system, from the molecular to the behavioural level, and show how noise contributes to trial-to-trial variability. We highlight how noise affects neuronal networks and the principles the nervous system applies to counter detrimental effects of noise, and briefly discuss noise's potential benefits.

Cellular and network contributions to excitability of layer 5 neocortical pyramidal neurons in the rat

There is a considerable gap between investigating the dynamics of single neurons and the computational aspects of neural networks. A growing number of studies have attempted to overcome this gap using the excitation in brain slices elicited by various chemical manipulations of the bath solution. However, there has been no quantitative study on the effects of these manipulations on the cellular and network factors controlling excitability. Using the whole-cell configuration of the patch-clamp technique we recorded the membrane potential from the soma of layer 5 pyramidal neurons in acute brain slices from the somatosensory cortex of young rats at 22°C and 35°C. Using blockers of synaptic transmission, we show distinct changes in cellular properties following modification of the ionic composition of the artificial cerebrospinal fluid (ACSF). Thus both cellular and network changes may contribute to the observed effects of slice excitation solutions on the physiology of single neurons. Furthermore, our data suggest that the difference in the ionic composition of current standard ACSF from that of CSF measured in vivo cause ACSF to depress network activity in acute brain slices. This may affect outcomes of experiments investigating biophysical and physiological properties of neurons in such preparations. Our results strongly advocate the necessity of redesigning experiments routinely carried out in the quiescent acute brain slice preparation.

A scriptable DSP-based system for dynamic conductance injection

A variety of software and hardware systems have been developed to inject controlled electrical conductances into excitable cells, to investigate the physiological mechanisms of action potential generation. These systems face several challenges: the need to model complex conductances, including voltage-gated ion channels, synaptic conductances controlled by electrical models of entire cells or even networks of cells, to do so rapidly and stably, with precisely controlled update intervals of 20micros or less, and to present an easy and flexible interface to the user, allowing new experiments to be designed and executed easily. In this paper I describe a new software system (SM-2) which is designed to meet these requirements, and which runs on the current generation of digital-signal-processing (DSP) analog input-output I/O boards, hosted in Windows PCs. Its key innovation is its configurability by simple user-written text scripts, or "scriptability", which gives it a high flexibility of purpose, and allows non-programmers the capacity to rapidly design and use new Hodgkin-Huxley-type active conductances, conductances with arbitrary current-voltage relationships, Markov process conductance mechanisms with user-specified rate matrices, and hybrid networks of virtual cells. At the same time, the hardware platform allows this to be achieved with a fast and accurately timed input-computation-output cycle.

Measurement of variability dynamics in cortical spike trains

We propose a method for the time-resolved joint analysis of two related aspects of single neuron variability, the spiking irregularity measured by the squared coefficient of variation (CV(2)) of the ISIs and the trial-by-trial variability of the spike count measured by the Fano factor (FF). We provide a calibration of both estimators using the theory of renewal processes, and verify it for spike trains recorded in vitro. Both estimators exhibit a considerable bias for short observations that count less than about 5-10 spikes on average. The practical difficulty of measuring the CV(2) in rate modulated data can be overcome by a simple procedure of spike train demodulation which was tested in numerical simulations and in real spike trains. We propose to test neuronal spike trains for deviations from the null-hypothesis FF=CV(2). We show that cortical pyramidal neurons, recorded under controlled stationary input conditions in vitro, comply with this assumption. Performing a time-resolved joint analysis of CV(2) and FF of a single unit recording from the motor cortex of a behaving monkey we demonstrate how the dynamic change of their quantitative relation can be interpreted with respect to neuron intrinsic and extrinsic factors that influence cortical variability in vivo. Finally, we discuss the effect of several additional factors such as serial interval correlation and refractory period on the empiric relation of FF and CV(2).

Pathway-specific use-dependent dynamics of excitatory synaptic transmission in rat intracortical circuits

Information processing in neuronal networks is determined by the use-dependent dynamics of synaptic transmission. Here we characterize the dynamic properties of excitatory synaptic transmission in two major intracortical pathways that target the output neurons of the neocortex, by recording unitary EPSPs from layer 5 pyramidal neurons evoked in response to action potential trains of increasing complexity in presynaptic layer 2/3 or layer 5 pyramidal neurons. We find that layer 2/3 to layer 5 synaptic transmission is dominated by frequency-dependent depression when generated at fixed frequencies of > 10 Hz. Synaptic depression evolved on a spike-by-spike basis in response to action potential trains that possessed a broad range of interspike intervals, but a low mean frequency (10 Hz). Layer 2/3 to layer 2/3 and layer 2/3 to layer 5 synapses were incapable of sustained release during prolonged, complex trains of presynaptic action potential firing (mean frequency, 48 Hz). By contrast, layer 5 to layer 5 synapses operated effectively across a wide range of frequencies, exhibiting increased efficacy at frequencies > 10 Hz. Furthermore, layer 5 to layer 5 synapses sustained release throughout the duration of prolonged, complex spike trains. The use-dependent properties of synaptic transmission could be modulated by pharmacologically changing the probability of release and by induction of long-term depression. The dynamic properties of intracortical excitatory synapses are therefore pathway-specific. We suggest that the synaptic output of layer 5 pyramidal neurons is ideally suited to control the neocortical network across a wide range of frequencies and for sustained periods of time, a behaviour that helps to explain the pivotal role played by layer 5 neurons in the genesis of periods of network 'up' states and epileptiform activity in the neocortex.

Cross-intensity functions and the estimate of spike-time jitter

Correlation measures are important tools for the analysis of simultaneously recorded spike trains. A well-known measure with probabilistic interpretation is the cross-intensity function (CIF), which is an estimate of the conditional probability that a neuron spikes as a function of the time lag to spikes in another neuron. The non-commutative nature of the CIF is particularly useful when different neuron classes are studied that can be distinguished based on their anatomy or physiology. Here we explore the utility of the CIF for estimating spike-time jitter in synaptic interactions between neuron pairs of connected classes. When applied to spike train pairs from sleeping songbirds, we are able to distinguish fast synaptic interactions mediated primarily by AMPA receptors from slower interactions mediated by NMDA receptors. We also find that spike jitter increases with the time lag between spikes, reflecting the accumulation of noise in neural activity sequences, such as in synfire chains. In conclusion, we demonstrate some new utility of the CIF as a spike-train measure.

Mean-driven and fluctuation-driven persistent activity in recurrent networks

Spike trains from cortical neurons show a high degree of irregularity, with coefficients of variation (CV) of their interspike interval (ISI) distribution close to or higher than one. It has been suggested that this irregularity might be a reflection of a particular dynamical state of the local cortical circuit in which excitation and inhibition balance each other. In this "balanced" state, the mean current to the neurons is below threshold, and firing is driven by current fluctuations, resulting in irregular Poisson-like spike trains. Recent data show that the degree of irregularity in neuronal spike trains recorded during the delay period of working memory experiments is the same for both low-activity states of a few Hz and for elevated, persistent activity states of a few tens of Hz. Since the difference between these persistent activity states cannot be due to external factors coming from sensory inputs, this suggests that the underlying network dynamics might support coexisting balanced states at different firing rates. We use mean field techniques to study the possible existence of multiple balanced steady states in recurrent networks of current-based leaky integrate-and-fire (LIF) neurons. To assess the degree of balance of a steady state, we extend existing mean-field theories so that not only the firing rate, but also the coefficient of variation of the interspike interval distribution of the neurons, are determined self-consistently. Depending on the connectivity parameters of the network, we find bistable solutions of different types. If the local recurrent connectivity is mainly excitatory, the two stable steady states differ mainly in the mean current to the neurons. In this case, the mean drive in the elevated persistent activity state is suprathreshold and typically characterized by low spiking irregularity. If the local recurrent excitatory and inhibitory drives are both large and nearly balanced, or even dominated by inhibition, two stable states coexist, both with subthreshold current drive. In this case, the spiking variability in both the resting state and the mnemonic persistent state is large, but the balance condition implies parameter fine-tuning. Since the degree of required fine-tuning increases with network size and, on the other hand, the size of the fluctuations in the afferent current to the cells increases for small networks, overall we find that fluctuation-driven persistent activity in the very simplified type of models we analyze is not a robust phenomenon. Possible implications of considering more realistic models are discussed.

Synaptic activation of dendritic AMPA and NMDA receptors generates transient high-frequency firing in substantia nigra dopamine neurons in vitro

Transient high-frequency activity of substantia nigra dopamine neurons is critical for striatal synaptic plasticity and associative learning. However, the mechanisms underlying this mode of activity are poorly understood because, in contrast to other rapidly firing neurons, high-frequency activity is not evoked by somatic current injection. Previous studies have suggested that activation of dendritic N-methyl-d-aspartate (NMDA) receptors and/or G-protein-coupled receptor (GPCR)-mediated reduction of action potential afterhyperpolarization and/or activation of cation channels underlie high-frequency activity. To address their relative contribution, transient high-frequency activity was evoked using local electrical stimulation (1 s, 10-100 Hz) in brain slices prepared from p15-p25 rats in the presence of GABA and D2 dopamine receptor antagonists. The frequency, pattern, and morphology of action potentials evoked under these conditions were similar to those observed in vivo. Evoked activity and reductions in action potential afterhyperpolarization were diminished greatly by application of alpha-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) or NMDA receptor selective antagonists and abolished completely by co-application of AMPA and NMDA antagonists. In contrast, application of glutamatergic and cholinergic GPCR antagonists moderately enhanced evoked activity. Dendritic pressure-pulse application of glutamate evoked high-frequency activity that was similarly sensitive to antagonism of AMPA or NMDA receptors. Taken together, these data suggest that dendritic AMPA and NMDA receptor-mediated synaptic conductances are sufficient to generate transient high-frequency activity in substantia nigra dopamine neurons by rapidly but transiently overwhelming the conductances underlying action potential afterhyperpolarization and/or engaging postsynaptic voltage-dependent ion channels in a manner that overcomes the limiting effects of afterhyperpolarization.

Dynamic clamp analyses of cardiac, endocrine, and neural function

The dynamic clamp introduces artificial conductances into cells to simulate electrical coupling, votage-dependent, leak, and synaptic conductances. This review describes how the dynamic clamp has been used to address various questions in the cardiac, endocrine, and nervous systems.

Coding with spike shapes and graded potentials in cortical networks

In cortical neurones, analogue dendritic potentials are thought to be encoded into patterns of digital spikes. According to this view, neuronal codes and computations are based on the temporal patterns of spikes: spike times, bursts or spike rates. Recently, we proposed an 'action potential waveform code' for cortical pyramidal neurones in which the spike shape carries information. Broader somatic action potentials are reliably produced in response to higher conductance input, allowing for four times more information transfer than spike times alone. This information is preserved during synaptic integration in a single neurone, as back-propagating action potentials of diverse shapes differentially shunt incoming postsynaptic potentials and so participate in the next round of spike generation. An open question has been whether the information in action potential waveforms can also survive axonal conduction and directly influence synaptic transmission to neighbouring neurones. Several new findings have now brought new light to this subject, showing cortical information processing that transcends the classical models.

Modulation of Cellular and Synaptic Variability in the Lamprey Spinal Cord

Variability is increasingly recognized as a characteristic feature of cellular, synaptic, and network properties. While studies have traditionally focused on mean values, significant effects can result from changes in variance. This study has examined cellular and synaptic variability in the lamprey spinal cord and its modulation by the neuropeptide substance P. Cellular and synaptic variability differed in different types of cell and synapse. Substance P reduced the variability of subthreshold locomotor-related depolarizations and spiking in motor neurons during network activity. These effects were associated with a reduction in the variability of spiking in glutamatergic excitatory network interneurons and with a reduction in the variance of excitatory interneuron-evoked excitatory postsynaptic potentials (EPSPs). Substance P also reduced the variance of postsynpatic potentials (PSPs) from crossing inhibitory and excitatory interneurons, but it increased the variance of inhibitory postsynpatic potentials (IPSPs) from ipsilateral inhibitory interneurons. The effects on the variance of different PSPs could occur with or without changes in the PSP amplitude. The reduction in the variance of excitatory interneuron-evoked EPSPs was protein kinase A, calcium, and N-methyl-d-aspartate (NMDA) dependent. The NMDA dependence suggested that substance P was acting postsynaptically. This was supported by the reduced variability of postsynaptic responses to glutamate by substance P. However, ultrastructural analyses suggested that there may also be a presynaptic component to the modulation, because substance P reduced the variability of synaptic vesicle diameters in putative glutamatergic terminals. These results suggest that cellular and synaptic variability can be targeted for modulation, making it an additional source of spinal cord plasticity.

Correlation entropy of synaptic input-output dynamics

The responses of synapses in the neocortex show highly stochastic and nonlinear behavior. The microscopic dynamics underlying this behavior, and its computational consequences during natural patterns of synaptic input, are not explained by conventional macroscopic models of deterministic ensemble mean dynamics. Here, we introduce the correlation entropy of the synaptic input-output map as a measure of synaptic reliability which explicitly includes the microscopic dynamics. Applying this to experimental data, we find that cortical synapses show a low-dimensional chaos driven by the natural input pattern.

Rate coding and spike-time variability in cortical neurons with two types of threshold dynamics

Neurons and dynamical models of spike generation display two different classes of threshold behavior: type 1 [firing frequency vs. current (f-I) relationship is continuous at threshold] and type 2 (discontinuous f-I). With steady current or conductance stimulation, regular-spiking (RS) pyramidal neurons and fast-spiking (FS) inhibitory interneurons in layer 2/3 of somatosensory cortex exhibit type 1 and type 2 threshold behaviors, respectively. We compared the postsynaptic firing variability of type 1 RS and type 2 FS cells, during naturalistic, fluctuating conductance input. In RS neurons, increasing the level of independently random, shunting inhibition caused a monotonic increase in spike reliability, whereas in FS interneurons, there was an optimum level of shunting inhibition for achieving the most reliable spike generation and the most precise spike-time encoding. This was observed over a range of different degrees of synchrony, or correlation, in the input. RS cells displayed a progressive rise in spike jitter during natural-like transient burst inputs, whereas for FS cells, jitter was mostly kept low. Furthermore, RS cells showed encoding of the input level in the spike shape, whereas FS cells did not. These differences between the two cell types are consistent with a role of RS neurons as rate-coding integrators, and a role of FS neurons as resonators controlling the coherence of synchronous firing.

Dynamic gain changes during attentional modulation

Attention causes a multiplicative effect on firing rates of cortical neurons without affecting their selectivity (Motter, 1993; McAdams & Maunsell, 1999a) or the relationship between the spike count mean and variance (McAdams & Maunsell, 1999b). We analyzed attentional modulation of the firing rates of 144 neurons in the secondary somatosensory cortex (SII) of two monkeys trained to switch their attention between a tactile pattern recognition task and a visual task. We found that neurons in SII cortex also undergo a predominantly multiplicative modulation in firing rates without affecting the ratio of variance to mean firing rate (i.e., the Fano factor). Furthermore, both additive and multiplicative components of attentional modulation varied dynamically during the stimulus presentation. We then used a standard conductance-based integrate-and-fire model neuron to ascertain which mechanisms might account for a multiplicative increase in firing rate without affecting the Fano factor. Six mechanisms were identified as biophysically plausible ways that attention could modify the firing rate: spike threshold, firing rate adaptation, excitatory input synchrony, synchrony between all inputs, membrane leak resistance, and reset potential. Of these, only a change in spike threshold or in firing rate adaptation affected model firing rates in a manner compatible with the observed neural data. The results indicate that only a limited number of biophysical mechanisms can account for observed attentional modulation.

StdpC: a modern dynamic clamp

With the advancement of computer technology many novel uses of dynamic clamp have become possible. We have added new features to our dynamic clamp software StdpC ("Spike timing-dependent plasticity Clamp") allowing such new applications while conserving the ease of use and installation of the popular earlier Dynclamp 2/4 package. Here, we introduce the new features of a waveform generator, freely programmable Hodgkin-Huxley conductances, learning synapses, graphic data displays, and a powerful scripting mechanism and discuss examples of experiments using these features. In the first example we built and 'voltage clamped' a conductance based model cell from a passive resistor-capacitor (RC) circuit using the dynamic clamp software to generate the voltage-dependent currents. In the second example we coupled our new spike generator through a burst detection/burst generation mechanism in a phase-dependent way to a neuron in a central pattern generator and dissected the subtle interaction between neurons, which seems to implement an information transfer through intraburst spike patterns. In the third example, making use of the new plasticity mechanism for simulated synapses, we analyzed the effect of spike timing-dependent plasticity (STDP) on synchronization revealing considerable enhancement of the entrainment of a post-synaptic neuron by a periodic spike train. These examples illustrate that with modern dynamic clamp software like StdpC, the dynamic clamp has developed beyond the mere introduction of artificial synapses or ionic conductances into neurons to a universal research tool, which might well become a standard instrument of modern electrophysiology.

Fundamental differences in spontaneous synaptic inhibition between deep and superficial layers of the rat entorhinal cortex

We have previously shown that there are clear differences between spontaneous excitatory synaptic currents recorded in layers V and II of the rat entorhinal cortex (EC) in vitro, and have suggested that these might contribute to a more pronounced susceptibility of the deeper layer to epileptogenesis. In the present study, we have made a detailed comparison of spontaneous synaptic inhibition between the two layers by recording spontaneous inhibitory synaptic currents (sIPSCs) using whole-cell patch-clamp techniques in EC slices. Pharmacological studies indicated that sIPSCs were mediated exclusively by gamma-aminobutyric acid (GABA)(A) receptors. There was little difference in average amplitudes, rise or decay times of sIPSCs in layer II compared with layer V. However, in the former, events occurred at 4-5 times the frequency seen in the latter, and frequencies of </=40 Hz were not uncommon. When activity-independent, miniature IPSCs were isolated in tetrodotoxin (TTX), the frequency in layer V was more than halved, but in layer II only a small reduction was seen, and the frequency remained very high. In terms of kinetics, while averaged sIPSCs in each layer were very similar, detailed comparison of individual sIPSCs within layers revealed distinct differences, possibly reflecting inputs from different subtypes of interneurons or inputs at different somatodendritic locations. In layer V, sIPSCs could be divided into three groups, one with slow rise and decay kinetics and a second with fast rise kinetics, further distinguished into two groups by either fast or slow decay kinetics. The distinction between events in layer II was simpler, one group having both fast rise and decay times and the second with both parameters much slower. Finally, IPSCs could occur in high-frequency bursts in both layers, although these were much more prevalent in layer II. The results are discussed in terms of the overall level of background inhibition in the two layers, as well as how this might relate to their susceptibilities to epileptogenesis.

Phase-response curves give the responses of neurons to transient inputs

Neuronal firing is determined largely by incoming barrages of excitatory postsynaptic potentials (EPSPs), each of which produce a transient increase in firing probability. To measure the effects of weak transient inputs on firing probability of cortical neurons, we compute phase-response curves (PRCs). PRCs, whose shape can be related to the dynamics of spike generation, document the changes in timing of spikes caused by an EPSP in a repetitively firing neuron as a function of when it arrives in the interspike interval (ISI). The PRC can be exactly related to the poststimulus time histogram (PSTH) so that knowledge of one uniquely determines the other. Typically, PRCs have zero values at the start and end of the ISI, where EPSPs have minimal effects and a peak in the middle. Where the peak occurs depends in part on the firing properties of neurons. The PRC can have regions of positivity and negativity corresponding respectively to speeding up and slowing down the time of the next spike. A simple canonical model for spike generation is introduced that shows how both the background firing rate and the degree of postspike afterhyperpolarization contribute to the shape of the PRC and thus to the PSTH. PRCs in strongly adapting neurons are highly skewed to the right (indicating a higher change in probability when the EPSPs appear late in the ISI) and can have negative regions (indicating a decrease in firing probability) early in the ISI. The PRC becomes more skewed to the right as the firing rate decreases. Thus at low firing rates, the spikes are triggered preferentially by inputs that occur only during a small time interval late in the ISI. This implies that the neuron is more of a coincidence detector at low firing frequencies and more of an integrator at high frequencies. The steady-state theory is shown to also hold for slowly varying inputs.

Background synaptic conductance and precision of EPSP-spike coupling at pyramidal cells

The temporal precision of converting excitatory postsynaptic potentials (EPSPs) into spikes at pyramidal cells is critical for the coding of information in the cortex. Several in vitro studies have shown that voltage-dependent conductances in pyramidal cells can prolong the EPSP time course resulting in an imprecise EPSP-spike coupling. We have used dynamic-clamp techniques to mimic the in vivo background synaptic conductance in cortical slices and investigated how the ongoing synaptic activity may affect the EPSP time course near threshold and the EPSP spike coupling. We report here that background synaptic conductance dramatically diminished the depolarization related prolongation of the EPSPs in pyramidal cells and improved the precision of spike timing. Furthermore, we found that background synaptic conductance can affect the interaction among action potentials in a spike train. Thus the level of ongoing synaptic activity in the cortex may regulate the capacity of pyramidal cells to process temporal information.

Dynamics of excitatory synaptic components in sustained firing at low rates

Sustained firing is necessary for the persistent activity associated with working memory. The relative contributions of the reverberation of excitation and of the temporal dynamics of the excitatory postsynaptic potential (EPSP) to the maintenance of activity are difficult to evaluate in classical preparations. We used simplified models of synchronous excitatory networks, hippocampal autapses and pairs, to study the synaptic mechanisms underlying firing at low rates. Calcium imaging and cell attached recordings showed that these neurons spontaneously fired bursts of action potentials that lasted for seconds over a wide range of frequencies. In 2-wk-old cells, the median firing frequency was low (11 +/- 8.8 Hz), whereas in 3- to 4-wk-old cells, it decreased to a very low value (2 +/- 1.3 Hz). In both cases, we have shown that the slowest synaptic component supported firing. In 2-wk-old autapses, antagonists of N-methyl-d-aspartate receptors (NMDARs) induced rare isolated spikes showing that the NMDA component of the EPSP was essential for bursts at low frequency. In 3- to 4-wk-old neurons, the very low frequency firing was maintained without the NMDAR activation. However EGTA-AM or alpha-methyl-4-carboxyphenylglycine (MCPG) removed the very slow depolarizing component of the EPSP and prevented the sustained firing at very low rate. A metabotropic glutamate receptor (mGluR)-activated calcium sensitive conductance is therefore responsible for a very slow synaptic component associated with firing at very low rate. In addition, our observations suggested that the asynchronous release of glutamate might participate also in the recurring bursting.

Spatio-temporal cholinergic modulation in cultured networks of rat cortical neurons: Evoked activity

We studied the effects of carbachol, a cholinergic agonist, on extracellularly evoked firing of networks in mature cultures of rat cortical neurons, using multi-electrode arrays to monitor the activity of large numbers of neurons simultaneously. These cultures show evoked burst firing which propagates through dense synaptic connections. When a brief voltage pulse was applied to one extracellular electrode, spiking electrical responses were evoked in neurons throughout the network. The response had two components: an early phase, terminating within 30-80 ms, and a late phase which could last several hundreds of milliseconds. Action potentials evoked during the early phase were precisely timed, with only small jitter. In contrast, the late phase characteristically showed clusters of electrical activity with significant spatio-temporal fluctuations. The late phase was suppressed by applying a relatively small amount of carbachol (5 microM) in the external solution, even though the spontaneous firing rate was not significantly changed. Carbachol increased both the spike-timing precision and the speed of propagation of population spikes, and selectively increased the firing coincidence in a subset of neuron pairs in the network, while suppressing late variable firing in responses. Hence, the results give quantitative support for the idea that cholinergic activation in the cortex has a general role of focusing or enhancing significant associative firing of neurons.

Background synaptic activity in rat entorhinal cortical neurones: differential control of transmitter release by presynaptic receptors

The entorhinal cortex (EC) is a key brain area controlling both hippocampal input and output via neurones in layer II and layer V, respectively. It is also a pivotal area in the generation and propagation of epilepsies involving the temporal lobe. We have previously shown that within the network of the EC, neurones in layer V are subject to powerful synaptic excitation but weak inhibition, whereas the reverse is true in layer II. The deep layers are also highly susceptible to acutely provoked epileptogenesis. Considerable evidence now points to a role of spontaneous background synaptic activity in control of neuronal, and hence network, excitability. In the present article we describe results of studies where we have compared background release of the excitatory transmitter, glutamate, and the inhibitory transmitter, GABA, in the two layers, the role of this background release in the balance of excitability, and its control by presynaptic auto- and heteroreceptors on presynaptic terminals.

Reliability and precision of neural spike timing: simulation of spectrally broadband synaptic inputs

Spectrally broadband stimulation of neurons has been an effective method for studying their dynamic responses to simulated synaptic inputs. Previous studies with such stimulation were mostly based upon the direct intracellular injection of noisy current waveforms. In the present study we analyze and compare the firing output of various identified molluscan neurons to aperiodic, broadband current signals using three types of stimulus paradigms: 1. direct injection in current clamp mode, 2. conductance injection using electrotonic coupling of the input waveform to the neuron, and 3. conductance injection using a simulated chemical excitatory connection. The current waveforms were presented in 15 successive trials and the trial-to-trial variations of the spike responses were analyzed using peri-stimulus spike density functions. Comparing the responses of the neurons to the same type of input waveforms, we found that conductance injection resulted in more reliable and precise spike responses than direct current injection. The statistical parameters of the response spike trains depended on the spectral distribution of the input. The reliability increased with increasing cutoff frequency, while the temporal jitter of spikes changed in the opposite direction. Neurons with endogenous bursting displayed lower reproducibility in their responses to noisy waveforms when injected directly; however, they fired far more reliably and precisely when receiving the same waveforms as conductance inputs. The results show that molluscan neurons are capable of accurately reproducing their responses to synaptic inputs. Conductance injection provides an enhanced experimental technique for assessing the neurons' spike timing reliability and it should be preferred over direct current injection of noisy waveforms.