Multiple Single-unit Recordings Research Articles

Development of multisensory processing in ferret parietal cortex.

It is well known that the nervous system adjusts itself to its environment during development. Although a great deal of effort has been directed towards understanding the developmental processes of the individual sensory systems (e.g., vision, hearing, etc.), only one major study has examined the maturation of multisensory processing in cortical neurons. Therefore, the present investigation sought to evaluate multisensory development in a different cortical region and species. Using multiple single-unit recordings in anaesthetised ferrets (n = 18) of different ages (from postnatal day 80 to 300), we studied the responses of neurons from the rostral posterior parietal (PPr) area to presentations of visual, tactile and combined visual-tactile stimulation. The results showed that multisensory neurons were infrequent at the youngest ages (pre-pubertal) and progressively increased through the later ages. Significant response changes that result from multisensory stimulation (defined as multisensory integration [MSI]) were observed in post-pubertal adolescent animals, and the magnitude of these integrated responses also increased across this age group. Furthermore, non-significant multisensory response changes were progressively increased in adolescent animals. Collectively, at the population level, MSI was observed to shift from primarily suppressive levels in infants to increasingly higher levels in later stages. These data indicate that, like the unisensory systems from which it is derived, multisensory processing shows developmental changes whose specific time course may be regionally and species-dependent.

A comparison of multisensory features of two auditory cortical areas: primary (A1) and higher-order dorsal zone (DZ).

From myriads of ongoing stimuli, the brain creates a fused percept of the environment. This process, which culminates in perceptual binding, is presumed to occur through the operations of multisensory neurons that occur throughout the brain. However, because different brain areas receive different inputs and have different cytoarchitechtonics, it would be expected that local multisensory features would also vary across regions. The present study investigated that hypothesis using multiple single-unit recordings from anesthetized cats in response to controlled, electronically-generated separate and combined auditory, visual, and somatosensory stimulation. These results were used to compare the multisensory features of neurons in cat primary auditory cortex (A1) with those identified in the nearby higher-order auditory region, the Dorsal Zone (DZ). Both regions exhibited the same forms of multisensory neurons, albeit in different proportions. Multisensory neurons exhibiting excitatory or inhibitory properties occurred in similar proportions in both areas. Also, multisensory neurons in both areas expressed similar levels of multisensory integration. Because responses to auditory cues alone were so similar to those that included non-auditory stimuli, it is proposed that this effect represents a mechanism by which multisensory neurons subserve the process of perceptual binding.

The contribution of low contrast-preferring neurons to information representation in the primary visual cortex after learning.

Animals exhibit improved perception of lower-contrast visual objects after training. We explored this neuronal mechanism using multiple single-unit recordings from deep layers of the primary visual cortex (V1) of trained rats during orientation discrimination. We found that the firing rates of a subset of neurons increased by reducing luminance contrast, being at least above basal activities at low contrast. These low contrast–preferring neurons were rare during passive viewing without training or anesthesia after training. They fired more frequently in correct-choice than incorrect-choice trials. At single-neuron and population levels, they efficiently represented low-contrast orientations. Following training, in addition to generally enhanced excitation, the phase synchronization of spikes to beta oscillations at high contrast was stronger in putative inhibitory than excitatory neurons. The change in excitation-inhibition balance might contribute to low-contrast preference. Thus, low-contrast preference in V1 activity is strengthened in an experience-dependent manner, which may contribute to low-contrast visual discrimination.

Twister3: a simple and fast microwire twister

Objective. Twisted wire probes (TWPs, e.g. stereotrodes and tetrodes) provide a cheap and reliable method for obtaining high quality, multiple single-unit neural recordings in freely moving animals. Despite their ubiquity, TWPs are constructed using a tedious procedure consisting of manually folding, turning, and fusing microwire. This imposes a significant labor burden on research personnel who use TWPs in their experiments. Approach. To address this issue, we created Twister3, an open-source microwire twisting machine. This machine features a quick-draw wire feeder that eliminates manual wire folding, an auto-aligning motor attachment mechanism which results in consistently straight probes, and a high speed motor for rapid probe turning.Main results. Twister3 greatly increases the speed and repeatability of constructing twisted microwire probes compared to existing options. Users with less than one hour of experience using the device were able to make ~70 tetrodes per hour, on average. It is cheap, well documented, and all associated designs and source code are open-source. Significance. Twister3 significantly reduces the labor burden of creating high-quality TWPs so electrophysiologists can spend more of their time performing recordings rather than making probes. Therefore, this device is of interest to any lab performing TWP neural recordings, for example, using microdrives.

Application of Parylene-Based Flexible Multi-Electrode Array for Recording From Subcortical Brain Regions From Behaving Rats.

Obtaining multiple single-unit recordings in particular neural networks from behaving animals is crucial for the understanding of cognitive functions of the brain. Attaining stable, chronic recordings from the brain is also the foundation to develop effective cortical prosthetic devices. However, severe immune response caused by micromotion between stiff implants and surrounding brain tissue often limits the lifetime of penetrating, neural recording devices. To reduce the stiffness mismatch between recording devices and brain tissue, we developed a flexible, polymer based multi-electrode array for recording single neuron activities from the rat hippocampus, a major subcortical structure of the rat brain. Parylene C, a biocompatible polymer, was used as the structural and insulation material of the multi-electrode array. 64 platinum (Pt) recording electrodes were placed in groups along each shank to conform to the anatomical distribution of hippocampal principle neurons. The multi-electrode array was chronically implanted in three animals. After recovery, neural activity together with movement traces were collected from the behaving animals.

A state space approach for piecewise-linear recurrent neural networks for identifying computational dynamics from neural measurements.

The computational and cognitive properties of neural systems are often thought to be implemented in terms of their (stochastic) network dynamics. Hence, recovering the system dynamics from experimentally observed neuronal time series, like multiple single-unit recordings or neuroimaging data, is an important step toward understanding its computations. Ideally, one would not only seek a (lower-dimensional) state space representation of the dynamics, but would wish to have access to its statistical properties and their generative equations for in-depth analysis. Recurrent neural networks (RNNs) are a computationally powerful and dynamically universal formal framework which has been extensively studied from both the computational and the dynamical systems perspective. Here we develop a semi-analytical maximum-likelihood estimation scheme for piecewise-linear RNNs (PLRNNs) within the statistical framework of state space models, which accounts for noise in both the underlying latent dynamics and the observation process. The Expectation-Maximization algorithm is used to infer the latent state distribution, through a global Laplace approximation, and the PLRNN parameters iteratively. After validating the procedure on toy examples, and using inference through particle filters for comparison, the approach is applied to multiple single-unit recordings from the rodent anterior cingulate cortex (ACC) obtained during performance of a classical working memory task, delayed alternation. Models estimated from kernel-smoothed spike time data were able to capture the essential computational dynamics underlying task performance, including stimulus-selective delay activity. The estimated models were rarely multi-stable, however, but rather were tuned to exhibit slow dynamics in the vicinity of a bifurcation point. In summary, the present work advances a semi-analytical (thus reasonably fast) maximum-likelihood estimation framework for PLRNNs that may enable to recover relevant aspects of the nonlinear dynamics underlying observed neuronal time series, and directly link these to computational properties.

Cell assemblies at multiple time scales with arbitrary lag constellations.

Hebb's idea of a cell assembly as the fundamental unit of neural information processing has dominated neuroscience like no other theoretical concept within the past 60 years. A range of different physiological phenomena, from precisely synchronized spiking to broadly simultaneous rate increases, has been subsumed under this term. Yet progress in this area is hampered by the lack of statistical tools that would enable to extract assemblies with arbitrary constellations of time lags, and at multiple temporal scales, partly due to the severe computational burden. Here we present such a unifying methodological and conceptual framework which detects assembly structure at many different time scales, levels of precision, and with arbitrary internal organization. Applying this methodology to multiple single unit recordings from various cortical areas, we find that there is no universal cortical coding scheme, but that assembly structure and precision significantly depends on the brain area recorded and ongoing task demands.

Neural representation of a spatial odor memory in the honeybee mushroom body

Insects make use of lateralized olfactory information from their left and right antennae. Honeybees can learn to distinguish side-specific odor cues in classical conditioning experiments, i.e. they associate a specific stimulus combination of odor identity and spatial location (left or right) with the reward [1]. This requires inter-hemispheric transfer of lateralized information and a side-specific odor memory. Mushroom body (MB) output neurons, the so-called extrinsic neurons (ENs), make inter-hemispheric connections between the two MBs and are thus candidates for the inter-hemispheric transfer of lateralized stimulus information. We could show previously that ENs in the honeybee undergo plastic changes in classical conditioning [2]. Here, we investigate neuronal plasticity in ENs of the honeybee in a sides-specific learning paradigm. We performed multiple single-unit recordings from ENs of one MB. Prior to conditioning (PRE) each bee was repeatedly presented with two different odors on the two antennae separately. During acquisition one of these odors was repeatedly presented to the antenna contralateral to the recording side (CS+) and paired with a sugar reward, while the other odor was presented without reward (differential conditioning). Three hours after training (POST) we repeated the initial protocol presenting each of the two odors repeatedly to each of the antennae. In the behavioral test the bees distinguished the CS+ stimulus configuration of odor and side from the other three stimulus combinations. At the neuronal level we found clear and distinct odor representations in the EN population before training (PRE) only when stimulated on the antenna ipsilateral to the recording side. However, no odor responses were measured in any of the ENs when stimulated at the contralateral antenna. This picture changed drastically after training (POST). Now, the rewarded stimulus combination (CS+) resulted in a strong population response pattern. The population code for the CS+ configuration was clearly distinct from all three other stimulus configurations. Quantification of the temporal response latencies showed that the ENs encode an odor approximately within 75ms for ipsilateral stimulation. Odor representation was delayed by about 60ms for a contralateral stimulation. We discuss two alternative explanations for this temporal delay. We hypothesized previously that ENs at the MB output encode the experience-dependent value of a particular stimulus [2-4]. Our results here provide additional evidence for this hypothesis. A representation of the rewarded stimulus combination (CS+) of a particular odor and its spatial location (left or right) develops only due to reward conditioning. Before learning, only ipsilateral odor information was represented.

Amphetamine Exerts Dose-Dependent Changes in Prefrontal Cortex Attractor Dynamics during Working Memory.

Modulation of neural activity by monoamine neurotransmitters is thought to play an essential role in shaping computational neurodynamics in the neocortex, especially in prefrontal regions. Computational theories propose that monoamines may exert bidirectional (concentration-dependent) effects on cognition by altering prefrontal cortical attractor dynamics according to an inverted U-shaped function. To date, this hypothesis has not been addressed directly, in part because of the absence of appropriate statistical methods required to assess attractor-like behavior in vivo. The present study used a combination of advanced multivariate statistical, time series analysis, and machine learning methods to assess dynamic changes in network activity from multiple single-unit recordings from the medial prefrontal cortex (mPFC) of rats while the animals performed a foraging task guided by working memory after pretreatment with different doses of d-amphetamine (AMPH), which increases monoamine efflux in the mPFC. A dose-dependent, bidirectional effect of AMPH on neural dynamics in the mPFC was observed. Specifically, a 1.0 mg/kg dose of AMPH accentuated separation between task-epoch-specific population states and convergence toward these states. In contrast, a 3.3 mg/kg dose diminished separation and convergence toward task-epoch-specific population states, which was paralleled by deficits in cognitive performance. These results support the computationally derived hypothesis that moderate increases in monoamine efflux would enhance attractor stability, whereas high frontal monoamine levels would severely diminish it. Furthermore, they are consistent with the proposed inverted U-shaped and concentration-dependent modulation of cortical efficiency by monoamines.

Single-unit analysis of somatosensory processing in the core auditory cortex of hearing ferrets.

The recent findings in several species that the primary auditory cortex processes non-auditory information have largely overlooked the possibility of somatosensory effects. Therefore, the present investigation examined the core auditory cortices (anterior auditory field and primary auditory cortex) for tactile responsivity. Multiple single-unit recordings from anesthetised ferret cortex yielded histologically verified neurons (n = 311) tested with electronically controlled auditory, visual and tactile stimuli, and their combinations. Of the auditory neurons tested, a small proportion (17%) was influenced by visual cues, but a somewhat larger number (23%) was affected by tactile stimulation. Tactile effects rarely occurred alone and spiking responses were observed in bimodal auditory-tactile neurons. However, the broadest tactile effect that was observed, which occurred in all neuron types, was that of suppression of the response to a concurrent auditory cue. The presence of tactile effects in the core auditory cortices was supported by a substantial anatomical projection from the rostral suprasylvian sulcal somatosensory area. Collectively, these results demonstrate that crossmodal effects in the auditory cortex are not exclusively visual and that somatosensation plays a significant role in modulation of acoustic processing, and indicate that crossmodal plasticity following deafness may unmask these existing non-auditory functions.

Differential spike timing and phase dynamics of reticular thalamic and prefrontal cortical neuronal populations during sleep spindles.

The 8-15 Hz thalamocortical oscillations known as sleep spindles are a universal feature of mammalian non-REM sleep, during which they are presumed to shape activity-dependent plasticity in neocortical networks. The cortex is hypothesized to contribute to initiation and termination of spindles, but the mechanisms by which it implements these roles are unknown. We used dual-site local field potential and multiple single-unit recordings in the thalamic reticular nucleus (TRN) and medial prefrontal cortex (mPFC) of freely behaving rats at rest to investigate thalamocortical network dynamics during natural sleep spindles. During each spindle epoch, oscillatory activity in mPFC and TRN increased in frequency from onset to offset, accompanied by a consistent phase precession of TRN spike times relative to the cortical oscillation. In mPFC, the firing probability of putative pyramidal cells was highest at spindle initiation and termination times. We thus identified "early" and "late" cell subpopulations and found that they had distinct properties: early cells generally fired in synchrony with TRN spikes, whereas late cells fired in antiphase to TRN activity and also had higher firing rates than early cells. The accelerating and highly structured temporal pattern of thalamocortical network activity over the course of spindles therefore reflects the engagement of distinct subnetworks at specific times across spindle epochs. We propose that early cortical cells serve a synchronizing role in the initiation and propagation of spindle activity, whereas the subsequent recruitment of late cells actively antagonizes the thalamic spindle generator by providing asynchronous feedback.

Microcircuits for Hierarchical Elaboration of Object Coding Across Primate Temporal Areas

In primates, neuronal representations of objects are processed hierarchically in occipitotemporal cortices. A "novel" feature of objects is thought to emerge and become prevalent at a cortical area because of processing in this area. We tested the possibility that a feature representation prevalent in a given area emerges in the microcircuit of a hierarchically prior area as a small number of prototypes and then becomes prevalent in the subsequent area. We recorded multiple single units in each of hierarchically sequential areas TE and 36 of macaque temporal cortex and found the predicted convergent microcircuit for object-object association in area TE. Associative codes were then built up over time in the microcircuit of area 36. These results suggest a computational principle underlying sequentially elaborated object representations.

Identifying sources of non-stationary neural ensemble dynamics

In the traditional view on brain activity dynamics, the cognitive flow of information wanders through multiple stable states driven by task-dependent inputs [1-3]. This focus has been recently challenged both empirically and from the modeling perspective. For instance, experimental studies suggest that olfactory [4] and gustatory representations [5] can be understood as a sequence of temporally stable, attractor-like states; but such transient states are essentially transient and driven by stochastic fluctuations. Likewise, in several contemporary models, intrinsic activity fluctuations can drive default transitions between states [6,7]. It has been recently proposed that such transient states are basically shaped by anatomical connectivity and transitions between them occur even in the absence of external stimuli [8]: Noise enriches the dynamical repertoire of deterministic states; creating flexible 'ghost' attractors which enable the effective processing of task-related cognitive entities [7]. A different metaphor of transient brain dynamics was proposed by Rabinovich and colleagues [9]. In such model, transitions between states mapping cognitive entities is purely deterministic: The dynamical portrait of the model consists of successions of temporally stable states i.e. metastable saddle points linked by heteroclinic channels. Such dynamical objects are particularly reliable, but neural activity eventually switches between them even without the intervention of noise or external inputs. In this work we develop an empirical criterion to discern whether observable neural ensemble activity can be originated by non-autonomous yet deterministic dynamical systems or rather by stochastic fluctuations between temporally attracting states. Towards this goal, we used in vivo multiple single-unit recording in rodent frontal cortex during a decision making task. Effective dynamics of neural activity is first empirically reconstructed in nonlinear state spaces [10]. Then, trajectory analyses enable us to differentiate systems driven by non-automatous dynamics from those driven by stochastic transitions.

A review of cell assemblies

Since the cell assembly (CA) was hypothesised, it has gained substantial support and is believed to be the neural basis of psychological concepts. A CA is a relatively small set of connected neurons, that through neural firing can sustain activation without stimulus from outside the CA, and is formed by learning. Extensive evidence from multiple single unit recording and other techniques provides support for the existence of CAs that have these properties, and that their neurons also spike with some degree of synchrony. Since the evidence is so broad and deep, the review concludes that CAs are all but certain. A model of CAs is introduced that is informal, but is broad enough to include, e.g. synfire chains, without including, e.g. holographic reduced representation. CAs are found in most cortical areas and in some sub-cortical areas, they are involved in psychological tasks including categorisation, short-term memory and long-term memory, and are central to other tasks including working memory. There is currently insufficient evidence to conclude that CAs are the neural basis of all concepts. A range of models have been used to simulate CA behaviour including associative memory and more process- oriented tasks such as natural language parsing. Questions involving CAs, e.g. memory persistence, CAs' complex interactions with brain waves and learning, remain unanswered. CA research involves a wide range of disciplines including biology and psychology, and this paper reviews literature directly related to the CA, providing a basis of discussion for this interdisciplinary community on this important topic. Hopefully, this discussion will lead to more formal and accurate models of CAs that are better linked to neuropsychological data.

Contextual encoding by ensembles of medial prefrontal cortex neurons

Contextual representations serve to guide many aspects of behavior and influence the way stimuli or actions are encoded and interpreted. The medial prefrontal cortex (mPFC), including the anterior cingulate subregion, has been implicated in contextual encoding, yet the nature of contextual representations formed by the mPFC is unclear. Using multiple single-unit tetrode recordings in rats, we found that different activity patterns emerged in mPFC ensembles when animals moved between different environmental contexts. These differences in activity patterns were significantly larger than those observed for hippocampal ensembles. Whereas ≈11% of mPFC cells consistently preferred one environment over the other across multiple exposures to the same environments, optimal decoding (prediction) of the environmental setting occurred when the activity of up to ≈50% of all mPFC neurons was taken into account. On the other hand, population activity patterns were not identical upon repeated exposures to the very same environment. This was partly because the state of mPFC ensembles seemed to systematically shift with time, such that we could sometimes predict the change in ensemble state upon later reentry into one environment according to linear extrapolation from the time-dependent shifts observed during the first exposure. We also observed that many strongly action-selective mPFC neurons exhibited a significant degree of context-dependent modulation. These results highlight potential differences in contextual encoding schemes by the mPFC and hippocampus and suggest that the mPFC forms rich contextual representations that take into account not only sensory cues but also actions and time.

大脳皮質視覚関連領野を中心としたマルチプローブ・マルチニューロン計測技術について

Multiple single-unit recordings with multichannel electrodes are becoming the standard method for elucidating the mechanism driving information processing in the brain. In this paper, we provide an overview of the history of electrophysiological recording techniques, beginning from single-unit recordings with single-channel electrodes to multiple single-unit recordings with multi-channel electrodes. We also introduce our current research based on a multiple single-unit recording technique applied to visual cortical areas. The current status and future challenges for multiple single-unit recording techniques are also considered.

Attracting states in anterior cingulate cortex populations associated with decision making: Altered dynamics when targeting dopamine system with d-Amphetamine

Attracting states in anterior cingulate cortex populations associated with decision making: Altered dynamics when targeting dopamine system with d-Amphetamine

Statistical Characterization of Single Unit Activity in the Prefrontal Cortex In Vivo

Event Abstract Back to Event Statistical Characterization of Single Unit Activity in the Prefrontal Cortex In Vivo Claudio S. Quiroga Lombard1*, Joachim Hass1, Christopher Lapish2, James Hyman3, Jeremy Seamans3 and Daniel Durstewitz1 1 Bernstein-Center for Computational Neuroscience Heidelberg-Mannheim, Central Institute of Mental Health, Heidelberg University, Computational Neuroscience, Germany 2 Indiana University Purdue University Indianapolis, Department of Psychology, Stark Neuroscience Institute, Indianapolis, United States 3 Brain Research Centre, Psychiatry, Faculty of Medicine, University of British Columbia, Vancouver, Canada The mammalian prefrontal cortex (PFC) is centrally involved in higher cognitive functions such as working memory, decision making, or cognitive flexibility. Malfunctioning of this system or the neuromodulatory inputs into it have been associated with the severe cognitive symptoms experienced in schizophrenia, attention-deficit hyperactivity disorder, and other psychiatric conditions. To prepare the construction of a physiologically valid network model of the PFC, we have carried out a detailed statistical analysis of neural activity from a set of in-vivo recordings from the PFC of behaving rats. Our analyses are based on multiple single-unit recordings obtained with multi-electrode arrays or tetrodes in various behavioral settings. In particular, the distributions of inter-spike intervals (ISI), statistics that capture local variations in spike trains (Lv) [1, 2], and neural interaction statistics, like Pearson-type cross-correlations [3,4], were examined. These analyses were performed both on selected time segments adhering to statistical criteria for weak stationarity (ST), and on the data sets as a whole without enforcing any stationarity conditions (non-ST). Since the examined data sets come from behavioral tasks with a lot of stimulus-, movement-, or cognition-related activity, the spike trains could be expected to strongly violate stationarity assumptions. Performing the analyses under both ST and non-ST conditions thus allowed to estimate the impact non-stationarities would have on the estimated quantities and distributions. Here we also introduce a method for assessing stationarity empirically. We found that many of the obtained distributional properties were robust with regards to violation of weak stationarity, at least with the amount of data available in the present study. Two of those properties are of particular interest, namely a power law relationship for the distribution of mean ISIs with a slope of approximately -1.5, and a clearly bimodal Lv-distribution suggesting the presence of at least two profoundly different spiking patterns in PFC. In contrast, cross-correlograms appeared quite sensitive to violation of stationarity conditions as reported previously [5]. The distribution of cross-correlations also appeared to have long tails, but did not seem to exhibit power-law behavior. The analyzed statistical characteristics should provide a number of important constraints for fitting connectivity and synaptic properties of PFC network models.

Local spindling in thalamus during cortical slow oscillation

Event Abstract Back to Event Local spindling in thalamus during cortical slow oscillation P. Barthó1, A. Slézia1, F. Mátyás1, I. Ulbert2 and L. Acsády1* 1 Hung. Acad. Sci.,, Institute of Experimental Medicine, Hungary 2 Hung. Acad. Sci.,, Institute of Psychology, Hungary Despite being the main input and output of the neocortex, the relationship of thalamus during cortical oscillations is not yet fully explored. To investigate this, we performed simultaneous multiple single-unit recordings in rats under urethane anesthesia in the ventral posteromedial nucleus (VPM) and the S1 cortex with four-shank silicon probes. In the VPM two types of spikes were recorded. Wide spikes showed the characteristic burst pattern of relay cells, while narrow spikes fired longer and slower bursts and displayed asymmetric cross-correlograms with wide spikes. During thalamic spindle episodes the two types of spikes fired at different phases, wide spikes preceding narrow spikes by ~25 ms. Most of the VPM multiunit activity was organized into spindle episodes, which only occasionally coincided with cortical spindles. These thalamic spindles were usually restricted to one or two electrode shanks. Both types of units were phase locked to spindles. Considering that VPM contains only relay cells, we propose that narrow spikes belong to axon terminals originating in the nRt. To prove this, we performed simultaneous juxtacell recording of the nRt soma, and axon terminal in VPM. Our data show that network activity in VPM is organized into spatially restricted spindle episodes, indicating that cortical control over VPM is not very, and VMP-nRt interaction dominates instead. Keywords: Neurophysiology, Neuroscience Conference: 13th Conference of the Hungarian Neuroscience Society (MITT), Budapest, Hungary, 20 Jan - 22 Jan, 2011. Presentation Type: Abstract Topic: Neurophysiology Citation: Barthó P, Slézia A, Mátyás F, Ulbert I and Acsády L (2011). Local spindling in thalamus during cortical slow oscillation. Front. Neurosci. Conference Abstract: 13th Conference of the Hungarian Neuroscience Society (MITT). doi: 10.3389/conf.fnins.2011.84.00093 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 03 Mar 2011; Published Online: 23 Mar 2011. * Correspondence: Dr. L. Acsády, Hung. Acad. Sci.,, Institute of Experimental Medicine, Budapest, Hungary, acsady@koki.hu Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers P. Barthó A. Slézia F. Mátyás I. Ulbert L. Acsády Google P. Barthó A. Slézia F. Mátyás I. Ulbert L. Acsády Google Scholar P. Barthó A. Slézia F. Mátyás I. Ulbert L. Acsády PubMed P. Barthó A. Slézia F. Mátyás I. Ulbert L. Acsády Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

Estimating the contribution of assembly activity to cortical dynamics from spike and population measures

The hypothesis that cortical networks employ the coordinated activity of groups of neurons, termed assemblies, to process information is debated. Results from multiple single-unit recordings are not conclusive because of the dramatic undersampling of the system. However, the local field potential (LFP) is a mesoscopic signal reflecting synchronized network activity. This raises the question whether the LFP can be employed to overcome the problem of undersampling. In a recent study in the motor cortex of the awake behaving monkey based on the locking of coincidences to the LFP we determined a lower bound for the fraction of spike coincidences originating from assembly activation. This quantity together with the locking of single spikes leads to a lower bound for the fraction of spikes originating from any assembly activity. Here we derive a statistical method to estimate the fraction of spike synchrony caused by assemblies—not its lower bound—from the spike data alone. A joint spike and LFP surrogate data model demonstrates consistency of results and the sensitivity of the method. Combining spike and LFP signals, we obtain an estimate of the fraction of spikes resulting from assemblies in the experimental data.