Multi-neuron Recordings Research Articles

Information in small neuronal ensemble activity in the hippocampal CA1 during delayed non-matching to sample performance in rats

BackgroundThe matrix-like organization of the hippocampus, with its several inputs and outputs, has given rise to several theories related to hippocampal information processing. Single-cell electrophysiological studies and studies of lesions or genetically altered animals using recognition memory tasks such as delayed non-matching-to-sample (DNMS) tasks support the theories. However, a complete understanding of hippocampal function necessitates knowledge of the encoding of information by multiple neurons in a single trial. The role of neuronal ensembles in the hippocampal CA1 for a DNMS task was assessed quantitatively in this study using multi-neuronal recordings and an artificial neural network classifier as a decoder.ResultsThe activity of small neuronal ensembles (6-18 cells) over brief time intervals (2-50 ms) contains accurate information specifically related to the matching/non-matching of continuously presented stimuli (stimulus comparison). The accuracy of the combination of neurons pooled over all the ensembles was markedly lower than those of the ensembles over all examined time intervals.ConclusionThe results show that the spatiotemporal patterns of spiking activity among cells in the small neuronal ensemble contain much information that is specifically useful for the stimulus comparison. Small neuronal networks in the hippocampal CA1 might therefore act as a comparator during recognition memory tasks.

Hippocampus Leads Ventral Striatum in Replay of Place-Reward Information

Author SummaryThinking back to an exciting event often includes the scene in which the event took place. Associations between specific places and emotional events are consolidated in memory, but how this is achieved is currently unknown. Two brain areas involved in learning such associations are the hippocampus and the ventral striatum, which represent spatial and emotional information, respectively. A highly valuable object in an environment will prompt humans and animals to take action, such as approaching the object. Here, we demonstrate that a combination of spatial and emotional aspects of a learning experience is replayed in the hippocampus and the ventral striatum during sleep, which is likely to contribute to the consolidation and strengthening of memory traces. This reactivation is coordinated such that the spatial information in the hippocampus is activated shortly before the emotional information in the ventral striatum. This finding is consistent with a central prediction from Memory Consolidation Theory, namely that the hippocampus initiates and orchestrates replay in connected brain areas. In addition, sleep replay occurs at a time scale about ten times faster than during the actual experience, which makes it a mechanism suitable for strengthening synaptic connections associating place with reward. Our results shed new light on the distributed way the brain processes, links, and retrieves different aspects of memories.

Origins of correlated activity in an olfactory circuit

Multineuronal recordings often reveal synchronized spikes in different neurons. How correlated spike timing affects neural codes depends on the statistics of correlations, which in turn reflects the connectivity that gives rise to correlations. However, determining the connectivity of neurons recorded in vivo can be difficult. Here, we investigate the origins of correlated activity in genetically-labeled neurons of the Drosophila antennal lobe. Dual recordings show synchronized spontaneous spikes in projection neurons (PNs) postsynaptic to the same type of olfactory receptor neuron (ORN). Odors increase these correlations. The primary origin of correlations lies in the divergence of each ORN onto every PN in its glomerulus. Reciprocal PN-PN connections make a smaller contribution to correlations, and PN spike trains in different glomeruli are only weakly correlated. PN axons from the same glomerulus reconverge in the lateral horn, where pooling redundant signals may allow lateral horn neurons to average out noise that arises independently in these PNs.

Recurrent Infomax Generates Cell Assemblies, Neuronal Avalanches, and Simple Cell-Like Selectivity

Recently multineuronal recording has allowed us to observe patterned firings, synchronization, oscillation, and global state transitions in the recurrent networks of central nervous systems. We propose a learning algorithm based on the process of information maximization in a recurrent network, which we call recurrent infomax (RI). RI maximizes information retention and thereby minimizes information loss through time in a network. We find that feeding in external inputs consisting of information obtained from photographs of natural scenes into an RI-based model of a recurrent network results in the appearance of Gabor-like selectivity quite similar to that existing in simple cells of the primary visual cortex. We find that without external input, this network exhibits cell assembly-like and synfire chain-like spontaneous activity as well as a critical neuronal avalanche. In addition, we find that RI embeds externally input temporal firing patterns to the network so that it spontaneously reproduces these patterns after learning. RI provides a simple framework to explain a wide range of phenomena observed in in vivo and in vitro neuronal networks, and it will provide a novel understanding of experimental results for multineuronal activity and plasticity from an information-theoretic point of view.

Inferring the capacity of the vector Poisson channel with a Bernoulli model

The capacity defines the ultimate fidelity limits of information transmission by any system. We derive the capacity of parallel Poisson process channels to judge the relative effectiveness of neural population structures. Because the Poisson process is equivalent to a Bernoulli process having small event probabilities, we infer the capacity of multi-channel Poisson models from their Bernoulli surrogates. For neural populations wherein each neuron has individual innervation, inter-neuron dependencies increase capacity, the opposite behavior of populations that share a single input. We use Shannon's rate-distortion theory to show that for Gaussian stimuli, the mean-squared error of the decoded stimulus decreases exponentially in both the population size and the maximal discharge rate. Detailed analysis shows that population coding is essential for accurate stimulus reconstruction. By modeling multi-neuron recordings as a sum of a neural population, we show that the resulting capacity is much less than the population's, reducing it to a level that can be less than provided with two separated neural responses. This result suggests that attempting neural control without spike sorting greatly reduces the achievable fidelity. In contrast, single-electrode neural stimulation does not incur any capacity deficit in comparison to stimulating individual neurons.

3P-226 多点同時記録によって抽出されたジュウシマツHVC局所回路における機能的ネットワーク(神経回路・脳の情報処理,日本生物物理学会若手奨励賞選考会,若手招待講演,第46回日本生物物理学会年会)

3P-226 多点同時記録によって抽出されたジュウシマツHVC局所回路における機能的ネットワーク(神経回路・脳の情報処理,日本生物物理学会若手奨励賞選考会,若手招待講演,第46回日本生物物理学会年会)

Cell type‐specific relationships between spiking and [Ca2+]i in neurons of the Xenopus tadpole olfactory bulb

Multi-neuronal recordings with Ca2+ indicator dyes usually relate [Ca2+]i to action potentials (APs) assuming a stereotypical dependency between the two. However, [Ca2+]i affects and is affected by numerous complex mechanisms that differ from cell type to cell type, from cell compartment to cell compartment. Moreover, [Ca2+]i depends on the specific way a cell is activated. Here we investigate, by combining calcium imaging and on-cell patch clamp recordings, the relationship between APs (spiking) and somatic [Ca2+]i in mitral and granule cells of the olfactory bulb in Xenopus laevis tadpoles. Both cell types exhibit ongoing and odour-modulated [Ca2+]i dynamics. In mitral cells, the occurrence of APs in both spontaneous and odour-evoked situations correlates tightly to step-like [Ca2+]i increases. Moreover, odorant-induced suppression of spontaneous firing couples to a decrease in [Ca2+]i. In contrast, granule cells show a substantial number of uncorrelated events such as increases in [Ca2+]i without APs occurring or APs without any effect upon [Ca2+]i. The correlation between spiking and [Ca2+]i is low, possibly due to somatic NMDAR-mediated and subthreshold voltage-activated Ca2+ entries, and thus does not allow a reliable prediction of APs based on calcium imaging. Taken together, our results demonstrate that the relationship between somatic [Ca2+]i and APs can be cell type specific. Taking [Ca2+]i dynamics as an indicator for spiking activity is thus only reliable if the correlation has been established in the system of interest. When [Ca2+]i and APs are precisely correlated, fast calcium imaging is an extremely valuable tool for determining spatiotemporal patterns of APs in neuronal population.

Multichannel Micromanipulator and Chamber System for Recording Multineuronal Activity in Alert, Non-Human Primates

We describe the design and performance of an electromechanical system for conducting multineuron recording experiments in alert non-human primates. The system is based on a simple design, consisting of a microdrive, control electronics, software, and a unique type of recording chamber. The microdrive consists of an aluminum frame, a set of eight linear actuators driven by computer-controlled miniature stepping motors, and two printed circuit boards (PCBs) that provide connectivity to the electrodes and the control electronics. The control circuitry is structured around an Atmel RISC-based microcontroller, which sends commands to as many as eight motor control cards, each capable of controlling eight motors. The microcontroller is programmed in C and uses serial communication to interface with a host computer. The graphical user interface for sending commands is written in C and runs on a conventional personal computer. The recording chamber is low in profile, mounts within a circular craniotomy, and incorporates a removable internal sleeve. A replaceable Sylastic membrane can be stretched across the bottom opening of the sleeve to provide a watertight seal between the cranial cavity and the external environment. This greatly reduces the susceptibility to infection, nearly eliminates the need for routine cleaning, and permits repeated introduction of electrodes into the brain at the same sites while maintaining the watertight seal. The system is reliable, easy to use, and has several advantages over other commercially available systems with similar capabilities.

Dynamic Synchrony of Firing in the Monkey Prefrontal Cortex during Working-Memory Tasks

Synchronized firing among neurons in the working brain is inferred to reflect coding by cell assemblies, which dynamically change their sizes and functional connections to encode various information. It therefore follows that, if synchronized firing reflects cell-assembly coding, it should show dynamic changes that depend on the tasks and events being processed and on the distance between the neurons. By using unique spike-sorting and multi-neuronal recording methods, we investigated such dynamics of synchrony in the prefrontal cortex of monkeys while they were successively performing two tasks in which working memory for either stimulus duration or color was required. Forty-eight percent of 1405 neuronal pairs showed firing synchrony during the performance of the tasks. Almost half of such neuronal pairs showed fixed synchrony and constantly fired together in both tasks. However, some neuronal pairs showed task-dependent synchrony that appeared in only one of the tasks. Moreover, the other neuronal pairs showed event-task-dependent synchrony that appeared during stimulus or retention periods in the tasks, but the periods showing synchrony varied between the tasks. Fixed synchrony and task-dependent synchrony were mostly observed among neighboring neurons and showed little variation of spike timings; the event-task-dependent synchrony, in contrast, was more often detected among distant neurons with larger variation of spike timings than the other two types of synchrony. These results suggest that some closely neighboring neurons have dynamic and sharp synchrony to represent certain situations (tasks), whereas some distant neurons show more dynamic and unstable synchronous firing to represent quickly changing events being processed in working memory.

Real-time and automatic sorting of multi-neuronal activity for sub-millisecond interactions in vivo

Recent in vitro electrophysiological studies have revealed that neighboring interneurons interact with each other in a sub-millisecond time range via gap junctions and that individual dendritic compartments generate local excitation spikes and back-propagated spikes within a single-neuron. However, most in vivo electrophysiological studies using behaving animals only focus on activity rates of single-neurons and/or large neuronal populations without considering the potential role of such sub-millisecond interactions among neurons. This neglect is due to the limitation of ordinary in vivo multi-neuronal recording and spike sorting techniques applied to behaving animals. Though independent component analysis (ICA) is a powerful method to overcome certain limitations, ICA has a serious problem in that the number of single-electrodes (microwires) must be more than the number of single-neurons to be recorded. Our recently-developed method has solved this limitation of ICA, but a few problems have remained: the computational load is heavy, the method can be used only for off-line, not real-time, processing, and the electrode-neuron drift problem remains unsolved. In this paper, solving all these problems, we introduce a novel system consisting of automatic and real-time spike sorting with ICA in combination with a newly developed multi-electrode, dodecatrode. The system has the potential to answer some important neurobiological questions that have not been explored in in vivo electrophysiological experiments: how sub-millisecond interactions between closely neighboring single-neurons act in freely behaving animals. The system promises to be a bridge connecting electrophysiological studies in vitro and in vivo.

Employing ICA and SOM for spike sorting of multielectrode recordings from CNS

For classification of action potential shapes in multineuron recordings, we present a spike sorting system employing independent component analysis (ICA) and an unsupervised artificial neural network (Kohonen’s self-organizing map, SOM). We focus on how ICA in the first stage of the spike sorting system can be used to address specific problems arising in recordings using multielectrode arrays in the CNS. Using real data recorded from the pontine nuclei in rats and simulated data, we evaluate the performance of several ICA algorithms to remove cross-talk between electrodes using data from continuous recording (or simulation). When using cut-out data, the standard format of extracellular spike recordings, new problems emerge and robust algorithms are needed. We demonstrate that several ICA algorithms show a good performance on cut-out data from multielectrode array recordings (simulated and real data). In tetrode recordings the same neuron is purposely recorded by several electrodes simultaneously and we show, how independent component analysis can be used in this case to identify redundant information and hence to compress relevant information, improving subsequent clustering of a SOM.

Sensorimotor-related discharge of simultaneously recorded, single neurons in the dorsal raphe nucleus of the awake, unrestrained rat

Multi-channel, multi-neuron recording procedures were used to monitor simultaneously the spike train activity of single neurons ( n=7–16 cells/animal) in the dorsal raphe (DR) nucleus of the awake, freely moving rat. Putative serotonergic and non-serotonergic neurons were distinguished from one another on the basis of established criteria, i.e. waveform shape and duration, firing pattern and firing frequency. As a group, presumed serotonergic neurons exhibited low tonic discharge rates, depressed firing after serotonin (5HT)-1a agonist administration, and, except for the transition from sleep to waking, a general insensitivity to specific sensory or motor events. By contrast, non-serotonergic cells in midline and lateral wing sub-regions of the nucleus displayed responses to a variety of sensorimotor events including locomotion, grooming, head movement, chewing, auditory stimuli, and whisker movement (both passive and active). However, within this latter group, the sensorimotor response repertoire of individual cells was not uniform. Likewise, non-5HT cells with diverse response profiles were identified in both medial and lateral sub-regions of the nucleus. Cells categorized as non-serotonergic also had varied responses to 5HT1a agonist administration. These results emphasize the diverse input/output relationships of individual DR neurons and underscore the need for a more comprehensive analysis of such properties under waking conditions in order to obtain a better understanding of the role of the DR nucleus in brain function.

Analysis of multidimensional neural activity via CNN-UM.

In this paper we show that the Cellular Nonlinear Network Universal Machine (CNN-UM) is an excellent tool for analyzing time series of multidimensional binary signals. The developed algorithm is dedicated to process electrophysiological multi-neuron recordings: our aim is to find specific multidimensional activity patterns, which may reflect higher order functional cell-assemblies. The analysis consists of two parts: first, the occurrences of different patterns are counted, then the statistical significance of each occurrence frequency is calculated separately.

Automatic sorting for multi-neuronal activity recorded with tetrodes in the presence of overlapping spikes.

Multi-neuronal recording is a powerful electrophysiological technique that has revealed much of what is known about the neuronal interactions in the brain. However, it is difficult to detect precise spike timings, especially synchronized simultaneous firings, among closely neighboring neurons recorded by one common electrode because spike waveforms overlap on the electrode when two or more neurons fire simultaneously. In addition, the non-Gaussian variability (nonstationarity) of spike waveforms, typically seen in the presence of so-called complex spikes, limits the ability to sort multi-neuronal activities into their single-neuron components. Because of these problems, the ordinary spike-sorting techniques often give inaccurate results. Our previous study has shown that independent component analysis (ICA) can solve these problems and separate single-neuron components from multi-neuronal recordings. The ICA has, however, one serious limitation that the number of separated neurons must be less than the number of electrodes. The present study combines the ICA and the efficiency of the ordinary spike-sorting technique (k-means clustering) to solve the spike-overlapping and the nonstationarity problems with no limitation on the number of single neurons to be separated. First, multi-neuronal activities are sorted into an overly large number of clusters by k-means clustering. Second, the sorted clusters are decomposed by ICA. Third, the decomposed clusters are progressively aggregated into a minimal set of putative single neurons based on similarities of basis vectors estimated by ICA. We applied the present procedure to multi-neuronal waveforms recorded with tetrodes composed of four microwires in the prefrontal cortex of awake behaving monkeys. The results demonstrate that there are functional connections among neighboring pyramidal neurons, some of which fire in a precise simultaneous manner and that precisely time-locked monosynaptic connections are working between neighboring pyramidal neurons and interneurons. Detection of these phenomena suggests that the present procedure can sort multi-neuronal activities, which include overlapping spikes and realistic non-Gaussian variability of spike waveforms, into their single-neuron components. We processed several types of synthesized data sets in this procedure and confirmed that the procedure was highly reliable and stable. The present method provides insights into the local circuit bases of excitatory and inhibitory interactions among neighboring neurons.

Trial to trial variability in either stimulus or action causes apparent correlation and synchrony in neuronal activity

In this report we show that the observed inter-neuronal correlation reflects a superposition of correlations associated with the intrinsic correlation between neurons, and correlations associated with variability in the stimuli presented to, or the actions performed by, the subject. We argue that the effects of either stimulus or action variability on the observed correlation, though generally ignored, can be substantial. Specifically, we demonstrate how observed correlations are effected by trial to trial variability in either stimulus or action. In addition, assuming that all relevant stimuli and actions are known, we outline a method for eliminating their effects on the observed correlation. It is also shown that tuning of correlations to a stimulus or an action might be a direct consequence of variability in that stimulus or action, even in the absence of any modulation of direct inter-neuronal interaction. The effects of stimulus and action variability should therefore be carefully considered when designing and interpreting experiments involving multi-neuronal recordings.

Dynamics of striate cortical activity in the alert macaque: II. Fast time scale synchronization.

Synchronous neuronal activity with millisecond precision has been postulated to contribute to the process of visual perceptual grouping. We have performed multineuron recordings in striate cortex of two alert macaque monkeys to determine if the occurrence and properties of this form of activity are consistent with the minimal requirements of this theory. We find that neuronal synchronization with millisecond precision is a prevalent and robust feature of stimulus-evoked activity in striate cortex. It occurs among adjacent cells recorded by the same electrode (<120 microm), among cells recorded at separate but nearby sites (300-400 microm) and between cells recorded at locations separated by 3-4 mm. The magnitude and probability of synchronous firing is inversely related to the spatial separation between the cells and it occurs within and between groups of cells that are both tuned and untuned for stimulus orientation and direction. Among those tuned for orientation, cell pairs separated by <400 microm showed no clear dependence of correlated firing on orientation preference. The occurrence of gamma-band (20-70 Hz) oscillations in the cellular firing patterns was a strong predictor of synchronous firing at each of the spatial scales. Nearly 90% of the cell pairs showing significant correlation also showed oscillatory firing in one or both cells of the pair. These results are consistent with some, but not all, of the previous reports of synchronous activity in striate cortex of both cat and primates. The similarities in the properties of synchronous oscillations in the monkey and cat suggest that this form of neuronal activity is a general property of mammalian striate cortex. The relation between correlation and oscillation suggests that neuronal rhythmicity is an important mechanism contributing to synchronization.

Multi-neuronal recordings reveal a differential effect of thapsigargin on bicuculline- or gabazine-induced epileptiform excitability in rat hippocampal neuronal networks

The present study was performed to investigate the effects of depleting intracellular Ca 2+ stores on bicuculline- or gabazine-induced epileptiform excitability. Studies were performed on monolayer rat hippocampal neuronal networks utilising a system that allowed simultaneous multiple extracellular single-unit recordings of neuronal activity. Hippocampal neuronal networks were prepared from enzymatically dissociated hippocampi from 18-day-old fetal Wistar rats. The cells were cultured in Neurobasal medium with B27 serum-free supplements directly onto the surface of planar multiple microelectrode arrays with a central recording array of 64 (4×16) indium-tin thin-film recording electrodes. All cells recorded at 21 days-in-vitro exhibited spontaneous discharge activity with firing rates between 0.3–30.7 Hz. γ-aminobutyric acid (GABA) produced a concentration-dependent decrease in firing (EC 50=9.1 μM) which could be blocked by pre-application of bicuculline methobromide (10 μM). Addition of the GABA A-receptor antagonists gabazine (10 μM) or bicuculline (10 μM) resulted in the rapid generation of synchronised bursting within all the cells recorded. Bicuculline exhibited heterogeneity of action on firing rate, whereas gabazine always increased firing. Pre-incubation with thapsigargin, which depletes intracellular calcium stores, resulted in a decrease in the amount of neuronal excitation produced by bicuculline, but not by gabazine, suggesting that bicuculline-induced neuronal excitation requires release of Ca 2+ from intracellular stores.

Cross-correlation measures of unresolved multi-neuron recordings

An increasing number of laboratories are studying population properties of the nervous system using data where the spike activity of more than one neuron is recorded on each electrode and where, accidentally or deliberately, these activities are not resolved into single unit spike trains. We have previously examined the consequences for measurement of cross-correlation between two such electrodes in the limited case where all individual distant (between electrode) correlations are the same and all individual close (on a single electrode) correlations are the same [Bedenbaugh, P.H., and Gerstein, G.L. (1997). Multiunit normalized cross correlation differs from the average single-unit normalized correlation. Neural Computation 9, 1265–1275]. Here, we lift these unrealistic restrictions to allow all values of individual correlation, and examine explicitly the cases of two or three unresolved neurons on each electrode. In these situations, the cross-correlation coefficient measured between the electrodes is a linear sum of the distant correlations, divided by a non-linear function of the close correlations. We then examine in detail the case of a single direct distant correlation and take account of all relevant indirect correlations. The measured interelectrode correlation shows a reduction of this actual distant correlation by a non-linear function of the close correlations on each electrode over most of their possible values. Finally, we examine the consequences of poor waveform sorting for correlation measures; here a supposedly isolated spike train is contaminated by some fraction of the activity of another train, a situation that unfortunately is all too common in experiments. All these distortions become far more serious in the more realistic situation of dynamic firing rates and correlations. This paper is intended as a cautionary note for those who want to draw inferences about neuronal organization and/or coding or representation by using cross-correlation analysis of unresolved recordings.

Model dependence in quantification of spike interdependence by joint peri-stimulus time histogram.

Multineuronal recordings have enabled us to examine context-dependent changes in the relationship between the activities of multiple cells. The joint peri-stimulus time histogram (JPSTH) is a much-used method for investigating the dynamics of the interdependence of spike events between pairs of cells. Its results are often taken as an estimate of interaction strength between cells, independent of modulations in the cells' firing rates. We evaluate the adequacy of this estimate by examining the mathematical structure of how the JPSTH quantifies an interaction strength after excluding the contribution of firing rates. We introduce a simple probabilistic model of interacting point processes to generate simulated spike data and show that the normalized JPSTH incorrectly infers the temporal structure of variations in the interaction parameter strength. This occurs because, in our model, the correct normalization of firing-rate contributions is different than that used in Aertsen, Gerstein, Habib, and Palm's (1989) effective connectivity model. This demonstrates that firing-rate modulations cannot be corrected for in a model-independent manner, and therefore the effective connectivity does not represent a universal characteristic that is independent of modulation of the firing rates. Aertsen et al.'s (1989) effective connectivity may still be used in the analysis of experimental data, provided we are aware that this is simply one of many ways of describing the structure of interdependence. We also discuss some measure-independent characteristics of the structure of interdependence.

Who tells one hand what the other is doing: the neurophysiology of bimanual movements.

Research into neural mechanisms of bimanual coordination sheds light on questions raised by psychophysics. While the results we review are merely suggestive, the overall structure of the neural mechanisms may be emerging slowly.Different mechanisms may serve the spatial and temporal coupling of bimanual movements. Temporal coupling depends on a lateralized pace-keeping mechanism possibly associated with spinal CPGs, is preferentially controlled by the dominant hemisphere, and does not depend directly on the corpus callosum. Spatial coupling, in contrast, arises through interhemispheric interaction. Individual neurons in each hemisphere reflect this interaction, as does interhemispheric synchronization. The interaction allows decoupling of the arms, as substantiated by the PV analysis. Furthermore, bimanual proximal arm movements and finger movements are controlled differently, as shown in differences in the patterns of neural activity in M1 and SMA.Taken as a whole, the research we review has not yet culminated in a conclusive model. However, by combining psychophysics and neurophysiology, we raise concrete, testable hypotheses that may help realize this goal.*To whom correspondence should be addressed (e-mail: eilon@hbf.huji.ac.il).