Neural Ensemble Recordings Research Articles

Spatial maps in piriform cortex during olfactory navigation.

Odors are a fundamental part of the sensory environment used by animals to inform behaviors such as foraging and navigation1,2. Primary olfactory (piriform) cortex is thought to be dedicated to encoding odor identity3-8. Here, using neural ensemble recordings in freely moving rats performing a novel odor-cued spatial choice task, we show that posterior piriform cortex neurons also carry a robust spatial map of the environment. Piriform spatial maps were stable across behavioral contexts independent of olfactory drive or reward availability, and the accuracy of spatial information carried by individual neurons depended on the strength of their functional coupling to the hippocampal theta rhythm. Ensembles of piriform neurons concurrently represented odor identity as well as spatial locations of animals, forming an "olfactory-place map". Our results reveal a previously unknown function for piriform cortex in spatial cognition and suggest that it is well-suited to form odor-place associations and guide olfactory cued spatial navigation.

A robust spike sorting method based on the joint optimization of linear discrimination analysis and density peaks

Spike sorting is a fundamental step in extracting single-unit activity from neural ensemble recordings, which play an important role in basic neuroscience and neurotechnologies. A few algorithms have been applied in spike sorting. However, when noise level or waveform similarity becomes relatively high, their robustness still faces a big challenge. In this study, we propose a spike sorting method combining Linear Discriminant Analysis (LDA) and Density Peaks (DP) for feature extraction and clustering. Relying on the joint optimization of LDA and DP: DP provides more accurate classification labels for LDA, LDA extracts more discriminative features to cluster for DP, and the algorithm achieves high performance after iteration. We first compared the proposed LDA-DP algorithm with several algorithms on one publicly available simulated dataset and one real rodent neural dataset with different noise levels. We further demonstrated the performance of the LDA-DP method on a real neural dataset from non-human primates with more complex distribution characteristics. The results show that our LDA-DP algorithm extracts a more discriminative feature subspace and achieves better cluster quality than previously established methods in both simulated and real data. Especially in the neural recordings with high noise levels or waveform similarity, the LDA-DP still yields a robust performance with automatic detection of the number of clusters. The proposed LDA-DP algorithm achieved high sorting accuracy and robustness to noise, which offers a promising tool for spike sorting and facilitates the following analysis of neural population activity.

In Vivo Neural Interfaces—From Small- to Large-Scale Recording

Brain functions arise from the coordinated activation of neuronal assemblies distributed across multiple brain regions. The electrical potential from the neuron captured by the electrode can be processed to extract brain information. A large number of densely and simultaneously recorded neuronal potential signals from neurons spanning multiple brain regions contribute to the insight of specific behaviors encoded by the neural ensembles. In this review, we focused on the neural interfaces developed for small- to large-scale recordings and discussed the developmental challenges and strategies in microsystem, electrode device, and interface material levels for the future larger-scale neural ensemble recordings.

Spatial maps in piriform cortex during olfactory navigation.

Odours are a fundamental part of the sensory environment used by animals to guide behaviours such as foraging and navigation1,2. Primary olfactory (piriform) cortex is thought to be the main cortical region for encoding odour identity3-8. Here, using neural ensemble recordings in freely moving rats performing an odour-cued spatial choice task, we show that posterior piriform cortex neurons carry a robust spatial representation of the environment. Piriform spatial representations have features of a learned cognitive map, being most prominent near odour ports, stable across behavioural contexts and independent of olfactory drive or reward availability. The accuracy of spatial information carried by individual piriform neurons was predicted by the strength of their functional coupling to the hippocampal theta rhythm. Ensembles of piriform neurons concurrently represented odour identity as well as spatial locations of animals, forming an odour-place map. Our results reveal a function for piriform cortex in spatial cognition and suggest that it is well-suited to form odour-place associations and guide olfactory-cued spatial navigation.

Learning and executing goal-directed choices by internally generated sequences in spiking neural circuits.

Recent neural ensemble recordings have established a link between goal-directed spatial decision making and internally generated neural sequences in the hippocampus of rats. To elucidate the synaptic mechanisms of these sequences underlying spatial decision making processes, we develop and investigate a spiking neural circuit model endowed with a combination of two synaptic plasticity mechanisms including spike-timing dependent plasticity (STDP) and synaptic scaling. In this model, the interplay of the combined synaptic plasticity mechanisms and network dynamics gives rise to neural sequences which propagate ahead of the animals’ decision point to reach goal locations. The dynamical properties of these forward-sweeping sequences and the rates of correct binary choices executed by these sequences are quantitatively consistent with experimental observations; this consistency, however, is lost in our model when only one of STDP or synaptic scaling is included. We further demonstrate that such sequence-based decision making in our network model can adaptively respond to time-varying and probabilistic associations of cues and goal locations, and that our model performs as well as an optimal Kalman filter model. Our results thus suggest that the combination of plasticity phenomena on different timescales provides a candidate mechanism for forming internally generated neural sequences and for implementing adaptive spatial decision making.

Reactivation of Rate Remapping in CA3.

The hippocampus is thought to contribute to episodic memory by creating, storing, and reactivating patterns that are unique to each experience, including different experiences that happen at the same location. Hippocampus can combine spatial and contextual/episodic information using a dual coding scheme known as "global" and "rate" remapping. Global remapping selects which set of neurons can activate at a given location. Rate remapping readjusts the firing rates of this set depending on current experience, thus expressing experience-unique patterns at each location. But can the experience-unique component be retrieved spontaneously? Whereas reactivation of recent, spatially selective patterns in hippocampus is well established, it is never perfect, raising the issue of whether the experiential component might be absent. This question is key to the hypothesis that hippocampus can assist memory consolidation by reactivating and broadcasting experience-specific "index codes" to neocortex. In CA3, global remapping exhibits attractor-like dynamics, whereas rate remapping apparently does not, leading to the hypothesis that only the former can be retrieved associatively and casting doubt on the general consolidation hypothesis. Therefore, we studied whether the rate component is reactivated spontaneously during sleep. We conducted neural ensemble recordings from CA3 while rats ran on a circular track in different directions (in different sessions) and while they slept. It was shown previously that the two directions of running result in strong rate remapping. During sleep, the most recent rate distribution was reactivated preferentially. Therefore, CA3 can retrieve patterns spontaneously that are unique to both the location and the content of recent experience. The hippocampus is required for memory of events and their spatial contexts. The primary correlate of hippocampal activity is location in space, but multiple memories can occur in the same location. To be useful for distinguishing these memories, the hippocampus must be able, not only to express, but also to retrieve both spatial and nonspatial information about events. Whether it can retrieve nonspatial information has been challenged recently. We exposed rats to two different experiences (running in different directions) in the same locations and showed that even the nonspatial components of hippocampal cell firing are reactivated spontaneously during sleep, supporting the conclusion that both types of information about a recent experience can be retrieved.

Mild Blast Events Alter Anxiety, Memory, and Neural Activity Patterns in the Anterior Cingulate Cortex

There is a general interest in understanding of whether and how exposure to emotionally traumatizing events can alter memory function and anxiety behaviors. Here we have developed a novel laboratory-version of mild blast exposure comprised of high decibel bomb explosion sound coupled with strong air blast to mice. This model allows us to isolate the effects of emotionally fearful components from those of traumatic brain injury or bodily injury typical associated with bomb blasts. We demonstrate that this mild blast exposure is capable of impairing object recognition memory, increasing anxiety in elevated O-maze test, and resulting contextual generalization. Our in vivo neural ensemble recording reveal that such mild blast exposures produced diverse firing changes in the anterior cingulate cortex, a region processing emotional memory and inhibitory control. Moreover, we show that these real-time neural ensemble patterns underwent post-event reverberations, indicating rapid consolidation of those fearful experiences. Identification of blast-induced neural activity changes in the frontal brain may allow us to better understand how mild blast experiences result in abnormal changes in memory functions and excessive fear generalization related to post-traumatic stress disorder.

Functional Connectivity Dynamics Among Cortical Neurons: A Dependence Analysis

This paper quantifies and comparatively validates functional connectivity between neurons by measuring the statistical dependence between their firing rates. Based on statistical analysis of the pairwise functional connectivity, we estimate, exclusively from neural data, the neural assembly functional connectivity given a behavior task, which provides a quantifiable representation of the dynamic nature during the behavioral task. Because of the time scale of behavior (100-1000 ms), a statistical method that yields robust estimators for this small sample size is desirable. In this work, the temporal resolutions of four estimators of functional connectivity are compared on both simulated data and real neural ensemble recordings. The comparison highlights how the properties and assumptions of statistical-based and phase-based metrics affect the interpretation of connectivity. Simulation results show that mean square contingency (MSC) and mutual information (MI) create more robust quantification of functional connectivity under identical conditions than cross correlation (CC) and phase synchronization (PhS) when the sample size is 1 s. The results of the simulated analysis are extended to real neuronal recordings to assess the functional connectivity in monkey's cortex corresponding to three movement states in a food reaching task and construct the assembly graph given a movement state and the activation degree of a state-related assembly over time using the statistical test exclusively from neural data dependencies. The activation degree of a given state-related assembly reaches the peak repeatedly when the specific movement states occur, which also reveals the network of interactions among the neurons are key for the operation of a specific behavior.

NeuroQuest: A comprehensive analysis tool for extracellular neural ensemble recordings

Analyzing the massive amounts of neural data collected using microelectrodes to extract biologically relevant information is a major challenge. Many scientific findings rest on the ability to overcome these challenges and to standardize experimental analysis across labs. This can be facilitated in part through comprehensive, efficient and practical software tools disseminated to the community at large. We have developed a comprehensive, MATLAB-based software package – entitled NeuroQuest – that bundles together a number of advanced neural signal processing algorithms in a user-friendly environment. Results demonstrate the efficiency and reliability of the software compared to other software packages, and versatility over a wide range of experimental conditions.

An inexpensive drivable cannulated microelectrode array for simultaneous unit recording and drug infusion in the same brain nucleus of behaving rats

Neurons are functionally segregated into discrete populations that perform specific computations. These computations, mediated by neuron-neuron electrochemical signaling, form the neural basis of behavior. Thus fundamental to a brain-based understanding of behavior is the precise determination of the contribution made by specific neurotransmitters to behaviorally relevant neural activity. To facilitate this understanding, we have developed a cannulated microelectrode array for use in behaving rats that enables simultaneous neural ensemble recordings and local infusion of drugs in the same brain nucleus. The system is inexpensive, easy to use, and produces robust and quantitatively reproducible drug effects on recorded neurons.

Do Electrode Properties Create a Problem in Interpreting Local Field Potential Recordings?

Local field potential (LFP) recordings within the brain have become an important tool used by neuroscientists to make inferences about the activity of a population of cells near an electrode. Each passing year analysis of LFPs in neuroscience seems to bring important new insights on the possible workings of networks in the brain to produce behavior (Buschman and Miller 2007; Canolty et al. 2006; Gregoriou et al. 2009; Liu and Newsome 2006; Lubenov and Siapas 2009; Pesaran et al. 2008; Womelsdorf et al. 2006). Indeed LFPs have become a near-ubiquitous tool in neurophysiology seemingly in use anywhere extracellular spikes are also recorded.

Neural correlates of behavior in the mothManduca sextain response to complex odors

WithManduca sextaas a model system, we analyzed how natural odor mixtures that are most effective in eliciting flight and foraging behaviors are encoded in the primary olfactory center in the brain, the antennal lobe. We used gas chromatography coupled with multiunit neural-ensemble recording to identify key odorants from flowers of two important nectar resources, the desert plantsDatura wrightiiandAgave palmeri, that elicited responses from individual antennal-lobe neurons. Neural-ensemble responses to theA. palmerifloral scent, comprising >60 odorants, could be reproduced by stimulation with a mixture of six of its constituents that had behavioral effectiveness equivalent to that of the complete scent. Likewise, a mixture of three floral volatiles fromD. wrightiielicited normal flight and feeding behaviors. By recording responses of neural ensembles to mixtures of varying behavioral effectiveness, we analyzed the coding of behaviorally “meaningful” odors. We considered four possible ensemble-coding mechanisms—mean firing rate, mean instantaneous firing rate, pattern of synchronous ensemble firing, and total net synchrony of firing—and found that mean firing rate and the pattern of ensemble synchrony were best correlated with behavior (R= 41% and 43%, respectively). Stepwise regression analysis showed that net synchrony and mean instantaneous firing rate contributed little to the variation in the behavioral results. We conclude that a combination of mean-rate coding and synchrony of firing of antennal-lobe neurons underlies generalization among related, behaviorally effective floral mixtures while maintaining sufficient contrast for discrimination of distinct scents.

Characterization and Coding of Behaviorally Significant Odor Mixtures

For animals to execute odor-driven behaviors, the olfactory system must process complex odor signals and maintain stimulus identity in the face of constantly changing odor intensities [1-5]. Surprisingly, how the olfactory system maintains identity of complex odors is unclear [6-10]. We took advantage of the plant-pollinator relationship between the Sacred Datura (Datura wrightii) and the moth Manduca sexta[11, 12] to determine how olfactory networks in this insect's brain represent odor mixtures. We combined gas chromatography and neural-ensemble recording in the moth's antennal lobe to examine population codes for the floral mixture and its fractionated components. Although the floral scent of D. wrightii comprises at least 60 compounds, only nine of those elicited robust neural responses. Behavioral experiments confirmed that these nine odorants mediate flower-foraging behaviors, but only as a mixture. Moreover, the mixture evoked equivalent foraging behaviors over a 1000-fold range in dilution, suggesting a singular percept across this concentration range. Furthermore, neural-ensemble recordings in the moth's antennal lobe revealed that reliable encoding of the floral mixture is organized through synchronized activity distributed across a population of glomerular coding units, and this timing mechanism may bind the features of a complex stimulus into a coherent odor percept.

Identifying functional connectivity in large-scale neural ensemble recordings: a multiscale data mining approach.

Identifying functional connectivity between neuronal elements is an essential first step toward understanding how the brain orchestrates information processing at the single-cell and population levels to carry out biological computations. This letter suggests a new approach to identify functional connectivity between neuronal elements from their simultaneously recorded spike trains. In particular, we identify clusters of neurons that exhibit functional interdependency over variable spatial and temporal patterns of interaction. We represent neurons as objects in a graph and connect them using arbitrarily defined similarity measures calculated across multiple timescales. We then use a probabilistic spectral clustering algorithm to cluster the neurons in the graph by solving a minimum graph cut optimization problem. Using point process theory to model population activity, we demonstrate the robustness of the approach in tracking a broad spectrum of neuronal interaction, from synchrony to rate co-modulation, by systematically varying the length of the firing history interval and the strength of the connecting synapses that govern the discharge pattern of each neuron. We also demonstrate how activity-dependent plasticity can be tracked and quantified in multiple network topologies built to mimic distinct behavioral contexts. We compare the performance to classical approaches to illustrate the substantial gain in performance.

Influence of norepinephrine on somatosensory neuronal responses in the rat thalamus: A combined modeling and in vivo multi-channel, multi-neuron recording study

Norepinephrine released within primary sensory circuits from locus coeruleus afferent fibers can produce a spectrum of modulatory actions on spontaneous or sensory-evoked activity of individual neurons. Within the ventral posterior medial thalamus, membrane currents modulated by norepinephrine have been identified. However, the relationship between the cellular effects of norepinephrine and the impact of norepinephrine release on populations of neurons encoding sensory signals is still open to question. To address this lacuna in understanding the net impact of the noradrenergic system on sensory signal processing, a computational model of the rat trigeminal somatosensory thalamus was generated. The effects of independent manipulation of different cellular actions of norepinephrine on simulated afferent input to the computational model were then examined. The results of these simulations aided in the design of in vivo neural ensemble recording experiments where sensory-driven responses of thalamic neurons were measured before and during locus coeruleus activation in waking animals. Together the simulated and experimental results reveal several key insights regarding the regulation of neural network operation by norepinephrine including: 1) cell-specific modulatory actions of norepinephrine, 2) mechanisms of norepinephrine action that can improve the tuning of the network and increase the signal-to-noise ratio of cellular responses in order to enhance network representation of salient stimulus features and 3) identification of the dynamic range of thalamic neuron function through which norepinephrine operates.

Large-scale neural ensemble recording in the brains of freely behaving mice

With the availability of sophisticated genetic techniques, the mouse is a valuable mammalian model to study the molecular and cellular basis of cognitive behaviors. However, the small size of mice makes it difficult for a systematic investigation of activity patterns of neural networks in vivo. Here we report the development and construction of a high-density ensemble recording array with up to 128-recording channels that can be formatted as single electrodes, stereotrodes, or tetrodes. This high-density recording array is capable of recording from hundreds of individual neurons simultaneously in the hippocampus of the freely behaving mice. This large-scale in vivo ensemble recording techniques, once coupled with mouse genetics, should be valuable to the study of complex relationship between the genes, neural network, and cognitive behaviors.

DATA-MEAns: An open source tool for the classification and management of neural ensemble recordings

The number of laboratories using techniques that allow to acquire simultaneous recordings of as many units as possible is considerably increasing. However, the development of tools used to analyse this multi-neuronal activity is generally lagging behind the development of the tools used to acquire these data. Moreover, the data exchange between research groups using different multielectrode acquisition systems is hindered by commercial constraints such as exclusive file structures, high priced licenses and hard policies on intellectual rights. This paper presents a free open-source software for the classification and management of neural ensemble data. The main goal is to provide a graphical user interface that links the experimental data to a basic set of routines for analysis, visualization and classification in a consistent framework. To facilitate the adaptation and extension as well as the addition of new routines, tools and algorithms for data analysis, the source code and documentation are freely available.

Spatial and Temporal Organization of Ensemble Representations for Different Odor Classes in the Moth Antennal Lobe

In the insect antennal lobe, odor discrimination depends on the ability of the brain to read neural activity patterns across arrays of uniquely identifiable olfactory glomeruli. Less is understood about the complex temporal dynamics and interglomerular interactions that underlie these spatial patterns. Using neural-ensemble recording, we show that the evoked firing patterns within and between groups of glomeruli are odor dependent and organized in both space and time. Simultaneous recordings from up to 15 units per ensemble were obtained from four zones of glomerular neuropil in response to four classes of odorants: pheromones, monoterpenoids, aromatics, and aliphatics. Each odor class evoked a different pattern of excitation and inhibition across recording zones. The excitatory response field for each class was spatially defined, but inhibitory activity was spread across the antennal lobe, reflecting a center-surround organization. Some chemically related odorants were not easily distinguished by their spatial patterns, but each odorant evoked transient synchronous firing across a uniquely different subset of ensemble units. Examination of 535 cell pairs revealed a strong relationship between their recording positions, temporal correlations, and similarity of odor response profiles. These findings provide the first definitive support for a nested architecture in the insect olfactory system that uses both spatial and temporal coordination of firing to encode chemosensory signals. The spatial extent of the representation is defined by a stereotyped focus of glomerular activity for each odorant class, whereas the transient temporal correlations embedded within the ensemble provide a second coding dimension that can facilitate discrimination between chemically similar volatiles.

Conditioned spikes: a simple and fast method to represent rates and temporal patterns in multielectrode recordings

Increasing evidence suggests that the brain utilizes distributed codes that can only be analyzed by simultaneously recording the activity of multiple neurons. This paper introduces a new methodology for studying neural ensemble recordings. The method uses a novel representation to provide complementary information about the stimuli which are contained in the temporal pattern of the spike sequence. By using this procedure, a high correlation of synchronized events with stimuli times is apparent. To quantify the results and to compare the performance of this method against the most traditional raster plot, we have used Fano factor and cross-correlation analysis. Our results suggest that several consecutive spikes from different neurons within an extended time window may encode behaviorally relevant information. We propose that this new representation, in addition to the other approaches currently used (standard raster plots, multivariate statistical methods, neuronal networks, information theory, etc.), can be a useful procedure to describe population spike dynamics.

Coordinated Reactivation of Distributed Memory Traces in Primate Neocortex

Conversion of new memories into a lasting form may involve the gradual refinement and linking together of neural representations stored widely throughout neocortex. This consolidation process may require coordinated reactivation of distributed components of memory traces while the cortex is "offline," i.e., not engaged in processing external stimuli. Simultaneous neural ensemble recordings from four sites in the macaque neocortex revealed such coordinated reactivation. In motor, somatosensory, and parietal cortex (but not prefrontal cortex), the behaviorally induced correlation structure and temporal patterning of neural ensembles within and between regions were preserved, confirming a major tenet of the trace-reactivation theory of memory consolidation.