Tetrodes markedly improve the reliability and yield of multiple single-unit isolation from multi-unit recordings in cat striate cortex

Subjective visual perception: from local processing to emergent phenomena of brain activity

The combination of electrophysiological recordings with ambiguous visual stimulation made possible the detection of neurons that represent the content of subjective visual perception and perceptual suppression in multiple cortical and subcortical brain regions. These neuronal populations, commonly referred to as the neural correlates of consciousness, are more likely to be found in the temporal and prefrontal cortices as well as the pulvinar, indicating that the content of perceptual awareness is represented with higher fidelity in higher-order association areas of the cortical and thalamic hierarchy, reflecting the outcome of competitive interactions between conflicting sensory information resolved in earlier stages. However, despite the significant insights into conscious perception gained through monitoring the activities of single neurons and small, local populations, the immense functional complexity of the brain arising from correlations in the activity of its constituent parts suggests that local, microscopic activity could only partially reveal the mechanisms involved in perceptual awareness. Rather, the dynamics of functional connectivity patterns on a mesoscopic and macroscopic level could be critical for conscious perception. Understanding these emergent spatio-temporal patterns could be informative not only for the stability of subjective perception but also for spontaneous perceptual transitions suggested to depend either on the dynamics of antagonistic ensembles or on global intrinsic activity fluctuations that may act upon explicit neural representations of sensory stimuli and induce perceptual reorganization. Here, we review the most recent results from local activity recordings and discuss the potential role of effective, correlated interactions during perceptual awareness.

Phase-amplitude coupling in rat orbitofrontal cortex discriminates between correct and incorrect decisions during associative learning

Cross-frequency interactions between oscillations in local field potentials (LFPs) are thought to support communication between brain structures by temporally coordinating neural activity. It is unknown, however, whether such interactions differentiate between different levels of performance in decision-making tasks. Here, we investigated theta (4-12 Hz) to gamma (30-100 Hz) phase-amplitude coupling in LFP recordings from rat orbitofrontal cortex. Across subsequent periods of a task in which rats learned to discriminate two odors associated with positive and negative outcomes, theta-to-gamma phase-amplitude coupling (PAC) was highest during the odor-sampling task period that preceded a Go/NoGo decision. This task-dependent modulation could not be explained by changes in oscillatory power and appeared to be time-locked to odor onset, not to the timing of the behavioral response. We found that PAC strength during odor sampling correlated with learning, as indexed by improved performance across trials. Moreover, this increase in PAC magnitude was apparent only on trials with correct Go and NoGo decisions, but not incorrect Go decisions. In addition, we found that PAC preferred coupling phase showed consistency over sessions only for correct, but not incorrect trials. In conclusion, orbitofrontal cortex theta-gamma PAC strength differentiates between different levels of performance in an olfactory decision-making task and may play a role in the generation and utilization of stimulus-based outcome predictions, necessary for adaptive decision-making.

Spike sorting for polytrodes: a divide and conquer approach

In order to determine patterns of neural activity, spike signals recorded by extracellular electrodes have to be clustered (sorted) with the aim of ensuring that each cluster represents all the spikes generated by an individual neuron. Many methods for spike sorting have been proposed but few are easily applicable to recordings from polytrodes which may have 16 or more recording sites. As with tetrodes, these are spaced sufficiently closely that signals from single neurons will usually be recorded on several adjacent sites. Although this offers a better chance of distinguishing neurons with similarly shaped spikes, sorting is difficult in such cases because of the high dimensionality of the space in which the signals must be classified. This report details a method for spike sorting based on a divide and conquer approach. Clusters are initially formed by assigning each event to the channel on which it is largest. Each channel-based cluster is then sub-divided into as many distinct clusters as possible. These are then recombined on the basis of pairwise tests into a final set of clusters. Pairwise tests are also performed to establish how distinct each cluster is from the others. A modified gradient ascent clustering (GAC) algorithm is used to do the clustering. The method can sort spikes with minimal user input in times comparable to real time for recordings lasting up to 45 min. Our results illustrate some of the difficulties inherent in spike sorting, including changes in spike shape over time. We show that some physiologically distinct units may have very similar spike shapes. We show that RMS measures of spike shape similarity are not sensitive enough to discriminate clusters that can otherwise be separated by principal components analysis (PCA). Hence spike sorting based on least-squares matching to templates may be unreliable. Our methods should be applicable to tetrodes and scalable to larger multi-electrode arrays (MEAs).

Laminar and Columnar Structure of Sensory-Evoked Multineuronal Spike Sequences in Adult Rat Barrel Cortex In Vivo

One of the most relevant questions regarding the function of the nervous system is how sensory information is represented in populations of cortical neurons. Despite its importance, the manner in which sensory-evoked activity propagates across neocortical layers and columns has yet not been fully characterized. In this study, we took advantage of the distinct organization of the rodent barrel cortex and recorded with multielectrode arrays simultaneously from up to 74 neurons localized in several functionally identified layers and columns of anesthetized adult Wistar rats in vivo. The flow of activity within neuronal populations was characterized by temporally precise spike sequences, which were repeatedly evoked by single-whisker stimulation. The majority of the spike sequences representing instantaneous responses were led by a subgroup of putative inhibitory neurons in the principal column at thalamo-recipient layers, thus revealing the presence of feedforward inhibition. However, later spike sequences were mainly led by infragranular excitatory neurons in neighboring columns. Although the starting point of the sequences was anatomically confined, their ending point was rather scattered, suggesting that the population responses are structurally dispersed. Our data show for the first time the simultaneous intra- and intercolumnar processing of information at high temporal resolution.

Carbon-nanotube-modified electrodes for highly efficient acute neural recording

Microelectrodes are widely used for monitoring neural activities in various neurobiological studies. The size of the neural electrode is an important factor in determining the signal-to-noise ratio (SNR) of recorded neural signals and, thereby, the recording sensitivity. Here, it is demonstrated that commercial tungsten microelectrodes can be modified with carbon nanotubes (CNTs), resulting in a highly sensitive recording ability. The impedance with the respect to surface area of the CNT-modified electrodes (CNEs) is much less than that of tungsten microelectrodes because of their large electrochemical surface area (ESA). In addition, the noise level of neural signals recorded by CNEs is significantly less. Thus, the SNR is greater than that obtained using tungsten microelectrodes. Importantly, when applied in a mouse brain in vivo, the CNEs can detect action potentials five times more efficiently than tungsten microelectrodes. This technique provides a significant advance in the recording of neural signals, especially in brain regions with sparse neuronal densities.

Tools for resolving functional activity and connectivity within intact neural circuits

Mammalian neural circuits are sophisticated biological systems that choreograph behavioral processes vital for survival. While the inherent complexity of discrete neural circuits has proven difficult to decipher, many parallel methodological developments promise to help delineate the function and connectivity of molecularly defined neural circuits. Here, we review recent technological advances designed to precisely monitor and manipulate neural circuit activity. We propose a holistic, multifaceted approach for unraveling how behavioral states are manifested through the cooperative interactions between discrete neurocircuit elements.

Estimating extracellular spike waveforms from CA1 pyramidal cells with multichannel electrodes

Extracellular (EC) recordings of action potentials from the intact brain are embedded in background voltage fluctuations known as the “local field potential” (LFP). In order to use EC spike recordings for studying biophysical properties of neurons, the spike waveforms must be separated from the LFP. Linear low-pass and high-pass filters are usually insufficient to separate spike waveforms from LFP, because they have overlapping frequency bands. Broad-band recordings of LFP and spikes were obtained with a 16-channel laminar electrode array (silicone probe). We developed an algorithm whereby local LFP signals from spike-containing channel were modeled using locally weighted polynomial regression analysis of adjoining channels without spikes. The modeled LFP signal was subtracted from the recording to estimate the embedded spike waveforms. We tested the method both on defined spike waveforms added to LFP recordings, and on in vivo-recorded extracellular spikes from hippocampal CA1 pyramidal cells in anaesthetized mice. We show that the algorithm can correctly extract the spike waveforms embedded in the LFP. In contrast, traditional high-pass filters failed to recover correct spike shapes, albeit produceing smaller standard errors. We found that high-pass RC or 2-pole Butterworth filters with cut-off frequencies below 12.5 Hz, are required to retrieve waveforms comparable to our method. The method was also compared to spike-triggered averages of the broad-band signal, and yielded waveforms with smaller standard errors and less distortion before and after the spike.

Relationship between the local structure of orientation map and the strength of orientation tuning of neurons in monkey V1: a 2-photon calcium imaging study

A majority of neurons in the monkey primary visual cortex (V1) are tuned to stimulus orientations. Preferred orientations and tuning strengths vary among V1 neurons. The preferred orientation of neurons gradually changes across the cortex with occasional failures of this organization. How V1 neurons are arranged by the strength of orientation tuning and whether neuronal arrangement for tuning strength relates to orientation preference maps remains controversial. In this study, we performed in vivo two-photon calcium imaging in macaque V1 to examine the local spatial organization of orientation tuning at the level of single cells. We recorded fluorescence signals from individual neurons loaded with a calcium-sensitive dye in layer 2 and the uppermost tier of layer 3. The strength of orientation tuning was shared by nearby neurons, and changed across the cortex. The neurons with similar tuning strength were distributed across at least the entire thickness of layer 2. The tuning strength was weaker in regions where neurons exhibited heterogeneous preferred orientations, as compared with regions where neurons shared similar orientation preferences. Nearby direction-selective neurons often shared their preferred directions, although only a few neurons were direction selective in the layers examined. Thus, the orientation tuning strength of V1 neurons is partially predictable from the local structure of orientation map. The weaker orientation tuning we found in regions with heterogeneous orientation preferences suggests that orientation-independent interactions among local populations of V1 neurons play a critical role in determining their orientation tuning.

Multi-unit recording with iridium oxide modified stereotrodes in Drosophila melanogaster

BACKGROUND

Drosophila is a very favorable animal model for the studies of neuroscience. However, it remains a great challenge to employ electrophysiological approaches in Drosophila to study the neuronal assembly dynamics in vivo, partially due to the small size of the Drosophila brain. Small and sensitive microelectrodes for multi-unit recordings are greatly desired.

NEW METHOD

We fabricated micro-scale stereotrodes for electrical recordings in Drosophila melanogaster. The stereotrodes were modified with iridium oxide (IrO2) under a highly controllable deposition procedure to improve their electrochemical properties. Electrical recordings were carried out using the IrO2 stereotrodes to detect spontaneous action potentials and LFPs in vivo.

RESULTS

The IrO2 electrodes exhibited significantly higher capacitance and lower impedance at 1 kHz. Electrical recording with the IrO2 stereotrodes in vivo demonstrated an average signal-to-noise ratio (SNR) of 7.3 and a significantly improved LFP sensitivity. 5 types of different neurons recorded were clearly separated. Electrophysiological responses to visual and odor stimulation were also detected, respectively.

COMPARISON WITH EXISTING METHOD(S)

The most widely used electrodes for electrical recording in Drosophila are glass microelectrode and sharpened tungsten microelectrode, which are typically used for single-unit recordings. Although tetrode technology has been used to record multi-neuronal activities from Drosophila, the fabricated IrO2 stereotrodes possess smaller geometry size but exhibited comparable recording signal-to noise ration and better sorting quality.

CONCLUSIONS

The IrO2 stereotrodes are capable to meet the requirements of multi-unit recording and spike sorting, which will be a useful tool for the electrophysiology-based researches especially in Drosophila and other small animals.

Three floral volatiles contribute to differential pollinator attraction in monkeyflowers (Mimulus)

Flowering plants employ a wide variety of signals, including scent, to attract the attention of pollinators. In this study we investigated the role of floral scent in mediating differential attraction between two species of monkeyflowers (Mimulus) reproductively isolated by pollinator preference. The emission rate and chemical identity of floral volatiles differ between the bumblebee-pollinated Mimulus lewisii and the hummingbird-pollinated M. cardinalis. Mimulus lewisii flowers produce an array of volatiles dominated by d-limonene, β-myrcene and E-β-ocimene. Of these three monoterpenes, M. cardinalis flowers produce only d-limonene, released at just 0.9% the rate of M. lewisii flowers. Using the Bombus vosnesenskii bumblebee, an important pollinator of M. lewisii, we conducted simultaneous gas chromatography with extracellular recordings in the bumblebee antennal lobe. Results from these experiments revealed that these three monoterpenes evoke significant neural responses, and that a synthetic mixture of the three volatiles evokes the same responses as the natural scent. Furthermore, the neural population shows enhanced responses to the M. lewisii scent over the scent of M. cardinalis. This neural response is reflected in behavior; in two-choice assays, bumblebees investigate artificial flowers scented with M. lewisii more frequently than ones scented with M. cardinalis, and in synthetic mixtures the three monoterpenes are necessary and sufficient to recapitulate responses to the natural scent of M. lewisii. In this system, floral scent alone is sufficient to elicit differential visitation by bumblebees, implying a strong role of scent in the maintenance of reproductive isolation between M. lewisii and M. cardinalis.

Physical principles for scalable neural recording

Simultaneously measuring the activities of all neurons in a mammalian brain at millisecond resolution is a challenge beyond the limits of existing techniques in neuroscience. Entirely new approaches may be required, motivating an analysis of the fundamental physical constraints on the problem. We outline the physical principles governing brain activity mapping using optical, electrical, magnetic resonance, and molecular modalities of neural recording. Focusing on the mouse brain, we analyze the scalability of each method, concentrating on the limitations imposed by spatiotemporal resolution, energy dissipation, and volume displacement. Based on this analysis, all existing approaches require orders of magnitude improvement in key parameters. Electrical recording is limited by the low multiplexing capacity of electrodes and their lack of intrinsic spatial resolution, optical methods are constrained by the scattering of visible light in brain tissue, magnetic resonance is hindered by the diffusion and relaxation timescales of water protons, and the implementation of molecular recording is complicated by the stochastic kinetics of enzymes. Understanding the physical limits of brain activity mapping may provide insight into opportunities for novel solutions. For example, unconventional methods for delivering electrodes may enable unprecedented numbers of recording sites, embedded optical devices could allow optical detectors to be placed within a few scattering lengths of the measured neurons, and new classes of molecularly engineered sensors might obviate cumbersome hardware architectures. We also study the physics of powering and communicating with microscale devices embedded in brain tissue and find that, while radio-frequency electromagnetic data transmission suffers from a severe power-bandwidth tradeoff, communication via infrared light or ultrasound may allow high data rates due to the possibility of spatial multiplexing. The use of embedded local recording and wireless data transmission would only be viable, however, given major improvements to the power efficiency of microelectronic devices.

The accuracy and precision of signal source localization with tetrodes

Four-sensor microelectrodes, commonly referred to as tetrodes, have the ability to significantly increase the signal-to-noise ratio of neuronal extracellular recordings. They also provide spatio-temporal information about extracellular action potentials (EAP) which may be used to localize and resolve individual neuronal signal sources. Since the relative position of sensors and neurons whose EAPs are recorded is not known during in vivo experiments, the accuracy and precision of neuronal source localization algorithms remain untested. In this study, electrical signals generated by a stimulator were recorded simultaneously with four recording micropipettes immersed in artificial cerebrospinal fluid. The location of the source was estimated using the multiple signal classification algorithm, with an accuracy and precision of ~4 µm and ~7 µm, respectively. These results suggest that in vivo localization and resolution of individual neuronal sources is feasible.

Hemodynamic and electrophysiological spontaneous low-frequency oscillations in the cortex: directional influences revealed by Granger causality

We used a combined electrophysiological/hemodynamic system to examine low-frequency oscillations (LFOs) in spontaneous neuronal activities (spike trains and local field potentials) and hemodynamic signals (cerebral blood flow) recorded from the anesthetized rat somatosensory and visual cortices. The laser Doppler flowmetry (LDF) probe was tilted slightly to approach the area in which a microelectrode array (MEA) was implanted for simultaneous recordings. Spike trains (STs) were converted into continuous-time rate functions (CRFs) using the ST instantaneous firing rates. LFOs were detected for all three of the components using the multi-taper method (MTM). The frequencies of these LFOs ranged from 0.052 to 0.167 Hz (mean±SD, 0.10±0.026 Hz) for cerebral blood flow (CBF), from 0.027 to 0.26 Hz (mean±SD, 0.12±0.041 Hz) for the CRFs of the STs and from 0.04 to 0.19 Hz (mean±SD, 0.11±0.035 Hz) for local field potentials (LFPs). We evaluated the Granger causal relationships of spontaneous LFOs among CBF, LFPs and CRFs using Granger causality (GC) analysis. Significant Granger causal relationships were observed from LFPs to CBF, from STs to CBF and from LFPs to STs at approximately 0.1 Hz. The present results indicate that spontaneous LFOs exist not only in hemodynamic components but also in neuronal activities of the rat cortex. To the best of our knowledge, the present study is the first to identify Granger causal influences among CBF, LFPs and STs and show that spontaneous LFOs carry important Granger causal influences from neural activities to hemodynamic signals.

A carbon-fiber electrode array for long-term neural recording

OBJECTIVE

Chronic neural recording in behaving animals is an essential method for studies of neural circuit function. However, stable recordings from small, densely packed neurons remains challenging, particularly over time-scales relevant for learning.

APPROACH

We describe an assembly method for a 16-channel electrode array consisting of carbon fibers (<5 µm diameter) individually insulated with Parylene-C and fire-sharpened. The diameter of the array is approximately 26 µm along the full extent of the implant.

MAIN RESULTS

Carbon fiber arrays were tested in HVC (used as a proper name), a song motor nucleus, of singing zebra finches where individual neurons discharge with temporally precise patterns. Previous reports of activity in this population of neurons have required the use of high impedance electrodes on movable microdrives. Here, the carbon fiber electrodes provided stable multi-unit recordings over time-scales of months. Spike-sorting indicated that the multi-unit signals were dominated by one, or a small number of cells. Stable firing patterns during singing confirmed the stability of these clusters over time-scales of months. In addition, from a total of 10 surgeries, 16 projection neurons were found. This cell type is characterized by sparse stereotyped firing patterns, providing unambiguous confirmation of single cell recordings.

SIGNIFICANCE

Carbon fiber electrode bundles may provide a scalable solution for long-term neural recordings of densely packed neurons.

Diverse synchrony of firing reflects diverse cell-assembly coding in the prefrontal cortex

In the present paper, we focus on the coding by cell assemblies in the prefrontal cortex (PFC) and discuss the diversity of the coding, which results in stable and dynamic representations and the processing of various information in that higher brain region. The key activity that reflects cell-assembly coding is the synchrony of the firing of multiple neurons when animals are performing cognitive and memory tasks. First, we introduce some studies that have shown task-related synchrony of neuronal firing in the monkey PFC. These studies have reported fixed and several types of dynamic synchronous firing during working memory, long-term visual memory, and goal selection. The results of these studies have indicated that cell assemblies in the PFC can contribute to both the stability and the dynamics of various types of information. Second, we refer to rat studies and introduce the findings of cellular interactions that contribute to synchrony in working memory, learning-induced changes in synchrony in spatial tasks, and interactions of the PFC and hippocampus in dynamic synchrony. These studies have proposed neuronal mechanisms of cell-assembly coding in the PFC and its critical role in the learning of task demands in problematic situations. Based on the monkey and rat studies, we conclude that cell-assembly coding in the PFC is diverse and has various facets, which allow multipotentiality in the higher brain region. Finally, we discuss the problem of the sizes of cell assembly, how diverse the sizes are in the PFC, and the technical problems in their investigation. We introduce a unique spike-sorting method that can detect small and local cell assemblies that consist of closely neighboring neurons. Then, we describe the findings of our study that showed that the monkey PFC has both small and large cell assemblies, which have different roles in information coding in the working brain.

The flexDrive: an ultra-light implant for optical control and highly parallel chronic recording of neuronal ensembles in freely moving mice

Electrophysiological recordings from ensembles of neurons in behaving mice are a central tool in the study of neural circuits. Despite the widespread use of chronic electrophysiology, the precise positioning of recording electrodes required for high-quality recordings remains a challenge, especially in behaving mice. The complexity of available drive mechanisms, combined with restrictions on implant weight tolerated by mice, limits current methods to recordings from no more than 4-8 electrodes in a single target area. We developed a highly miniaturized yet simple drive design that can be used to independently position 16 electrodes with up to 64 channels in a package that weighs ~2 g. This advance over current designs is achieved by a novel spring-based drive mechanism that reduces implant weight and complexity. The device is easy to build and accommodates arbitrary spatial arrangements of electrodes. Multiple optical fibers can be integrated into the recording array and independently manipulated in depth. Thus, our novel design enables precise optogenetic control and highly parallel chronic recordings of identified single neurons throughout neural circuits in mice.

A model-based spike sorting algorithm for removing correlation artifacts in multi-neuron recordings

We examine the problem of estimating the spike trains of multiple neurons from voltage traces recorded on one or more extracellular electrodes. Traditional spike-sorting methods rely on thresholding or clustering of recorded signals to identify spikes. While these methods can detect a large fraction of the spikes from a recording, they generally fail to identify synchronous or near-synchronous spikes: cases in which multiple spikes overlap. Here we investigate the geometry of failures in traditional sorting algorithms, and document the prevalence of such errors in multi-electrode recordings from primate retina. We then develop a method for multi-neuron spike sorting using a model that explicitly accounts for the superposition of spike waveforms. We model the recorded voltage traces as a linear combination of spike waveforms plus a stochastic background component of correlated Gaussian noise. Combining this measurement model with a Bernoulli prior over binary spike trains yields a posterior distribution for spikes given the recorded data. We introduce a greedy algorithm to maximize this posterior that we call "binary pursuit". The algorithm allows modest variability in spike waveforms and recovers spike times with higher precision than the voltage sampling rate. This method substantially corrects cross-correlation artifacts that arise with conventional methods, and substantially outperforms clustering methods on both real and simulated data. Finally, we develop diagnostic tools that can be used to assess errors in spike sorting in the absence of ground truth.

Parallel processing via a dual olfactory pathway in the honeybee

In their natural environment, animals face complex and highly dynamic olfactory input. Thus vertebrates as well as invertebrates require fast and reliable processing of olfactory information. Parallel processing has been shown to improve processing speed and power in other sensory systems and is characterized by extraction of different stimulus parameters along parallel sensory information streams. Honeybees possess an elaborate olfactory system with unique neuronal architecture: a dual olfactory pathway comprising a medial projection-neuron (PN) antennal lobe (AL) protocerebral output tract (m-APT) and a lateral PN AL output tract (l-APT) connecting the olfactory lobes with higher-order brain centers. We asked whether this neuronal architecture serves parallel processing and employed a novel technique for simultaneous multiunit recordings from both tracts. The results revealed response profiles from a high number of PNs of both tracts to floral, pheromonal, and biologically relevant odor mixtures tested over multiple trials. PNs from both tracts responded to all tested odors, but with different characteristics indicating parallel processing of similar odors. Both PN tracts were activated by widely overlapping response profiles, which is a requirement for parallel processing. The l-APT PNs had broad response profiles suggesting generalized coding properties, whereas the responses of m-APT PNs were comparatively weaker and less frequent, indicating higher odor specificity. Comparison of response latencies within and across tracts revealed odor-dependent latencies. We suggest that parallel processing via the honeybee dual olfactory pathway provides enhanced odor processing capabilities serving sophisticated odor perception and olfactory demands associated with a complex olfactory world of this social insect.

Development of tube tetrodes and a multi-tetrode drive for deep structure electrophysiological recordings in the macaque brain

Understanding the principles that underlie information processing by neuronal networks requires simultaneous recordings from large populations of well isolated single units. Twisted wire tetrodes (TWTs), typically made by winding together four ultrathin wires (diameter: 12-25 μm), are ideally suited for such population recordings. They are advantageous over single electrodes; both with respect to quality of isolation as well as the number of single units isolated and have therefore been used extensively for superficial cortical recordings. However, their limited tensile strength poses a difficulty to their use for recordings in deep brain areas. We therefore developed a method to overcome this limitation and utilize tetrodes for electrophysiological recordings in the inferotemporal cortex of rhesus macaque. We fabricated a novel, stiff tetrode called the tube tetrode (TuTe) and developed a multi-tetrode driving system for advancing up to 5 TuTes through a ball and socket chamber to precise locations in the temporal lobe of a rhesus macaque. The signal quality acquired with TuTes was comparable to conventional TWTs and allowed excellent isolation of multiple single units. We describe here a simple method for constructing TuTes, which requires only standard laboratory equipment. Further, our TuTes can be easily adapted to work with other microdrives commonly used for electrophysiological investigation in the macaque brain and produce minimal damage to the cortex along its path because of their ultrathin diameter. The tetrode development described here could allow studying neuronal populations in deep lying brain structures previously difficult to reach with the current technology.

Nanotools for neuroscience and brain activity mapping

Neuroscience is at a crossroads. Great effort is being invested into deciphering specific neural interactions and circuits. At the same time, there exist few general theories or principles that explain brain function. We attribute this disparity, in part, to limitations in current methodologies. Traditional neurophysiological approaches record the activities of one neuron or a few neurons at a time. Neurochemical approaches focus on single neurotransmitters. Yet, there is an increasing realization that neural circuits operate at emergent levels, where the interactions between hundreds or thousands of neurons, utilizing multiple chemical transmitters, generate functional states. Brains function at the nanoscale, so tools to study brains must ultimately operate at this scale, as well. Nanoscience and nanotechnology are poised to provide a rich toolkit of novel methods to explore brain function by enabling simultaneous measurement and manipulation of activity of thousands or even millions of neurons. We and others refer to this goal as the Brain Activity Mapping Project. In this Nano Focus, we discuss how recent developments in nanoscale analysis tools and in the design and synthesis of nanomaterials have generated optical, electrical, and chemical methods that can readily be adapted for use in neuroscience. These approaches represent exciting areas of technical development and research. Moreover, unique opportunities exist for nanoscientists, nanotechnologists, and other physical scientists and engineers to contribute to tackling the challenging problems involved in understanding the fundamentals of brain function.

A detailed and fast model of extracellular recordings

We present a novel method to generate realistic simulations of extracellular recordings. The simulations were obtained by superimposing the activity of neurons placed randomly in a cube of brain tissue. Detailed models of individual neurons were used to reproduce the extracellular action potentials of close-by neurons. To reduce the computational load, the contributions of neurons further away were simulated using previously recorded spikes with their amplitude normalized by the distance to the recording electrode. For making the simulations more realistic, we also considered a model of a finite-size electrode by averaging the potential along the electrode surface and modeling the electrode-tissue interface with a capacitive filter. This model allowed studying the effect of the electrode diameter on the quality of the recordings and how it affects the number of identified neurons after spike sorting. Given that not all neurons are active at a time, we also generated simulations with different ratios of active neurons and estimated the ratio that matches the signal-to-noise values observed in real data. Finally, we used the model to simulate tetrode recordings.

Stimulus-response-outcome coding in the pigeon nidopallium caudolaterale

A prerequisite for adaptive goal-directed behavior is that animals constantly evaluate action outcomes and relate them to both their antecedent behavior and to stimuli predictive of reward or non-reward. Here, we investigate whether single neurons in the avian nidopallium caudolaterale (NCL), a multimodal associative forebrain structure and a presumed analogue of mammalian prefrontal cortex, represent information useful for goal-directed behavior. We subjected pigeons to a go-nogo task, in which responding to one visual stimulus (S+) was partially reinforced, responding to another stimulus (S-) was punished, and responding to test stimuli from the same physical dimension (spatial frequency) was inconsequential. The birds responded most intensely to S+, and their response rates decreased monotonically as stimuli became progressively dissimilar to S+; thereby, response rates provided a behavioral index of reward expectancy. We found that many NCL neurons' responses were modulated in the stimulus discrimination phase, the outcome phase, or both. A substantial fraction of neurons increased firing for cues predicting non-reward or decreased firing for cues predicting reward. Interestingly, the same neurons also responded when reward was expected but not delivered, and could thus provide a negative reward prediction error or, alternatively, signal negative value. In addition, many cells showed motor-related response modulation. In summary, NCL neurons represent information about the reward value of specific stimuli, instrumental actions as well as action outcomes, and therefore provide signals useful for adaptive behavior in dynamically changing environments.

Place cell activation predicts subsequent memory

A major quandary in memory research is how hippocampal place cells, widely recognized as elements of a spatial map, contribute to episodic memory, our capacity to remember unique experiences that depends on hippocampal function. Here we recorded from hippocampal neurons as rats performed a T-maze alternation task in which they were required to remember a preceding experience over a delay in order to make a subsequent spatial choice. As it has been reported previously in other variations of this task, we observed differential firing that predicted correct subsequent choices, even as the animal traversed identical locations prior to the choice. Here we also observed that most place cells also fired differently on correct as compared to error trials. Among these cells, a large majority fired strongly before the delay or during the retrieval phase but were less active or failed to activate when the animal subsequently made an error. These findings join the place cell phenomenon with episodic memory performance dependent on the hippocampus, revealing that memory accuracy can be predicted by the activation of single place cells in the hippocampus.

An unsupervised method for on-chip neural spike detection in multi-electrode recording systems

Emerging multi-electrode-based brain-machine interfaces (BMIs) and large multi-electrode arrays used in in vitro experiments, enable recording of single neuron's activity on multiple electrodes and allow for an in-depth investigation of neural preparations, even at a sub-cellular level. However, the use of these devices entails stringent area and power consumption constraints for the signal-processing hardware units. In addition, the high autonomy of these units and an ability to automatically adapt to changes in the recorded neural preparations is required. Implementing spike detection in close proximity to recording electrodes offers the advantage of reducing the transmission data bandwidth. By eliminating the need of transmitting the full, redundant recordings of neural activity and by transmitting only the spike waveforms or spike times, significant power savings can be achieved in the majority of cases. Here, we present a low-complexity, unsupervised, adaptable, real-time spike-detection method targeting multi-electrode recording devices and compare this method to other spike-detection methods with regard to complexity and performance.

High-density microelectrode array recordings and real-time spike sorting for closed-loop experiments: an emerging technology to study neural plasticity

Understanding plasticity of neural networks is a key to comprehending their development and function. A powerful technique to study neural plasticity includes recording and control of pre- and post-synaptic neural activity, e.g., by using simultaneous intracellular recording and stimulation of several neurons. Intracellular recording is, however, a demanding technique and has its limitations in that only a small number of neurons can be stimulated and recorded from at the same time. Extracellular techniques offer the possibility to simultaneously record from larger numbers of neurons with relative ease, at the expenses of increased efforts to sort out single neuronal activities from the recorded mixture, which is a time consuming and error prone step, referred to as spike sorting. In this mini-review, we describe recent technological developments in two separate fields, namely CMOS-based high-density microelectrode arrays, which also allow for extracellular stimulation of neurons, and real-time spike sorting. We argue that these techniques, when combined, will provide a powerful tool to study plasticity in neural networks consisting of several thousand neurons in vitro.

Laminar dependence of neuronal correlations in visual cortex

Neuronal responses are correlated on a range of timescales. Correlations can affect population coding and may play an important role in cortical function. Correlations are known to depend on stimulus drive, behavioral context, and experience, but the mechanisms that determine their properties are poorly understood. Here we make use of the laminar organization of cortex, with its variations in sources of input, local circuit architecture, and neuronal properties, to test whether networks engaged in similar functions but with distinct properties generate different patterns of correlation. We find that slow timescale correlations are prominent in the superficial and deep layers of primary visual cortex (V1) of macaque monkeys, but near zero in the middle layers. Brief timescale correlation (synchrony), on the other hand, was slightly stronger in the middle layers of V1, although evident at most cortical depths. Laminar variations were also apparent in the power of the local field potential, with a complementary pattern for low frequency (<10 Hz) and gamma (30-50 Hz) power. Recordings in area V2 revealed a laminar dependence similar to V1 for synchrony, but slow timescale correlations were not different between the input layers and nearby locations. Our results reveal that cortical circuits in different laminae can generate remarkably different patterns of correlations, despite being tightly interconnected.

Reward cues in space: commonalities and differences in neural coding by hippocampal and ventral striatal ensembles

Forming place-reward associations critically depends on the integrity of the hippocampal-ventral striatal system. The ventral striatum (VS) receives a strong hippocampal input conveying spatial-contextual information, but it is unclear how this structure integrates this information to invigorate reward-directed behavior. Neuronal ensembles in rat hippocampus (HC) and VS were simultaneously recorded during a conditioning task in which navigation depended on path integration. In contrast to HC, ventral striatal neurons showed low spatial selectivity, but rather coded behavioral task phases toward reaching goal sites. Outcome-predicting cues induced a remapping of firing patterns in the HC, consistent with its role in episodic memory. VS remapped in conjunction with the HC, indicating that remapping can take place in multiple brain regions engaged in the same task. Subsets of ventral striatal neurons showed a "flip" from high activity when cue lights were illuminated to low activity in intertrial intervals, or vice versa. The cues induced an increase in spatial information transmission and sparsity in both structures. These effects were paralleled by an enhanced temporal specificity of ensemble coding and a more accurate reconstruction of the animal's position from population firing patterns. Altogether, the results reveal strong differences in spatial processing between hippocampal area CA1 and VS, but indicate similarities in how discrete cues impact on this processing.

Variability of acute extracellular action potential measurements with multisite silicon probes

Device miniaturization technologies have led to significant advances in sensors for extracellular measurements of electrical activity in the brain. Multisite, silicon-based probes containing implantable electrode arrays afford greater coverage of neuronal activity than single electrodes and therefore potentially offer a more complete view of how neuronal ensembles encode information. However, scaling up the number of sites is not sufficient to ensure capture of multiple neurons, as action potential signals from extracellular electrodes may vary due to numerous factors. In order to understand the large-scale recording capabilities and potential limitations of multisite probes, it is important to quantify this variability, and to determine whether certain key device parameters influence the recordings. Here we investigate the effect of four parameters, namely, electrode surface, width of the structural support shafts, shaft number, and position of the recording site relative to the shaft tip. This study employs acutely implanted silicon probes containing up to 64 recording sites, whose performance is evaluated by the metrics of noise, spike amplitude, and spike detection probability. On average, we find no significant effect of device geometry on spike amplitude and detection probability but we find significant differences among individual experiments, with the likelihood of detecting spikes varying by a factor of approximately three across trials.

Functional organization of envelope-responsive neurons in early visual cortex: organization of carrier tuning properties

It is well established that visual cortex neurons having similar selectivity for orientation, direction of motion, ocular dominance, and other properties of first-order (luminance-defined) stimuli are clustered into a columnar organization. However, the cortical architecture of neuronal responses to second-order (contrast/texture-defined) stimuli is poorly understood. A useful second-order stimulus is a contrast envelope, consisting of a finely detailed pattern (carrier) whose contrast varies on a coarse spatial scale (envelope). In this study, we analyzed the cortical organization of carrier tuning properties of neurons, which responded to contrast-modulated stimuli. We examined whether neurons tuned to similar carrier properties are clustered spatially and whether such spatial clusters are arranged in columns. To address these questions, we recorded single-unit activity, multiunit activity, and local field potentials simultaneously from area 18 of anesthetized cats, using single-channel microelectrodes and multielectrode arrays. Our data showed that neurons tuned to similar carrier spatial frequency are distributed in a highly clustered manner; neurons tuned to similar carrier orientation are also significantly clustered. Neurons along linear arrays perpendicular to the brain surface always exhibited similar optimal carrier spatial frequency, indicating a columnar organization. Multi-pronged tetrode recordings indicated that the diameter of these columns is ≥450 μm. Optimal carrier orientation was also significantly clustered but with finer-grain organization and greater scatter. These results indicate a fine anatomical structure of cortical organization of second-order information processing and suggest that there are probably more maps in cat area 18 than previously believed.

How many neurons can we see with current spike sorting algorithms?

Recent studies highlighted the disagreement between the typical number of neurons observed with extracellular recordings and the ones to be expected based on anatomical and physiological considerations. This disagreement has been mainly attributed to the presence of sparsely firing neurons. However, it is also possible that this is due to limitations of the spike sorting algorithms used to process the data. To address this issue, we used realistic simulations of extracellular recordings and found a relatively poor spike sorting performance for simulations containing a large number of neurons. In fact, the number of correctly identified neurons for single-channel recordings showed an asymptotic behavior saturating at about 8–10 units, when up to 20 units were present in the data. This performance was significantly poorer for neurons with low firing rates, as these units were twice more likely to be missed than the ones with high firing rates in simulations containing many neurons. These results uncover one of the main reasons for the relatively low number of neurons found in extracellular recording and also stress the importance of further developments of spike sorting algorithms.

Action potential waveform variability limits multi-unit separation in freely behaving rats

Extracellular multi-unit recording is a widely used technique to study spontaneous and evoked neuronal activity in awake behaving animals. These recordings are done using either single-wire or mulitwire electrodes such as tetrodes. In this study we have tested the ability of single-wire electrodes to discriminate activity from multiple neurons under conditions of varying noise and neuronal cell density. Using extracellular single-unit recording, coupled with iontophoresis to drive cell activity across a wide dynamic range, we studied spike waveform variability, and explored systematic differences in single-unit spike waveform within and between brain regions as well as the influence of signal-to-noise ratio (SNR) on the similarity of spike waveforms. We also modelled spike misclassification for a range of cell densities based on neuronal recordings obtained at different SNRs. Modelling predictions were confirmed by classifying spike waveforms from multiple cells with various SNRs using a leading commercial spike-sorting system. Our results show that for single-wire recordings, multiple units can only be reliably distinguished under conditions of high recording SNR (≥4) and low neuronal density (≈20,000/ mm³). Physiological and behavioural changes, as well as technical limitations typical of awake animal preparations, reduce the accuracy of single-channel spike classification, resulting in serious classification errors. For SNR <4, the probability of misclassifying spikes approaches 100% in many cases. Our results suggest that in studies where the SNR is low or neuronal density is high, separation of distinct units needs to be evaluated with great caution.

Neural correlates of long-term object memory in the mouse anterior cingulate cortex

Damage to the hippocampal formation results in a profound temporally graded retrograde amnesia, implying that it is necessary for memory acquisition but not its long-term storage. It is therefore thought that memories are transferred from the hippocampus to the cortex for long-term storage in a process called systems consolidation (Dudai and Morris, 2000). Where in the cortex this occurs remains an open question. Recent work (Frankland et al., 2005; Vetere et al., 2011) suggests the anterior cingulate cortex (ACC) as a likely candidate area, but there is little direct electrophysiological evidence to support this claim. Previously, we demonstrated object-associated firing correlates in caudal ACC during tests of recognition memory and described evidence of neuronal responses to where an object had been following a brief delay. However, long-term memory requires evidence of more durable representations. Here we examined the activity of ACC neurons while testing for long-term memory of an absent object. Mice explored two objects in an arena and then were returned 6 h later with one of the objects removed. Mice continued to explore where the object had been, demonstrating memory for that object. Remarkably, some ACC neurons continued to respond where the object had been, while others developed new responses in the absent object's location. The incidence of absent-object responses by ACC neurons was greatly increased with increased familiarization to the objects, and such responses were still evident 1 month later. These data strongly suggest that the ACC contains neural correlates of consolidated object/place association memory.

Flexible optitrode for localized light delivery and electrical recording

We present optitrode, a miniaturized flexible probe for integrated, localized light delivery and electrical recording. This device features an annular light guide with transparent polymer and fused silica layers surrounding a twisted-wire tetrode. We have developed a novel fabrication process, V-groove guided capillary assembly, to achieve high-precision, coaxial alignment of the various layers of the device. Optitrode with a length-to-diameter ratio ∼500 (5 cm long, 100 μm diameter) has been fabricated, and both the electrical and optical functions have been characterized. The prototype can deliver 11% (110 mW) of the total laser power under abrupt bending angle ∼25°.

Applicability of independent component analysis on high-density microelectrode array recordings

Emerging complementary metal oxide semiconductor (CMOS)-based, high-density microelectrode array (HD-MEA) devices provide high spatial resolution at subcellular level and a large number of readout channels. These devices allow for simultaneous recording of extracellular activity of a large number of neurons with every neuron being detected by multiple electrodes. To analyze the recorded signals, spiking events have to be assigned to individual neurons, a process referred to as "spike sorting." For a set of observed signals, which constitute a linear mixture of a set of source signals, independent component (IC) analysis (ICA) can be used to demix blindly the data and extract the individual source signals. This technique offers great potential to alleviate the problem of spike sorting in HD-MEA recordings, as it represents an unsupervised method to separate the neuronal sources. The separated sources or ICs then constitute estimates of single-neuron signals, and threshold detection on the ICs yields the sorted spike times. However, it is unknown to what extent extracellular neuronal recordings meet the requirements of ICA. In this paper, we evaluate the applicability of ICA to spike sorting of HD-MEA recordings. The analysis of extracellular neuronal signals, recorded at high spatiotemporal resolution, reveals that the recorded data cannot be modeled as a purely linear mixture. As a consequence, ICA fails to separate completely the neuronal signals and cannot be used as a stand-alone method for spike sorting in HD-MEA recordings. We assessed the demixing performance of ICA using simulated data sets and found that the performance strongly depends on neuronal density and spike amplitude. Furthermore, we show how postprocessing techniques can be used to overcome the most severe limitations of ICA. In combination with these postprocessing techniques, ICA represents a viable method to facilitate rapid spike sorting of multidimensional neuronal recordings.

Semi-supervised spike sorting using pattern matching and a scaled Mahalanobis distance metric

Sorting action potentials (spikes) from tetrode recordings can be time consuming, labor intensive, and inconsistent, depending on the methods used and the experience of the operator. The techniques presented here were designed to address these issues. A feature related to the slope of the spike during repolarization is computed. A small subsample of the features obtained from the tetrode (ca. 10,000-20,000 events) is clustered using a modified version of k-means that uses Mahalanobis distance and a scaling factor related to the cluster size. The cluster-size-based scaling improves the clustering by increasing the separability of close clusters, especially when they are of disparate size. The full data set is then classified from the statistics of the clusters. The technique yields consistent results for a chosen number of clusters. A MATLAB implementation is able to classify more than 5000 spikes per second on a modern workstation.

A novel tetrode microdrive for simultaneous multi-neuron recording from different regions of primate brain

A unique custom-made tetrode microdrive for recording from large numbers of neurons in several areas of primate brain is described as a means for assessing simultaneous neural activity in cortical and subcortical structures in nonhuman primates (NHPs) performing behavioral tasks. The microdrive device utilizes tetrode technology with up to six ultra-thin microprobe guide tubes (0.1mm) that can be independently positioned, each containing reduced diameter tetrode and/or hexatrode microwires (0.02 mm) for recording and isolating single neuron activity. The microdrive device is mounted within the standard NHP cranial well and allows traversal of brain depths up to 40.0 mm. The advantages of this technology are demonstrated via simultaneously recorded large populations of neurons with tetrode type probes during task performance from a) primary motor cortex and deep brain structures (caudate-putamen and hippocampus) and b) multiple layers within the prefrontal cortex. The means to characterize interactions of well-isolated ensembles of neurons recorded simultaneously from different regions, as shown with this device, has not been previously available for application in primate brain. The device has extensive application to primate models for the detection and study of inoperative or maladaptive neural circuits related to human neurological disorders.

Dipole characterization of single neurons from their extracellular action potentials

The spatial variation of the extracellular action potentials (EAP) of a single neuron contains information about the size and location of the dominant current source of its action potential generator, which is typically in the vicinity of the soma. Using this dependence in reverse in a three-component realistic probe + brain + source model, we solved the inverse problem of characterizing the equivalent current source of an isolated neuron from the EAP data sampled by an extracellular probe at multiple independent recording locations. We used a dipole for the model source because there is extensive evidence it accurately captures the spatial roll-off of the EAP amplitude, and because, as we show, dipole localization, beyond a minimum cell-probe distance, is a more accurate alternative to approaches based on monopole source models. Dipole characterization is separable into a linear dipole moment optimization where the dipole location is fixed, and a second, nonlinear, global optimization of the source location. We solved the linear optimization on a discrete grid via the lead fields of the probe, which can be calculated for any realistic probe + brain model by the finite element method. The global source location was optimized by means of Tikhonov regularization that jointly minimizes model error and dipole size. The particular strategy chosen reflects the fact that the dipole model is used in the near field, in contrast to the typical prior applications of dipole models to EKG and EEG source analysis. We applied dipole localization to data collected with stepped tetrodes whose detailed geometry was measured via scanning electron microscopy. The optimal dipole could account for 96% of the power in the spatial variation of the EAP amplitude. Among various model error contributions to the residual, we address especially the error in probe geometry, and the extent to which it biases estimates of dipole parameters. This dipole characterization method can be applied to any recording technique that has the capabilities of taking multiple independent measurements of the same single units.

Towards reliable spike-train recordings from thousands of neurons with multielectrodes

The new generation of silicon-based multielectrodes comprising hundreds or more electrode contacts offers unprecedented possibilities for simultaneous recordings of spike trains from thousands of neurons. Such data will not only be invaluable for finding out how neural networks in the brain work, but will likely be important also for neural prosthesis applications. This opportunity can only be realized if efficient, accurate and validated methods for automatic spike sorting are provided. In this review we describe some of the challenges that must be met to achieve this goal, and in particular argue for the critical need of realistic model data to be used as ground truth in the validation of spike-sorting algorithms.

Matched subspace detector based feature extraction for sorting of multi-sensor action potentials

This paper proposes a novel matched subspace detector (MSD) based algorithm for extracting discriminant features from multi-sensor measurements of extracellular action potentials (APs) to facilitate their subsequent separation according to the neuron of origin. The method does not require the construction of AP templates, and is therefore suitable for unsupervised AP sorting applications. In addition, detailed simulations show that the proposed algorithm outperforms existing single-sensor based feature extraction approaches.

Signal source localization with tetrodes: experimental verification

Multi-sensor electrodes for extracellular recording of neuronal action potentials have significantly increased the signal-to-noise ratio (SNR) in neurophysiological experiments, ultimately leading to a more accurate interpretation of scientific data. Apart from improving SNR, we hypothesize that these electrodes can be used to estimate the location of underlying neuronal signal sources, and perhaps other parameters such as the size and shape of neurons whose activities are being recorded. This study introduces the multiple signal classification (MUSIC) algorithm to the problem of neuron localization and presents the first experimental demonstration of signal source localization using commercially available 4-sensor electrodes (tetrodes).

Separation of multiunit signals by independent component analysis in complex-valued time-frequency domain

Multiunit recording with a multi-electrode in the brain has been widely used in neuroscience studies. After the data recording, neuronal spikes should be sorted according to spike waveforms. For the spike sorting, independent component analysis (ICA) has recently been used because ICA potentially solves the problem to separate even overlapped multiple neuronal spikes into the single. However, we found that multiunit signals are recorded in each electrode channel with channel-specific delay. This situation does not satisfy the instantaneous mixture condition prerequisite for most of ICA algorithms. Actually, this delayed mixture situation was shown to degrade the performance of an ordinary ICA. In this study, in order to overcome this problem, complex-valued processing in the time-frequency domain is applied to multiunit signals by the wavelet transform. In the space spanned by the wavelet coefficients, the condition of instantaneous mixture is almost fulfilled. By application to a synthetic multiunit signal, the ICA algorithm extended to complex-valued signals makes much improvement in spike sorting performance so that even overlapped multiple spikes are successfully separated. Taken together, the complex-valued method could be a powerful tool for spike sorting.

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.

Optetrode: a multichannel readout for optogenetic control in freely moving mice

Recent advances in optogenetics have improved the precision with which defined circuit elements can be controlled optically in freely moving mammals; in particular, recombinase-dependent opsin viruses, used with a growing pool of transgenic mice expressing recombinases, allow manipulation of specific cell types. However, although optogenetic control has allowed neural circuits to be manipulated in increasingly powerful ways, combining optogenetic stimulation with simultaneous multichannel electrophysiological readout of isolated units in freely moving mice remains a challenge. We designed and validated the optetrode, a device that allows for colocalized multi-tetrode electrophysiological recording and optical stimulation in freely moving mice. Optetrode manufacture employs a unique optical fiber-centric coaxial design approach that yields a lightweight (2 g), compact and robust device that is suitable for behaving mice. This low-cost device is easy to construct (2.5 h to build without specialized equipment). We found that the drive design produced stable high-quality recordings and continued to do so for at least 6 weeks following implantation. We validated the optetrode by quantifying, for the first time, the response of cells in the medial prefrontal cortex to local optical excitation and inhibition, probing multiple different genetically defined classes of cells in the mouse during open field exploration.

Tetrode recordings in the cerebellar cortex

Multi-unit recordings with tetrodes have been used in brain studies for many years, but surprisingly, scarcely in the cerebellum. The cerebellum is subdivided in multiple small functional zones. Understanding the proper features of the cerebellar computations requires a characterization of neuronal activity within each area. By allowing simultaneous recordings of neighboring cells, tetrodes provide a helpful technique to study the dynamics of the cerebellar local networks. Here, we discuss experimental configurations to optimize such recordings and demonstrate their use in the different layers of the cerebellar cortex. We show that tetrodes can also be used to perform simultaneous recordings from neighboring units in freely moving rats using a custom-made drive, thus permitting studies of cerebellar network dynamics in a large variety of behavioral conditions.