Multiple Single Neurons Research Articles

Two-layered microwell-array device for preparation of single-neuron culture samples

When a single neuron is cultured in isolation from other neurons, its axon connects with its own dendrites to form a simple, independent network with no synaptic inputs from other neurons. This culture system enables detailed analysis of synaptic function and morphology change in neurites at the single-neuron level, which is useful for elucidating the pathogenesis of neurological diseases and for evaluating the efficacy of therapeutic drugs for them. However, there was previously no device technology capable of simultaneously forming multiple single-neuron samples while allowing co-culture with astrocytes, which is essential for culture of a single neuron isolated from other neurons. In this study, we propose a novel microwell-array device for preparing single-neuron samples. The device consists of an upper layer for cell seeding and a lower layer for cell culture. Each layer has 16 × 16 microwells, and the bottom of each well is made of a 1 μm thick silicon nitride membrane. The membrane of the upper well has one microhole for seeding a single neuron, and the lower membrane has multiple microholes for interaction between a single neuron and astrocytes which are co-cultured back-to-back on both sides of the membrane. When neurons are seeded into the upper well, only one of them passes through the microhole in the upper membrane and falls onto the lower membrane. We evaluated a seeding efficiency of single neurons by changing seeding hole diameter and seeding density. The results showed that the yield of more than 20% was obtained regardless of the seeding density when the seeding hole diameter was 13 μm. We also confirmed that single neurons seeded in this manner and co-cultured with astrocytes developed neurites and formed synapses. These results demonstrated the usefulness of this device for the preparation of single-neuron culture samples.

Cardiac rhythmicity and burstiness are major characteristics of neuronal firing in medullary autonomic networks across sleep-wake states in freely moving rats

Cardiac rhythmicity is a hallmark of the autonomic control system from its sensory inputs all the way to sympathetic efferent nerve discharge directed to the heart and vasculature. Neuron firing in brainstem networks, essential in this control, was studied in great detail under anesthesia but recordings of sympathetic-related neuron firing in freely moving animals remain extremely scarce. We have shown recently [1], that such recordings are possible to perform in freely behaving rats and reported that the pattern of neural activity adheres to the basic principles of organization proposed earlier [2] based on data collected within the constraints of anesthesia, decerebration, or head-restrain. Here we analyzed the firing of sympathetic-related neurons from these recordings over different arousal states. We hypothesized that the essential characteristics, such as rhythmicity and burstiness remain preserved during different sleep-wake states. To address this issue, 23 neurons recorded continuously during waking and sleep in 3 rats were selected. Electrocardiogram, cortical electroencephalogram, and neck muscle electromyogram were recorded along with simultaneous recordings of multiple single neurons using tetrode electrodes or fine wire bundles aimed at central autonomic structures in the medulla as described in [1]. Firing characteristics were analyzed in steady state segments of quiet waking (QW) and slow wave sleep (SWS) as well as during transient events in SWS and REM sleep. We found that steady state discharge of rhythmic bursts was dominant in both QW and SWS even though the average firing rate in the group decreased (from 12.6+1.6 to 7.4+1.3 spike/s) from wake to SWS. Quite remarkably, fluctuations in firing rate within and between QW and SWS were associated with a decrease in the number of bursts rather than the spike per burst ratio. On the other hand, phasic events, such as microarousal associated with transient muscle activity and tachycardia were associated with short (2-5 s) episodes of increase in firing rate characterized by relatively weak cardiac rhythmicity and increased synchronization within the network (i.e. between neurons detected on the same tetrodes).The results show that the study of multiple single neuron relationships in central sympathetic networks is a viable tool to explore autonomic control mechanisms and we emphasize the importance of neural control of cardiovascular adjustments in complex behaviors, including stress, exercise, arousal, sleep-wake states, and different tasks.[1] Kocsis, B.; Topchiy, I., Rhythmic firing of neurons in the medulla of conscious freely behaving rats: rhythmic coupling with baroreceptor input. Pflugers Archiv: Eur J physiol 2022.[2] Barman, S. M., 2019 Ludwig Lecture: Rhythms in sympathetic nerve activity are a key to understanding neural control of the cardiovascular system. Am J Physiol Regul Integr Comp Physiol 2020. NIMH This is the full abstract presented at the American Physiology Summit 2023 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

P.161 Spatiotemporal dynamics of neuronal ensembles in the primate prefrontal cortex during virtual reality navigation tasks

Background: Brain-machine-interface research has utilized multichannel single neuron recordings to decode movement intention. However, the prefrontal cortex (PFC) contains mental representations of more abstract task and goal elements which may be utilized as important signals in a brain-machine-interface. We therefore utilized virtual reality to simulate a real-world task while recording from ensembles of primate PFC neurons. Methods: Two male rhesus macaques (macaca mulatta) were trained to navigate a virtual reality environment using a joystick and learn a context-object association rule. We implanted each monkey with two 96-channel Utah arrays (Blackrock Microsystems) in the lateral PFC (areas 9/46 and 8a) and simultaneously recorded from multiple single neurons. Results: A linear support-vector-machine decoded task elements (context, target location and chosen direction of movement) with significantly greater than chance accuracy. This information was decoded in a sequential manner as the primates made a rule-based decision, with context information appearing first, followed by target location, and chosen side. Conclusions: We found that different neuronal ensembles encode the elements needed for implementing the context rule, and that such ensembles are activated sequentially. Brain-machine-interface systems may benefit by integrating neural data from the PFC, providing salient goal-related information such as the content of the goal and its spatial location.

Rhythmic firing of neurons in the medulla of conscious freely behaving rats: rhythmic coupling with baroreceptor input.

Recent investigations emphasized the importance of neural control of cardiovascular adjustments in complex behaviors, including stress, exercise, arousal, sleep-wake states, and different tasks. Baroreceptor feedback is an essential component of this system acting on different time scales from maintaining stable levels of cardiovascular parameters on the long-term to rapid alterations according to behavior. The baroreceptor input is essentially rhythmic, reflecting periodic fluctuations in arterial blood pressure. Cardiac rhythm is a prominent feature of the autonomic control system, present on different levels, including neuron activity in central circuits. The mechanism of rhythmic entrainment of neuron firing by the baroreceptor input was studied in great detail under anesthesia, but recordings of sympathetic-related neuron firing in freely moving animals remain extremely scarce. In this study, we recorded multiple single neuron activity in the reticular formation of the medulla in freely moving rats during natural behavior. Neurons firing in synchrony with the cardiac rhythm were detected in each experiment (n = 4). In agreement with prior observations in anesthetized cats, we found that neurons in this area exhibited high neuron-to-neuron variability and temporal flexibility in their coupling to cardiac rhythm in freely moving rats, as well. This included firing in bursts at multiples of cardiac cycles, but not directly coupled to the heartbeat, supporting the concept of baroreceptor input entraining intrinsic neural oscillations rather than imposing a rhythm of solely external origin on these networks. It may also point to a mechanism of maintaining the basic characteristics of sympathetic neuron activity, i.e., burst discharge and cardiac-related rhythmicity, on the background of behavior-related adjustments in their firing rate.

Huygens Synchronization of Medial Septal Pacemaker Neurons Generates Hippocampal Theta Oscillation

Episodic learning and memory retrieval are critically dependent on a hippocampal 4-12 Hz oscillatory ‘clock’ signal, the theta oscillation. This clock is largely externally paced, by a network of GABAergic neurons in the medial septum (MS). Theoretical studies suggested a range of hypotheses how this network may achieve theta synchrony; however, experimental evidence is still lacking. By recording multiple single MS neurons and hippocampal local field potential oscillations simultaneously, with both acute and chronically implanted silicon probes, we show that MS pacemaker units oscillate at individual frequencies within the theta range in rodents. Synchronization of MS neuron frequencies, accompanied by an elevation of firing rates, was found to parallel hippocampal theta formation in multiple rodent model systems. This suggests a general mechanism for theta synchronization, akin to the synchronization of weakly coupled pendulum clocks observed by Huygens in the 17th century. We optogenetically identified the MS pacemaker units as parvalbumin-expressing GABAergic neurons, while the previously enigmatic MS glutamatergic neurons were mostly theta-activated non-rhythmic cells. Our data were consistent with a network model of partially connected single-compartment inhibitory pacemaker neurons, in which synchronization and de-synchronization in the frequency domain upon waxing and waning tonic excitatory drive was sufficient to toggle MS network output between theta and non-theta states. These results provide experimental and theoretical support to a frequency-synchronization mechanism for pacing hippocampal theta, which may serve as an inspirational prototype for the countless examples of synchronization processes in the central nervous system from Nematoda to Anthropoda to Chordate and Vertebrate phyla.

Differential responsivity of neurons in perirhinal cortex, lateral entorhinal cortex, and dentate gyrus during time-bridging learning.

Many studies have focused on the function of hippocampal region CA1 as a critical site for associative memory, but much less is known about changes in the afferents to CA1. Here we report the activity of multiple single neurons from perirhinal and entorhinal cortex and from dentate gyrus during trace eyeblink conditioning as well as consolidated recall, and in pseudo-conditioned control rabbits. We also report an analysis of theta activity filtered from the local field potential (LFP). Our results show early associative changes in single-neuron firing rate as well as theta oscillations in lateral entorhinal cortex (EC) and dentate gyrus (DG), and increases in the number of responsive neurons in perirhinal cortex. In both EC and DG, a subset of neurons from conditioned animals exhibited an elevated baseline firing rate and large responses to the conditioned stimulus and trace period. A similar population of cells has been seen in DG and in medial, but not lateral, EC during spatial tasks, suggesting that lateral EC contains cells responsive to a temporal associative task. In contrast to recent studies in our laboratory that found significant CA1 contributions to long-term memory, the activity profiles of neurons within EC and DG were similar for conditioned and pseudoconditioned rabbits during post-consolidation sessions. Collectively these results demonstrate that individual subregions of medial temporal lobe differentially support new and remotely acquired memories. Neuron firing profiles were similar on training trials when conditioned responses were and were not exhibited, demonstrating that these temporal lobe regions represent the CS-US association and do not control the behavioral response. The analysis of theta activity revealed that theta power was modulated by the conditioning stimuli in both the conditioned and pseudoconditioned groups and that although both groups exhibited a resetting of phase to the corneal airpuff, only the conditioned group exhibited a resetting of phase to the whisker conditioned stimulus.

Identification and characterization of functional modules reflecting transcriptome transition during human neuron maturation

BackgroundNeuron maturation is a critical process in neurogenesis, during which neurons gain their morphological, electrophysiological and molecular characteristics for their functions as the central components of the nervous system.ResultsTo better understand the molecular changes during this process, we combined the protein-protein interaction network and public single cell RNA-seq data of mature and immature neurons to identify functional modules relevant to the neuron maturation process in humans. Among the 109 functional modules in total, 33 showed significant gene expression level changes (discriminating modules) which participate in varied functions including energy consumption, synaptic functions and housekeeping functions such as translation and splicing. Based on the identified modules, we trained a neuron maturity index (NMI) model for the quantification of maturation states of single neurons or purified bulk neurons. Applied to multiple single neuron transcriptome data sets of neuron development in humans and mice, the NMI model made estimation of neuron maturity states which were significantly correlated with the neuron maturation trajectories in both species, implying the reproducibility and conservation of the identified transcriptome transition.ConclusionWe identified 33 discriminating modules whose activities were significantly correlated with single neuron maturity states, which may play important roles in the neuron maturation process.

Phase Locking of Multiple Single Neurons to the Local Field Potential in Cat V1.

The local field potential (LFP) is thought to reflect a temporal reference for neuronal spiking, which may facilitate information coding and orchestrate the communication between neural populations. To explore this proposed role, we recorded the LFP and simultaneously the spike activity of one to three nearby neurons in V1 of anesthetized cats during the presentation of drifting sinusoidal gratings, binary dense noise stimuli, and natural movies. In all stimulus conditions and during spontaneous activity, the average LFP power at frequencies >20 Hz was higher when neurons were spiking versus not spiking. The spikes were weakly but significantly phase locked to all frequencies of the LFP. The average spike phase of the LFP was stable across high and low levels of LFP power, but the strength of phase locking at low frequencies (≤10 Hz) increased with increasing LFP power. In a next step, we studied how strong stimulus responses of single neurons are reflected in the LFP and the LFP-spike relationship. We found that LFP power was slightly increased and phase locking was slightly stronger during strong compared with weak stimulus-locked responses. In summary, the coupling strength between high frequencies of the LFP and spikes was not strongly modulated by LFP power, which is thought to reflect spiking synchrony, nor was it strongly influenced by how strongly the neuron was driven by the stimulus. Furthermore, a comparison between neighboring neurons showed no clustering of preferred LFP phase. We argue that hypotheses on the relevance of phase locking in their current form are inconsistent with our findings.

High-throughput spatial light modulation two-photon microscopy for fast functional imaging.

The optical monitoring of multiple single neuron activities requires high-throughput parallel acquisition of signals at millisecond temporal resolution. To this aim, holographic two-photon microscopy (2PM) based on spatial light modulators (SLMs) has been developed in combination with standard laser scanning microscopes. This requires complex coordinate transformations for the generation of holographic patterns illuminating the points of interest. We present a simpler and fully digital setup (SLM-2PM) which collects three-dimensional two-photon images by only exploiting the SLM. This configuration leads to an accurate placement of laser beamlets over small focal volumes, eliminating mechanically moving parts and making the system stable over long acquisition times. Fluorescence signals are diffraction limited and are acquired through a pixelated detector, setting the actual limit to the acquisition rate. High-resolution structural images were acquired by raster-scanning the sample with a regular grid of excitation focal volumes. These images allowed the selection of the structures to be further investigated through an interactive operator-guided selection process. Functional signals were collected by illuminating all the preselected points with a single hologram. This process is exemplified for high-speed (up to 1kHz) two-photon calcium imaging on acute cerebellar slices.

Brain Plasticity by Multiple Single Neurons Scattered in the Brain Cortex

Brain Plasticity by Multiple Single Neurons Scattered in the Brain Cortex

Recording Single Neurons' Action Potentials from Freely Moving Pigeons Across Three Stages of Learning

While the subject of learning has attracted immense interest from both behavioral and neural scientists, only relatively few investigators have observed single-neuron activity while animals are acquiring an operantly conditioned response, or when that response is extinguished. But even in these cases, observation periods usually encompass only a single stage of learning, i.e. acquisition or extinction, but not both (exceptions include protocols employing reversal learning; see Bingmanet al.(1) for an example). However, acquisition and extinction entail different learning mechanisms and are therefore expected to be accompanied by different types and/or loci of neural plasticity. Accordingly, we developed a behavioral paradigm which institutes three stages of learning in a single behavioral session and which is well suited for the simultaneous recording of single neurons' action potentials. Animals are trained on a single-interval forced choice task which requires mapping each of two possible choice responses to the presentation of different novel visual stimuli (acquisition). After having reached a predefined performance criterion, one of the two choice responses is no longer reinforced (extinction). Following a certain decrement in performance level, correct responses are reinforced again (reacquisition). By using a new set of stimuli in every session, animals can undergo the acquisition-extinction-reacquisition process repeatedly. Because all three stages of learning occur in a single behavioral session, the paradigm is ideal for the simultaneous observation of the spiking output of multiple single neurons. We use pigeons as model systems, but the task can easily be adapted to any other species capable of conditioned discrimination learning.

Recording Single Neurons' Action Potentials from Freely Moving Pigeons Across Three Stages of Learning

While the subject of learning has attracted immense interest from both behavioral and neural scientists, only relatively few investigators have observed single-neuron activity while animals are acquiring an operantly conditioned response, or when that response is extinguished. But even in these cases, observation periods usually encompass only a single stage of learning, i.e. acquisition or extinction, but not both (exceptions include protocols employing reversal learning; see Bingman et al.1 for an example). However, acquisition and extinction entail different learning mechanisms and are therefore expected to be accompanied by different types and/or loci of neural plasticity. Accordingly, we developed a behavioral paradigm which institutes three stages of learning in a single behavioral session and which is well suited for the simultaneous recording of single neurons' action potentials. Animals are trained on a single-interval forced choice task which requires mapping each of two possible choice responses to the presentation of different novel visual stimuli (acquisition). After having reached a predefined performance criterion, one of the two choice responses is no longer reinforced (extinction). Following a certain decrement in performance level, correct responses are reinforced again (reacquisition). By using a new set of stimuli in every session, animals can undergo the acquisition-extinction-reacquisition process repeatedly. Because all three stages of learning occur in a single behavioral session, the paradigm is ideal for the simultaneous observation of the spiking output of multiple single neurons. We use pigeons as model systems, but the task can easily be adapted to any other species capable of conditioned discrimination learning.

The spatiotemporal organization of cerebellar network activity resolved by two-photon imaging of multiple single neurons

In order to investigate the spatiotemporal organization of neuronal activity in local microcircuits, techniques allowing the simultaneous recording from multiple single neurons are required. To this end, we implemented an advanced spatial-light modulator two-photon microscope (SLM-2PM). A critical issue for cerebellar theory is the organization of granular layer activity in the cerebellum, which has been predicted by single-cell recordings and computational models. With SLM-2PM, calcium signals could be recorded from different network elements in acute cerebellar slices including granule cells (GrCs), Purkinje cells (PCs) and molecular layer interneurons. By combining WCRs with SLM-2PM, the spike/calcium relationship in GrCs and PCs could be extrapolated toward the detection of single spikes. The SLM-2PM technique made it possible to monitor activity of over tens to hundreds neurons simultaneously. GrC activity depended on the number of spikes in the input mossy fiber bursts. PC and molecular layer interneuron activity paralleled that in the underlying GrC population revealing the spread of activity through the cerebellar cortical network. Moreover, circuit activity was increased by the GABA-A receptor blocker, gabazine, and reduced by the AMPA and NMDA receptor blockers, NBQX and APV. The SLM-2PM analysis of spatiotemporal patterns lent experimental support to the time-window and center-surround organizing principles of the granular layer.

Morris‐Coole Epilepsia Prize – 2012

Epilepsia is pleased to announce the winner of the 2012 Morris-Coole Epilepsia Prize—Mark R. Bower, Ph.D.—for the article “Spatiotemporal neuronal correlates of seizure generation in focal epilepsy” (Mark R. Bower, Matt Stead, Fredric B. Meyer, W. Richard Marsh, and Gregory A. Worrell, Epilepsia 53: 807–816, 2012). The research group is from the Mayo Clinic in Rochester, MN, U.S.A. The Morris-Coole Epilepsia Prize is supported by the International League Against Epilepsy (ILAE) and the Morris-Coole Trust. The Prize is awarded each year to the first author of a paper that presents high quality research (basic or clinical) with important implications for understanding and/or treating epilepsy-related phenomena. It is designed to encourage young scientists and/or clinicians to continue their work in epilepsy research. The Prize consists of $5,000 and a specially designed insignia pin. This year's Prizewinner will receive the award, and present a talk on his research, at the Awards Symposium of the 30th International Epilepsy Congress in Montreal on June 24, 2013. Selection of the Prizewinner starts with nominations from the Associate Editors of Epilepsia. From the set of nominated papers (full-length original research reports, published in a print issue of Epilepsia during 2012), a committee composed of the Co-Editors-in-Chief of Epilepsia and the President of ILAE chooses one study that the committee believes will have a particularly significant impact on the epilepsy field. Dr. Bower, the recipient of the 2012 (Epilepsia Volume 53) Prize, is Research Fellow in the Department of Neurology at Mayo Clinic, and a member of the Mayo Systems Electrophysiology Laboratory led by Drs. Matt Stead and Gregory A. Worrell. He received his Ph.D. in Neuroscience from the University of Arizona working with Dr. Bruce McNaughton on place cells and cognitive map formation in freely moving animals. His postdoctoral fellowship, with Dr. Paul Buckmaster at Stanford University, involved recording the activity of multiple single neurons prior to and during seizures in the pilocarpine model of epilepsy; he was awarded the Eric W. Lothman Fellowship by the Epilepsy Foundation for this work. At Mayo Clinic, Dr. Bower has helped translate the recording and analytical techniques developed in animal models to long-term, continuous recordings from patients undergoing treatment for epilepsy. His research centers on understanding how ongoing neuronal activity progresses into seizures, and on identifying robust biomarkers of impending seizures from the activity of multiple single neurons—with the goal of providing new therapies for patients with epilepsy.

A new method to infer higher-order spike correlations from membrane potentials

What is the role of higher-order spike correlations for neuronal information processing? Common data analysis methods to address this question are devised for the application to spike recordings from multiple single neurons. Here, we present a new method which evaluates the subthreshold membrane potential fluctuations of one neuron, and infers higher-order correlations among the neurons that constitute its presynaptic population. This has two important advantages: Very large populations of up to several thousands of neurons can be studied, and the spike sorting is obsolete. Moreover, this new approach truly emphasizes the functional aspects of higher-order statistics, since we infer exactly those correlations which are seen by a neuron. Our approach is to represent the subthreshold membrane potential fluctuations as presynaptic activity filtered with a fixed kernel, as it would be the case for a leaky integrator neuron model. This allows us to adapt the recently proposed method CuBIC (cumulant based inference of higher-order correlations from the population spike count; Staude et al., J Comput Neurosci 29(1–2):327–350, 2010c) with which the maximal order of correlation can be inferred. By numerical simulation we show that our new method is reasonably sensitive to weak higher-order correlations, and that only short stretches of membrane potential are required for their reliable inference. Finally, we demonstrate its remarkable robustness against violations of the simplifying assumptions made for its construction, and discuss how it can be employed to analyze in vivo intracellular recordings of membrane potentials.

Concept cells: the building blocks of declarative memory functions

Intracranial recordings in subjects suffering from intractable epilepsy - made during their evaluation for an eventual surgical removal of the epileptic focus - have allowed the extraordinary opportunity to study the firing of multiple single neurons in awake and behaving human subjects. These studies have shown that neurons in the human medial temporal lobe respond in a remarkably selective and abstract manner to particular persons or objects, such as Jennifer Aniston, Luke Skywalker or the Tower of Pisa. These neurons have been named 'Jennifer Aniston neurons' or, more recently, 'concept cells'. I argue that the sparse, explicit and abstract representation of these neurons is crucial for memory functions, such as the creation of associations and the transition between related concepts that leads to episodic memories and the flow of consciousness.

An online brain–machine interface using decoding of movement direction from the human electrocorticogram

A brain–machine interface (BMI) can be used to control movements of an artificial effector, e.g. movements of an arm prosthesis, by motor cortical signals that control the equivalent movements of the corresponding body part, e.g. arm movements. This approach has been successfully applied in monkeys and humans by accurately extracting parameters of movements from the spiking activity of multiple single neurons. We show that the same approach can be realized using brain activity measured directly from the surface of the human cortex using electrocorticography (ECoG). Five subjects, implanted with ECoG implants for the purpose of epilepsy assessment, took part in our study. Subjects used directionally dependent ECoG signals, recorded during active movements of a single arm, to control a computer cursor in one out of two directions. Significant BMI control was achieved in four out of five subjects with correct directional decoding in 69%–86% of the trials (75% on average). Our results demonstrate the feasibility of an online BMI using decoding of movement direction from human ECoG signals. Thus, to achieve such BMIs, ECoG signals might be used in conjunction with or as an alternative to intracortical neural signals.

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.

Large-scale Recording of Neurons by Movable Silicon Probes in Behaving Rodents

A major challenge in neuroscience is linking behavior to the collective activity of neural assemblies. Understanding of input-output relationships of neurons and circuits requires methods with the spatial selectivity and temporal resolution appropriate for mechanistic analysis of neural ensembles in the behaving animal, i.e. recording of representatively large samples of isolated single neurons. Ensemble monitoring of neuronal activity has progressed remarkably in the past decade in both small and large-brained animals, including human subjects. Multiple-site recording with silicon-based devices are particularly effective because of their scalability, small volume and geometric design. Here, we describe methods for recording multiple single neurons and local field potential in behaving rodents, using commercially available micro-machined silicon probes with custom-made accessory components. There are two basic options for interfacing silicon probes to preamplifiers: printed circuit boards and flexible cables. Probe supplying companies (http://www.neuronexustech.com/; http://www.sbmicrosystems.com/; http://www.acreo.se/) usually provide the bonding service and deliver probes bonded to printed circuit boards or flexible cables. Here, we describe the implantation of a 4-shank, 32-site probe attached to flexible polyimide cable, and mounted on a movable microdrive. Each step of the probe preparation, microdrive construction and surgery is illustrated so that the end user can easily replicate the process.

Measuring the Quality of Neuronal Identification in Ensemble Recordings

Technological advances in electrode construction and digital signal processing now allow recording simultaneous extracellular action potential discharges from many single neurons, with the potential to revolutionize understanding of the neural codes for sensory, motor, and cognitive variables. Such studies have revealed the importance of ensemble neural codes, encoding information in the dynamic relationships among the action potential spike trains of multiple single neurons. Although the success of this research depends on the accurate classification of extracellular action potentials to individual neurons, there are no widely used quantitative methods for assessing the quality of the classifications. Here we describe information theoretic measures of action potential waveform isolation applicable to any dataset that have an intuitive, universal interpretation, that are not dependent on the methods or choice of parameters for single-unit isolation, and that have been validated using a dataset of simultaneous intracellular and extracellular neuronal recordings from Sprague Dawley rats.