Functional organization and structure of the serotonergic neuronal network of terrestrial snail

The extension of knowledge how the brain works requires permanent improvement of methods of recording of neuronal activity and increase in the number of neurons recorded simultaneously to better understand the collective work of neuronal networks and assemblies. Conventional methods allow simultaneous intracellular recording up to 2-5 neurons and their membrane potentials, currents or monosynaptic connections or observation of spiking of neuronal groups with subsequent discrimination of individual spikes with loss of details of the dynamics of membrane potential. We recorded activity of a compact group of serotonergic neurons (up to 56 simultaneously) in the ganglion of a terrestrial mollusk using the method of optical recording of membrane potential that allowed to record individual action potentials in details with action potential parameters and to reveal morphology of the neurons rcorded. We demonstrated clear clustering in the group in relation with the dynamics of action potentials and phasic or tonic components in the neuronal responses to external electrophysiological and tactile stimuli. Also, we showed that identified neuron Pd2 could induce activation of a significant number of neurons in the group whereas neuron Pd4 did not induce any activation. However, its activation is delayed with regard to activation of the reacting group of neurons. Our data strongly support the concept of possible delegation of the integrative function by the network to a single neuron.     

Predicting synchronous firing of large neural populations from sequential recordings

A major goal in neuroscience is to understand how populations of neurons code for stimuli or actions. While the number of neurons that can be recorded simultaneously is increasing at a fast pace, in most cases these recordings cannot access a complete population: some neurons that carry relevant information remain unrecorded. In particular, it is hard to simultaneously record all the neurons of the same type in a given area. Recent progress have made possible to profile each recorded neuron in a given area thanks to genetic and physiological tools, and to pool together recordings from neurons of the same type across different experimental sessions. However, it is unclear how to infer the activity of a full population of neurons of the same type from these sequential recordings. Neural networks exhibit collective behaviour, e.g. noise correlations and synchronous activity, that are not directly captured by a conditionally-independent model that would just put together the spike trains from sequential recordings. Here we show that we can infer the activity of a full population of retina ganglion cells from sequential recordings, using a novel method based on copula distributions and maximum entropy modeling. From just the spiking response of each ganglion cell to a repeated stimulus, and a few pairwise recordings, we could predict the noise correlations using copulas, and then the full activity of a large population of ganglion cells of the same type using maximum entropy modeling. Remarkably, we could generalize to predict the population responses to different stimuli with similar light conditions and even to different experiments. We could therefore use our method to construct a very large population merging cells’ responses from different experiments. We predicted that synchronous activity in ganglion cell populations saturates only for patches larger than 1.5mm in radius, beyond what is today experimentally accessible.     

Recording Large-scale Neuronal Ensembles with Silicon Probes in the Anesthetized Rat

Large scale electrophysiological recordings from neuronal ensembles offer the opportunity to investigate how the brain orchestrates the wide variety of behaviors from the spiking activity of its neurons. One of the most effective methods to monitor spiking activity from a large number of neurons in multiple local neuronal circuits simultaneously is by using silicon electrode arrays^1-3.Action potentials produce large transmembrane voltage changes in the vicinity of cell somata. These output signals can be measured by placing a conductor in close proximity of a neuron. If there are many active (spiking) neurons in the vicinity of the tip, the electrode records combined signal from all of them, where contribution of a single neuron is weighted by its ''electrical distance''. Silicon probes are ideal recording electrodes to monitor multiple neurons because of a large number of recording sites (+64) and a small volume. Furthermore, multiple sites can be arranged over a distance of millimeters, thus allowing for the simultaneous recordings of neuronal activity in the various cortical layers or in multiple cortical columns (Fig. 1). Importantly, the geometrically precise distribution of the recording sites also allows for the determination of the spatial relationship of the isolated single neurons⁴. Here, we describe an acute, large-scale neuronal recording from the left and right forelimb somatosensory cortex simultaneously in an anesthetized rat with silicon probes (Fig. 2).     

ASSET: Analysis of Sequences of Synchronous Events in Massively Parallel Spike Trains

With the ability to observe the activity from large numbers of neurons simultaneously using modern recording technologies, the chance to identify sub-networks involved in coordinated processing increases. Sequences of synchronous spike events (SSEs) constitute one type of such coordinated spiking that propagates activity in a temporally precise manner. The synfire chain was proposed as one potential model for such network processing. Previous work introduced a method for visualization of SSEs in massively parallel spike trains, based on an intersection matrix that contains in each entry the degree of overlap of active neurons in two corresponding time bins. Repeated SSEs are reflected in the matrix as diagonal structures of high overlap values. The method as such, however, leaves the task of identifying these diagonal structures to visual inspection rather than to a quantitative analysis. Here we present ASSET (Analysis of Sequences of Synchronous EvenTs), an improved, fully automated method which determines diagonal structures in the intersection matrix by a robust mathematical procedure. The method consists of a sequence of steps that i) assess which entries in the matrix potentially belong to a diagonal structure, ii) cluster these entries into individual diagonal structures and iii) determine the neurons composing the associated SSEs. We employ parallel point processes generated by stochastic simulations as test data to demonstrate the performance of the method under a wide range of realistic scenarios, including different types of non-stationarity of the spiking activity and different correlation structures. Finally, the ability of the method to discover SSEs is demonstrated on complex data from large network simulations with embedded synfire chains. Thus, ASSET represents an effective and efficient tool to analyze massively parallel spike data for temporal sequences of synchronous activity.     

A Unified Approach to Linking Experimental,Statistical and Computational Analysis of Spike Train Data

A fundamental issue in neuroscience is how to identify the multiple biophysical mechanisms through which neurons generate observed patterns of spiking activity. In previous work, we proposed a method for linking observed patterns of spiking activity to specific biophysical mechanisms based on a state space modeling framework and a sequential Monte Carlo, or particle filter, estimation algorithm. We have shown, in simulation, that this approach is able to identify a space of simple biophysical models that were consistent with observed spiking data (and included the model that generated the data), but have yet to demonstrate the application of the method to identify realistic currents from real spike train data. Here, we apply the particle filter to spiking data recorded from rat layer V cortical neurons, and correctly identify the dynamics of an slow, intrinsic current. The underlying intrinsic current is successfully identified in four distinct neurons, even though the cells exhibit two distinct classes of spiking activity: regular spiking and bursting. This approach – linking statistical, computational, and experimental neuroscience – provides an effective technique to constrain detailed biophysical models to specific mechanisms consistent with observed spike train data.     

A Granger causality measure for point process models of ensemble neural spiking activity

The ability to identify directional interactions that occur among multiple neurons in the brain is crucial to an understanding of how groups of neurons cooperate in order to generate specific brain functions. However, an optimal method of assessing these interactions has not been established. Granger causality has proven to be an effective method for the analysis of the directional interactions between multiple sets of continuous-valued data, but cannot be applied to neural spike train recordings due to their discrete nature. This paper proposes a point process framework that enables Granger causality to be applied to point process data such as neural spike trains. The proposed framework uses the point process likelihood function to relate a neuron's spiking probability to possible covariates, such as its own spiking history and the concurrent activity of simultaneously recorded neurons. Granger causality is assessed based on the relative reduction of the point process likelihood of one neuron obtained excluding one of its covariates compared to the likelihood obtained using all of its covariates. The method was tested on simulated data, and then applied to neural activity recorded from the primary motor cortex (MI) of a Felis catus subject. The interactions present in the simulated data were predicted with a high degree of accuracy, and when applied to the real neural data, the proposed method identified causal relationships between many of the recorded neurons. This paper proposes a novel method that successfully applies Granger causality to point process data, and has the potential to provide unique physiological insights when applied to neural spike trains.     

Detecting action potentials in neuronal populations with calcium imaging.

The study of neural circuits requires methods for simultaneously recording the activity of populations of neurons. Here, using calcium imaging of neocortical brain slices we take advantage of the ubiquitous distribution of calcium channels in neurons to develop a method to reconstruct the action potentials occurring in a population of neurons. Combining calcium imaging with whole-cell or perforated patch recordings from neurons loaded with acetoxymethyl ester or potassium salt forms of calcium indicators, we demonstrate that each action potential produces a stereotyped calcium transient in the somata of pyramidal neurons. These signals are detectable without averaging, and the signal-to-noise is sufficient to carry out a reconstruction of the spiking pattern of hundreds of neurons, up to relatively high firing frequencies. This technique could in principle be applied systematically to follow the activity of neuronal populations in vitro and in vivo.     

Micro-drive Array for Chronic in vivo Recording: Tetrode Assembly

The tetrode, a bundle of four electrodes, has proven to be a valuable tool for the simultaneous recording of multiple neurons in-vivo. The differential amplitude of action potential signatures over the channels of a tetrode allows for the isolation of single-unit activity from multi-unit signals. The ability to precisely control the stereotaxic location and depth of the tetrode is critical for studying coordinated neural activity across brain regions. In combination with a micro-drive array, it is possible to achieve precise placement and stable control of many tetrodes over the course of days to weeks. In this protocol, we demonstrate how to fabricate and condition tetrodes using basic tools and materials, install the tetrodes into a multi-drive tetrode array for chronic in-vivo recording in the rat, make ground wire connections to the micro-drive array, and attach a protective cone onto the micro-drive array in order to protect the tetrodes from physical contact with the environment. Open in a separate windowClick here to view.^{(90M, flv)}     

Role of Acetylcholine in the Modulation of Activity of Neocortical Neurons in Awake Animals Performing an Instrumental Conditioned Reflex

We studied modulatory effects of the cholinergic system on the activity of sensorimotor cortex neurons related to realization of an instrumental conditioned placing reflex. Experiments were carried out on awake cats; multibarrel glass microelectrodes were used for extracellular recording of impulse activity of neurons in the sensorimotor cortex and iontophoretic application of synaptically active agents within the recording region. The background and reflex-related activity was recorded in the course of realization of conditioned movements, and then changes of spiking induced by applications of the testing substances were examined. Applications of acetylcholine and carbachol resulted in increases in the intensity of impulse reactions of neocortical neurons evoked by presentation of an acoustic signal and in simultaneous shortening of the response latencies. An agonist of muscarinic receptors, pylocarpine, exerted a similar effect on the evoked activity of sensorimotor cortex neurons. Blockers of muscarinic receptors, atropine and scopolamine, vice versa, sharply suppressed impulse reactions of cortical neurons to afferent stimulation and simultaneously increased latencies of these responses. Applications of an agonist of nicotinic receptors, nicotine, was accompanied by suppression of impulse neuronal responses, an increase in the latency of spike reactions to presentation of a sound signal, and a corresponding increase in the latency of a conditioned motor reaction. In contrast, application of an antagonist of nicotinic receptors, tubocurarine, significantly intensified neuronal spike responses and shortened their latency. The mechanisms underlying the effects of antagonists of membrane muscarinic and nicotinic cholinoreceptors and the role of activation of these receptors in the modulation of activity of pyramidal and non-pyramidal neocortical neurons related to realization of the instrumental motor reflex are discussed.     

State-space analysis of time-varying higher-order spike correlation for multiple neural spike train data

Precise spike coordination between the spiking activities of multiple neurons is suggested as an indication of coordinated network activity in active cell assemblies. Spike correlation analysis aims to identify such cooperative network activity by detecting excess spike synchrony in simultaneously recorded multiple neural spike sequences. Cooperative activity is expected to organize dynamically during behavior and cognition; therefore currently available analysis techniques must be extended to enable the estimation of multiple time-varying spike interactions between neurons simultaneously. In particular, new methods must take advantage of the simultaneous observations of multiple neurons by addressing their higher-order dependencies, which cannot be revealed by pairwise analyses alone. In this paper, we develop a method for estimating time-varying spike interactions by means of a state-space analysis. Discretized parallel spike sequences are modeled as multi-variate binary processes using a log-linear model that provides a well-defined measure of higher-order spike correlation in an information geometry framework. We construct a recursive Bayesian filter/smoother for the extraction of spike interaction parameters. This method can simultaneously estimate the dynamic pairwise spike interactions of multiple single neurons, thereby extending the Ising/spin-glass model analysis of multiple neural spike train data to a nonstationary analysis. Furthermore, the method can estimate dynamic higher-order spike interactions. To validate the inclusion of the higher-order terms in the model, we construct an approximation method to assess the goodness-of-fit to spike data. In addition, we formulate a test method for the presence of higher-order spike correlation even in nonstationary spike data, e.g., data from awake behaving animals. The utility of the proposed methods is tested using simulated spike data with known underlying correlation dynamics. Finally, we apply the methods to neural spike data simultaneously recorded from the motor cortex of an awake monkey and demonstrate that the higher-order spike correlation organizes dynamically in relation to a behavioral demand.     

Dissecting cascade computational components in spiking neural networks

Finding out the physical structure of neuronal circuits that governs neuronal responses is an important goal for brain research. With fast advances for large-scale recording techniques, identification of a neuronal circuit with multiple neurons and stages or layers becomes possible and highly demanding. Although methods for mapping the connection structure of circuits have been greatly developed in recent years, they are mostly limited to simple scenarios of a few neurons in a pairwise fashion; and dissecting dynamical circuits, particularly mapping out a complete functional circuit that converges to a single neuron, is still a challenging question. Here, we show that a recent method, termed spike-triggered non-negative matrix factorization (STNMF), can address these issues. By simulating different scenarios of spiking neural networks with various connections between neurons and stages, we demonstrate that STNMF is a persuasive method to dissect functional connections within a circuit. Using spiking activities recorded at neurons of the output layer, STNMF can obtain a complete circuit consisting of all cascade computational components of presynaptic neurons, as well as their spiking activities. For simulated simple and complex cells of the primary visual cortex, STNMF allows us to dissect the pathway of visual computation. Taken together, these results suggest that STNMF could provide a useful approach for investigating neuronal systems leveraging recorded functional neuronal activity.     

Electrophysiological Properties of Motor Neurons in a Mouse Model of Severe Spinal Muscular Atrophy: In Vitro versus In Vivo Development

We examined the electrophysiological activity of motor neurons from the mouse model of severe spinal muscular atrophy (SMA) using two different methods: whole cell patch clamp of neurons cultured from day 13 embryos; and multi-electrode recording of ventral horns in spinal cord slices from pups on post-natal days 5 and 6. We used the MED64 multi-electrode array to record electrophysiological activity from motor neurons in slices from the lumbar spinal cord of SMA pups and their unaffected littermates. Recording simultaneously from up to 32 sites across the ventral horn, we observed a significant decrease in the number of active neurons in 5–6 day-old SMA pups compared to littermates. Ventral horn activity in control pups is significantly activated by serotonin and depressed by GABA, while these agents had much less effect on SMA slices. In contrast to the large differences observed in spinal cord, neurons cultured from SMA embryos for up to 21 days showed no significant differences in electrophysiological activity compared to littermates. No differences were observed in membrane potential, frequency of spiking and synaptic activity in cells from SMA embryos compared to controls. In addition, we observed no difference in cell survival between cells from SMA embryos and their unaffected littermates. Our results represent the first report on the electrophysiology of SMN-deficient motor neurons, and suggest that motor neuron development in vitro follows a different path than in vivo development, a path in which loss of SMN expression has little effect on motor neuron function and survival.     

Stress-Induced Impairment of a Working Memory Task: Role of Spiking Rate and Spiking History Predicted Discharge

Stress, pervasive in society, contributes to over half of all work place accidents a year and over time can contribute to a variety of psychiatric disorders including depression, schizophrenia, and post-traumatic stress disorder. Stress impairs higher cognitive processes, dependent on the prefrontal cortex (PFC) and that involve maintenance and integration of information over extended periods, including working memory and attention. Substantial evidence has demonstrated a relationship between patterns of PFC neuron spiking activity (action-potential discharge) and components of delayed-response tasks used to probe PFC-dependent cognitive function in rats and monkeys. During delay periods of these tasks, persistent spiking activity is posited to be essential for the maintenance of information for working memory and attention. However, the degree to which stress-induced impairment in PFC-dependent cognition involves changes in task-related spiking rates or the ability for PFC neurons to retain information over time remains unknown. In the current study, spiking activity was recorded from the medial PFC of rats performing a delayed-response task of working memory during acute noise stress (93 db). Spike history-predicted discharge (SHPD) for PFC neurons was quantified as a measure of the degree to which ongoing neuronal discharge can be predicted by past spiking activity and reflects the degree to which past information is retained by these neurons over time. We found that PFC neuron discharge is predicted by their past spiking patterns for nearly one second. Acute stress impaired SHPD, selectively during delay intervals of the task, and simultaneously impaired task performance. Despite the reduction in delay-related SHPD, stress increased delay-related spiking rates. These findings suggest that neural codes utilizing SHPD within PFC networks likely reflects an additional important neurophysiological mechanism for maintenance of past information over time. Stress-related impairment of this mechanism is posited to contribute to the cognition-impairing actions of stress.     

Optical recording of the electrical activity of synaptically interacting Aplysia neurons in culture using potentiometric probes.

We used multiple-site optical recording methods, in conjunction with impermeant molecular probes of the cell membrane potential, to record the electrical activity of model neural circuits in vitro. Our system consisted of co-cultured pairs of left upper quadrant neurons from the abdominal ganglion of the marine gastropod Aplysia. These neurons interact via inhibitory synapses in vitro. Photodynamic damage to the neurons was essentially eliminated over the time course of the measurements, approximately less than 30 s, by removing oxygen from the recording solution and replacing it with argon. This procedure did not affect the synaptic interactions. We observed repetitive spiking activity in single-trace optical recordings with a maximum signal-to-noise ratio per detector of approximately 50. Individual optical signals that corresponded to either the activity of the presynaptic neuron or that of the postsynaptic neuron were clearly identified. This allowed us to monitor the activity of synaptically interacting neurons, observed as a reduction of the firing rate of the postsynaptic cell after activity of the presynaptic cell. Our results demonstrate that optical methods are appropriate for recording prolonged, asynchronous activity from synaptically interacting neurons in culture.     

Spontaneous spiking and synaptic depression underlie noradrenergic control of feed-forward inhibition

Inhibitory interneurons across diverse brain regions commonly exhibit spontaneous spiking activity, even in the absence of external stimuli. It is not well understood how stimulus-evoked inhibition can be distinguished from background inhibition arising from spontaneous firing. We found that noradrenaline simultaneously reduced spontaneous inhibitory inputs and enhanced evoked inhibitory currents recorded from principal neurons of the mouse dorsal cochlear nucleus (DCN). Together, these effects produced a large increase in signal-to-noise ratio for stimulus-evoked inhibition. Surprisingly, the opposing effects on background and evoked currents could both be attributed to noradrenergic silencing of spontaneous spiking in glycinergic interneurons. During spontaneous firing, glycine release was decreased due to strong short-term depression. Elimination of background spiking relieved inhibitory synapses from depression and thereby enhanced stimulus-evoked inhibition. Our findings illustrate a simple yet powerful neuromodulatory mechanism to shift the balance between background and stimulus-evoked signals.     

Fast inference of interactions in assemblies of stochastic integrate-and-fire neurons from spike recordings

We present two Bayesian procedures to infer the interactions and external currents in an assembly of stochastic integrate-and-fire neurons from the recording of their spiking activity. The first procedure is based on the exact calculation of the most likely time courses of the neuron membrane potentials conditioned by the recorded spikes, and is exact for a vanishing noise variance and for an instantaneous synaptic integration. The second procedure takes into account the presence of fluctuations around the most likely time courses of the potentials, and can deal with moderate noise levels. The running time of both procedures is proportional to the number S of spikes multiplied by the squared number N of neurons. The algorithms are validated on synthetic data generated by networks with known couplings and currents. We also reanalyze previously published recordings of the activity of the salamander retina (including from 32 to 40 neurons, and from 65,000 to 170,000 spikes). We study the dependence of the inferred interactions on the membrane leaking time; the differences and similarities with the classical cross-correlation analysis are discussed.     

Dynamical changes and temporal precision of synchronized spiking activity in monkey motor cortex during movement preparation.

Movement preparation is considered to be based on central processes which are responsible for improving motor performance. For instance, it has been shown that motor cortical neurones change their activity selectively in relation to prior information about movement parameters. However, it is not clear how groups of neurones dynamically organize their activity to cope with computational demands. The aim of the study was to compare the firing rate of multiple simultaneously recorded neurones with the interaction between them by describing not only the frequency of occurrence of epochs of significant synchronization, but also its modulation in time and its changes in temporal precision during an instructed delay. Multiple single-neurone activity was thus recorded in monkey motor cortex during the performance of two different delayed multi-directional pointing tasks. In order to detect conspicuous spike coincidences in simultaneously recorded spike trains by tolerating temporal jitter ranging from 0 to 20 ms and to calculate their statistical significance, a modified method of the 'Unitary Events' analysis was used. Two main results were obtained. First, simultaneously recorded neurones synchronize their spiking activity in a highly dynamic way. Synchronization becomes significant only during short periods (about 100 to 200 ms). Several such periods occurred during a behavioural trial more or less regularly. Second, in many pairs of neurones, the temporal precision of synchronous activity was highest at the end of the preparatory period. As a matter of fact, at the beginning of this period, after the presentation of the preparatory signal, neurones significantly synchronize their spiking activity, but with low temporal precision. As time advances, significant synchronization becomes more precise. Data indicate that not only the discharge rate is involved in preparatory processes, but also temporal aspects of neuronal activity as expressed in the precise synchronization of individual action potentials.     

Spatiotemporal Spike Coding of Behavioral Adaptation in the Dorsal Anterior Cingulate Cortex

The frontal cortex controls behavioral adaptation in environments governed by complex rules. Many studies have established the relevance of firing rate modulation after informative events signaling whether and how to update the behavioral policy. However, whether the spatiotemporal features of these neuronal activities contribute to encoding imminent behavioral updates remains unclear. We investigated this issue in the dorsal anterior cingulate cortex (dACC) of monkeys while they adapted their behavior based on their memory of feedback from past choices. We analyzed spike trains of both single units and pairs of simultaneously recorded neurons using an algorithm that emulates different biologically plausible decoding circuits. This method permits the assessment of the performance of both spike-count and spike-timing sensitive decoders. In response to the feedback, single neurons emitted stereotypical spike trains whose temporal structure identified informative events with higher accuracy than mere spike count. The optimal decoding time scale was in the range of 70–200 ms, which is significantly shorter than the memory time scale required by the behavioral task. Importantly, the temporal spiking patterns of single units were predictive of the monkeys’ behavioral response time. Furthermore, some features of these spiking patterns often varied between jointly recorded neurons. All together, our results suggest that dACC drives behavioral adaptation through complex spatiotemporal spike coding. They also indicate that downstream networks, which decode dACC feedback signals, are unlikely to act as mere neural integrators.     

Analyzing multiple spike trains with nonparametric granger causality

Simultaneous recordings of spike trains from multiple single neurons are becoming commonplace. Understanding the interaction patterns among these spike trains remains a key research area. A question of interest is the evaluation of information flow between neurons through the analysis of whether one spike train exerts causal influence on another. For continuous-valued time series data, Granger causality has proven an effective method for this purpose. However, the basis for Granger causality estimation is autoregressive data modeling, which is not directly applicable to spike trains. Various filtering options distort the properties of spike trains as point processes. Here we propose a new nonparametric approach to estimate Granger causality directly from the Fourier transforms of spike train data. We validate the method on synthetic spike trains generated by model networks of neurons with known connectivity patterns and then apply it to neurons simultaneously recorded from the thalamus and the primary somatosensory cortex of a squirrel monkey undergoing tactile stimulation.