Propagating population activity patterns during spontaneous slow waves in the thalamus of rodents.

Slow waves (SWs) represent the most prominent electrophysiological events in the thalamocortical system under anesthesia and during deep sleep. Recent studies have revealed that SWs have complex spatiotemporal dynamics and propagate across neocortical regions. However, it is still unclear whether neuronal activity in the thalamus exhibits similar propagation properties during SWs. Here, we report propagating population activity in the thalamus of ketamine/xylazine-anesthetized rats and mice visualized by high-density silicon probe recordings. In both rodent species, propagation of spontaneous thalamic activity during up-states was most frequently observed in dorsal thalamic nuclei such as the higher order posterior (Po), lateral posterior (LP) or laterodorsal (LD) nuclei. The preferred direction of thalamic activity spreading was along the dorsoventral axis, with over half of the up-states exhibiting a gradual propagation in the ventral-to-dorsal direction. Furthermore, simultaneous neocortical and thalamic recordings collected under anesthesia demonstrated that there is a weak but noticeable interrelation between propagation patterns observed during cortical up-states and those displayed by thalamic population activity. In addition, using chronically implanted silicon probes, we detected propagating activity patterns in the thalamus of naturally sleeping rats during slow-wave sleep. However, in comparison to propagating up-states observed under anesthesia, these propagating patterns were characterized by a reduced rate of occurrence and a faster propagation speed. Our findings suggest that the propagation of spontaneous population activity is an intrinsic property of the thalamocortical network during synchronized brain states such as deep sleep or anesthesia. Additionally, our data implies that the neocortex may have partial control over the formation of propagation patterns within the dorsal thalamus under anesthesia.

DREDge: robust motion correction for high-density extracellular recordings across species.

High-density microelectrode arrays (MEAs) have opened new possibilities for systems neuroscience in human and non-human animals, but brain tissue motion relative to the array poses a challenge for downstream analyses, particularly in human recordings. We introduce DREDge (Decentralized Registration of Electrophysiology Data), a robust algorithm which is well suited for the registration of noisy, nonstationary extracellular electrophysiology recordings. In addition to estimating motion from spikes in the action potential (AP) frequency band, DREDge enables automated tracking of motion at high temporal resolution in the local field potential (LFP) frequency band. In human intraoperative recordings, which often feature fast (period <1s) motion, DREDge correction in the LFP band enabled reliable recovery of evoked potentials, and significantly reduced single-unit spike shape variability and spike sorting error. Applying DREDge to recordings made during deep probe insertions in nonhuman primates demonstrated the possibility of tracking probe motion of centimeters across several brain regions while simultaneously mapping single unit electrophysiological features. DREDge reliably delivered improved motion correction in acute mouse recordings, especially in those made with an recent ultra-high density probe. We also implemented a procedure for applying DREDge to recordings made across tens of days in chronic implantations in mice, reliably yielding stable motion tracking despite changes in neural activity across experimental sessions. Together, these advances enable automated, scalable registration of electrophysiological data across multiple species, probe types, and drift cases, providing a stable foundation for downstream scientific analyses of these rich datasets.

The medial septum controls hippocampal supra-theta oscillations.

Hippocampal theta oscillations orchestrate faster beta-to-gamma oscillations facilitating the segmentation of neural representations during navigation and episodic memory. Supra-theta rhythms of hippocampal CA1 are coordinated by local interactions as well as inputs from the entorhinal cortex (EC) and CA3 inputs. However, theta-nested gamma-band activity in the medial septum (MS) suggests that the MS may control supra-theta CA1 oscillations. To address this, we performed multi-electrode recordings of MS and CA1 activity in rodents and found that MS neuron firing showed strong phase-coupling to theta-nested supra-theta episodes and predicted changes in CA1 beta-to-gamma oscillations on a cycle-by-cycle basis. Unique coupling patterns of anatomically defined MS cell types suggested that indirect MS-to-CA1 pathways via the EC and CA3 mediate distinct CA1 gamma-band oscillations. Optogenetic activation of MS parvalbumin-expressing neurons elicited theta-nested beta-to-gamma oscillations in CA1. Thus, the MS orchestrates hippocampal network activity at multiple temporal scales to mediate memory encoding and retrieval.

Synaptic alterations and neuronal firing in human epileptic neocortical excitatory networks.

Epilepsy is a prevalent neurological condition, with underlying neuronal mechanisms involving hyperexcitability and hypersynchrony. Imbalance between excitatory and inhibitory circuits, as well as histological reorganization are relatively well-documented in animal models or even in the human hippocampus, but less is known about human neocortical epileptic activity. Our knowledge about changes in the excitatory signaling is especially scarce, compared to that about the inhibitory cell population. This study investigated the firing properties of single neurons in the human neocortex in vitro, during pharmacological blockade of glutamate receptors, and additionally evaluated anatomical changes in the excitatory circuit in tissue samples from epileptic and non-epileptic patients. Both epileptic and non-epileptic tissues exhibited spontaneous population activity (SPA), NMDA receptor antagonization reduced SPA recurrence only in epileptic tissue, whereas further blockade of AMPA/kainate receptors reversibly abolished SPA emergence regardless of epilepsy. Firing rates did not significantly change in excitatory principal cells and inhibitory interneurons during pharmacological experiments. Granular layer (L4) neurons showed an increased firing rate in epileptic compared to non-epileptic tissue. The burstiness of neurons remained unchanged, except for that of inhibitory cells in epileptic recordings, which decreased during blockade of glutamate receptors. Crosscorrelograms computed from single neuron discharge revealed both mono- and polysynaptic connections, particularly involving intrinsically bursting principal cells. Histological investigations found similar densities of SMI-32-immunopositive long-range projecting pyramidal cells in both groups, and shorter excitatory synaptic active zones with a higher proportion of perforated synapses in the epileptic group. These findings provide insights into epileptic modifications from the perspective of the excitatory system and highlight discrete alterations in firing patterns and synaptic structure. Our data suggest that NMDA-dependent glutamatergic signaling, as well as the excitatory synaptic machinery are perturbed in epilepsy, which might contribute to epileptic activity in the human neocortex.

Concurrent and Prospective Associations of Reward Response with Affective and Alcohol Problems: ADHD-Related Differential Vulnerability.

Attention-deficit/hyperactivity disorder (ADHD) is a heterogeneous disorder. Data on the role of transdiagnostic, intermediate phenotypes in ADHD-relevant characteristics and outcomes are needed to advance conceptual understanding and approaches to precision psychiatry. Specifically, the extent to which the association between neural response to reward and ADHD-associated affective, externalizing, internalizing, and substance use problems differ depending on ADHD status is unknown. Aims were to examine, in 129 adolescents, whether concurrent and prospective associations of fMRI-measured initial response to reward attainment (relative to loss) with affectivity and externalizing, internalizing, and alcohol use problems differs between youth at-risk for (i.e., subclinical) (n = 50) and not at-risk for ADHD. Adolescents were, on average, 15.29 years old (SD = 1.00; 38% female), 50 were at-risk for (Mage = 15.18 years, SD = 1.04; 22% female) and 79 not at-risk for (Mage = 15.37 years, SD = 0.98; 48.1% female) ADHD. Both concurrent and prospective relations differed given ADHD risk: across analyses, in at-risk youth, greater superior frontal gyrus response was associated with lower concurrent depressive problems but in not at-risk youth, these characteristics were not related. Controlling for baseline use, in at-risk youth, greater putamen response was associated with greater 18-month hazardous alcohol use, whereas in not at-risk youth, greater putamen response was associated with lower use. Where in brain and for which outcomes modulate (direction of) observed relations: superior frontal gyrus response is relevant for depressive problems whereas putamen response is relevant for alcohol problems and greater neural responsivity is linked to less depressive but to more alcohol problems in adolescents at-risk for ADHD and less alcohol problems in adolescents not at-risk. Differences in neural response to reward differentially confer vulnerability for adolescent depressive and alcohol problems depending on ADHD risk.

The role of superficial and deep layers in the generation of high frequency oscillations and interictal epileptiform discharges in the human cortex.

Describing intracortical laminar organization of interictal epileptiform discharges (IED) and high frequency oscillations (HFOs), also known as ripples. Defining the frequency limits of slow and fast ripples. We recorded potential gradients with laminar multielectrode arrays (LME) for current source density (CSD) and multi-unit activity (MUA) analysis of interictal epileptiform discharges IEDs and HFOs in the neocortex and mesial temporal lobe of focal epilepsy patients. IEDs were observed in 20/29, while ripples only in 9/29 patients. Ripples were all detected within the seizure onset zone (SOZ). Compared to hippocampal HFOs, neocortical ripples proved to be longer, lower in frequency and amplitude, and presented non-uniform cycles. A subset of ripples (≈ 50%) co-occurred with IEDs, while IEDs were shown to contain variable high-frequency activity, even below HFO detection threshold. The limit between slow and fast ripples was defined at 150 Hz, while IEDs' high frequency components form clusters separated at 185 Hz. CSD analysis of IEDs and ripples revealed an alternating sink-source pair in the supragranular cortical layers, although fast ripple CSD appeared lower and engaged a wider cortical domain than slow ripples MUA analysis suggested a possible role of infragranularly located neural populations in ripple and IED generation. Laminar distribution of peak frequencies derived from HFOs and IEDs, respectively, showed that supragranular layers were dominated by slower (< 150 Hz) components. Our findings suggest that cortical slow ripples are generated primarily in upper layers while fast ripples and associated MUA in deeper layers. The dissociation of macro- and microdomains suggests that microelectrode recordings may be more selective for SOZ-linked ripples. We found a complex interplay between neural activity in the neocortical laminae during ripple and IED formation. We observed a potential leading role of cortical neurons in deeper layers, suggesting a refined utilization of LMEs in SOZ localization.

Edge computing on TPU for brain implant signal analysis.

The ever-increasing number of recording sites of silicon-based probes imposes a great challenge for detecting and evaluating single-unit activities in an accurate and efficient manner. Currently separate solutions are available for high precision offline evaluation and separate solutions for embedded systems where computational resources are more limited. We propose a deep learning-based spike sorting system, that utilizes both unsupervised and supervised paradigms to learn a general feature embedding space and detect neural activity in raw data as well as predict the feature vectors for sorting. The unsupervised component uses contrastive learning to extract features from individual waveforms, while the supervised component is based on the MobileNetV2 architecture. One of the key advantages of our system is that it can be trained on multiple, diverse datasets simultaneously, resulting in greater generalizability than previous deep learning-based models. We demonstrate that the proposed model does not only reaches the accuracy of current state-of-art offline spike sorting methods but has the unique potential to run on edge Tensor Processing Units (TPUs), specialized chips designed for artificial intelligence and edge computing. We compare our model performance with state of art solutions on paired datasets as well as on hybrid recordings as well. The herein demonstrated system paves the way to the integration of deep learning-based spike sorting algorithms into wearable electronic devices, which will be a crucial element of high-end brain-computer interfaces.

Adolescent ADHD and electrophysiological reward responsiveness: A machine learning approach to evaluate classification accuracy and prognosis.

We evaluated event-related potential (ERP) indices of reinforcement sensitivity as ADHD biomarkers by examining, in N=306 adolescents (Mage=15.78, SD=1.08), the extent to which ERP amplitude and latency variables measuring reward anticipation and response (1) differentiate, in age- and sex-matched subsamples, (i) youth with vs. without ADHD, (ii) youth at-risk for vs. not at-risk for ADHD, and, in the with ADHD subsample, (iii) youth with the inattentive vs. the hyperactive/impulsive (H/I) and combined presentations. We further examined the extent to which ERP variables (2) predict, in the ADHD subsample, substance use (i) concurrently and (ii) prospectively at 18-month follow-up. Linear support vector machine analyses indicated ERPs weakly differentiate youth with/without (65%) - and at-risk for/not at-risk for (63%) - ADHD but better differentiate ADHD presentations (78%). Regression analyses showed in adolescents with ADHD, ERPs explain a considerable proportion of variance (50%) in concurrent alcohol use and, controlling for concurrent marijuana and tobacco use, explain a considerable proportion of variance (87 and 87%) in, and predict later marijuana and tobacco use. Findings are consistent with the dual-pathway model of ADHD. Results also highlight limitations of a dichotomous, syndromic classification and indicate differences in neural reinforcement sensitivity are a promising ADHD prognostic biomarker.

Seeing beyond the spikes: reconstructing the complete spatiotemporal membrane potential distribution from paired intra- and extracellular recordings.

Although electrophysiologists have been recording intracellular neural activity routinely ever since the ground-breaking work of Hodgkin and Huxley, and extracellular multichannel electrodes have also been used frequently and extensively, a practical experimental method to track changes in membrane potential along a complete single neuron is still lacking. Instead of obtaining multiple intracellular measurements on the same neuron, we propose an alternative method by combining single-channel somatic patch-clamp and multichannel extracellular potential recordings. In this work, we show that it is possible to reconstruct the complete spatiotemporal distribution of the membrane potential of a single neuron with the spatial resolution of an extracellular probe during action potential generation. Moreover, the reconstruction of the membrane potential allows us to distinguish between the two major but previously hidden components of the current source density (CSD) distribution: the resistive and the capacitive currents. This distinction provides a clue to the clear interpretation of the CSD analysis, because the resistive component corresponds to transmembrane ionic currents (all the synaptic, voltage-sensitive and passive currents), whereas capacitive currents are considered to be the main contributors of counter-currents. We validate our model-based reconstruction approach on simulations and demonstrate its application to experimental data obtained in vitro via paired extracellular and intracellular recordings from a single pyramidal cell of the rat hippocampus. In perspective, the estimation of the spatial distribution of resistive membrane currents makes it possible to distiguish between active and passive sinks and sources of the CSD map and the localization of the synaptic input currents, which make the neuron fire. KEY POINTS: A new computational method is introduced to calculate the unbiased current source density distribution on a single neuron with known morphology. The relationship between extracellular and intracellular electric potential is determined via mathematical formalism, and a new reconstruction method is applied to reveal the full spatiotemporal distribution of the membrane potential and the resistive and capacitive current components. The new reconstruction method was validated on simulations. Simultaneous and colocalized whole-cell patch-clamp and multichannel silicon probe recordings were performed from the same pyramidal neuron in the rat hippocampal CA1 region, in vitro. The method was applied in experimental measurements and returned precise and distinctive characteristics of various intracellular phenomena, such as action potential generation, signal back-propagation and the initial dendritic depolarization preceding the somatic action potential.

Sharp-wave ripple doublets induce complex dendritic spikes in parvalbumin interneurons in vivo.

Neuronal plasticity has been shown to be causally linked to coincidence detection through dendritic spikes (dSpikes). We demonstrate the existence of SPW-R-associated, branch-specific, local dSpikes and their computational role in basal dendrites of hippocampal PV+ interneurons in awake animals. To measure the entire dendritic arbor of long thin dendrites during SPW-Rs, we used fast 3D acousto-optical imaging through an eccentric deep-brain adapter and ipsilateral local field potential recording. The regenerative calcium spike started at variable, NMDA-AMPA-dependent, hot spots and propagated in both direction with a high amplitude beyond a critical distance threshold (~150 µm) involving voltage-gated calcium channels. A supralinear dendritic summation emerged during SPW-R doublets when two successive SPW-R events coincide within a short temporal window (~150 ms), e.g., during more complex association tasks, and generated large dSpikes with an about 2.5-3-fold amplitude increase which propagated down to the soma. Our results suggest that these doublet-associated dSpikes can work as a dendritic-level temporal and spatial coincidence detector during SPW-R-related network computation in awake mice.

The sleep EEG envelope is a novel, neuronal firing-based human biomarker.

Sleep EEG reflects voltage differences relative to a reference, while its spectrum reflects its composition of various frequencies. In contrast, the envelope of the sleep EEG reflects the instantaneous amplitude of oscillations, while its spectrum reflects the rhythmicity of the occurrence of these oscillations. The sleep EEG spectrum is known to relate to demographic, psychological and clinical characteristics, but the envelope spectrum has been rarely studied. In study 1, we demonstrate in human invasive data from cortex-penetrating microelectrodes and subdural grids that the sleep EEG envelope spectrum reflects neuronal firing. In study 2, we demonstrate that the scalp EEG envelope spectrum is stable within individuals. A multivariate learning algorithm could predict age (r = 0.6) and sex (r = 0.5) from the EEG envelope spectrum. With age, oscillations shifted from a 4-5 s rhythm to faster rhythms. Our results demonstrate that the sleep envelope spectrum is a promising biomarker of demographic and disease-related phenotypes.

Huygens synchronization of medial septal pacemaker neurons generates hippocampal theta oscillation.

Episodic learning and memory retrieval are dependent on hippocampal theta oscillation, thought to rely on the GABAergic network of the medial septum (MS). To test how this network achieves theta synchrony, we recorded MS neurons and hippocampal local field potential simultaneously in anesthetized and awake mice and rats. We show that MS pacemakers synchronize their individual rhythmicity frequencies, akin to coupled pendulum clocks as observed by Huygens. We optogenetically identified them as parvalbumin-expressing GABAergic neurons, while MS glutamatergic neurons provide tonic excitation sufficient to induce theta. In accordance, waxing and waning tonic excitation is sufficient to toggle between theta and non-theta states in a network model of single-compartment inhibitory pacemaker neurons. These results provide experimental and theoretical support to a frequency-synchronization mechanism for pacing hippocampal theta, which may serve as an inspirational prototype for synchronization processes in the central nervous system from Nematoda to Arthropoda to Chordate and Vertebrate phyla.

From End to End: Gaining, Sorting, and Employing High-Density Neural Single Unit Recordings.

The meaning behind neural single unit activity has constantly been a challenge, so it will persist in the foreseeable future. As one of the most sourced strategies, detecting neural activity in high-resolution neural sensor recordings and then attributing them to their corresponding source neurons correctly, namely the process of spike sorting, has been prevailing so far. Support from ever-improving recording techniques and sophisticated algorithms for extracting worthwhile information and abundance in clustering procedures turned spike sorting into an indispensable tool in electrophysiological analysis. This review attempts to illustrate that in all stages of spike sorting algorithms, the past 5 years innovations' brought about concepts, results, and questions worth sharing with even the non-expert user community. By thoroughly inspecting latest innovations in the field of neural sensors, recording procedures, and various spike sorting strategies, a skeletonization of relevant knowledge lays here, with an initiative to get one step closer to the original objective: deciphering and building in the sense of neural transcript.

Bursting of excitatory cells is linked to interictal epileptic discharge generation in humans.

Knowledge about the activity of single neurons is essential in understanding the mechanisms of synchrony generation, and particularly interesting if related to pathological conditions. The generation of interictal spikes-the hypersynchronous events between seizures-is linked to hyperexcitability and to bursting behaviour of neurons in animal models. To explore its cellular mechanisms in humans we investigated the activity of clustered single neurons in a human in vitro model generating both physiological and epileptiform synchronous events. We show that non-epileptic synchronous events resulted from the finely balanced firing of excitatory and inhibitory cells, which was shifted towards an enhanced excitability in epileptic tissue. In contrast, interictal-like spikes were characterised by an asymmetric overall neuronal discharge initiated by excitatory neurons with the presumptive leading role of bursting pyramidal cells, and possibly terminated by inhibitory interneurons. We found that the overall burstiness of human neocortical neurons is not necessarily related to epilepsy, but the bursting behaviour of excitatory cells comprising both intrinsic and synaptically driven bursting is clearly linked to the generation of epileptiform synchrony.

Reliability of reward ERPs in middle-late adolescents using a custom and a standardized preprocessing pipeline.

Despite advantage of neuroimaging measures in translational research frameworks, less is known about the psychometric properties thereof, especially in middle-late adolescents. Earlier, we examined evidence of convergent and incremental validity of reward anticipation and response event-related potentials (ERPs) and here we examined, in the same sample of 43 adolescents (Mage  = 15.67 years; SD = 1.01; range: 14-18; 32.6% boys), data quality (signal-to-noise ratio [SNR]), stability (mean amplitude across trials), and internal consistency (Cronbach's α and split-half reliability) of the same ERPs. Further, because observed time course and peak amplitude of ERP grand averages and thus findings on SNR, stability, and internal consistency may depend on preprocessing method, we employed a custom and a standardized preprocessing pipeline and compared findings across those. Using our custom pipeline, reward anticipation components were stable by the 40th trial, achieved acceptable internal consistency by the 19th, and all (but the stimulus-preceding negativity [SPN]) achieved acceptable SNR by the 41st trial. Initial response to reward components were stable by the 20th trial and achieved acceptable internal consistency by the 11th and acceptable SNR by the 45th trial. Difference scores had worse psychometric properties than parent measures. Time course and peak amplitudes of ERPs and thus results on SNR, stability, and internal consistency were comparable across preprocessing pipelines. In case of reward anticipation ERPs examined here, 41 trials (+4 artifacted and removed) and, in case of reward response ERPs, 45 trials (+5 artifacted) yielded stable and internally consistent estimates with acceptable SNR. Results are robust across preprocessing methods.

Dataset of cortical activity recorded with high spatial resolution from anesthetized rats.

Publicly available neural recordings obtained with high spatial resolution are scarce. Here, we present an electrophysiological dataset recorded from the neocortex of twenty rats anesthetized with ketamine/xylazine. The wideband, spontaneous recordings were acquired with a single-shank silicon-based probe having 128 densely-packed recording sites arranged in a 32 × 4 array. The dataset contains the activity of a total of 7126 sorted single units extracted from all layers of the cortex. Here, we share raw neural recordings, as well as spike times, extracellular spike waveforms and several properties of units packaged in a standardized electrophysiological data format. For technical validation of our dataset, we provide the distributions of derived single unit properties along with various spike sorting quality metrics. This large collection of in vivo data enables the investigation of the high-resolution electrical footprint of cortical neurons which in turn may aid their electrophysiology-based classification. Furthermore, the dataset might be used to study the laminar-specific neuronal activity during slow oscillation, a brain rhythm strongly involved in neural mechanisms underlying memory consolidation and sleep.

ELVISort: encoding latent variables for instant sorting, an artificial intelligence-based end-to-end solution.

Objective.The growing number of recording sites of silicon-based probes means that an increasing amount of neural cell activities can be recorded simultaneously, facilitating the investigation of underlying complex neural dynamics. In order to overcome the challenges generated by the increasing number of channels, highly automated signal processing tools are needed. Our goal was to build a spike sorting model that can perform as well as offline solutions while maintaining high efficiency, enabling high-performance online sorting.Approach.In this paper we present ELVISort, a deep learning method that combines the detection and clustering of different action potentials in an end-to-end fashion.Main results.The performance of ELVISort is comparable with other spike sorting methods that use manual or semi-manual techniques, while exceeding the methods which use an automatic approach: ELVISort has been tested on three independent datasets and yielded average F1scores of 0.96, 0.82 and 0.81, which comparable with the results of state-of-the-art algorithms on the same data. We show that despite the good performance, ELVISort is capable to process data in real-time: the time it needs to execute the necessary computations for a sample of given length is only 1/15.71 of its actual duration (i.e. the sampling time multiplied by the number of the sampling points).Significance.ELVISort, because of its end-to-end nature, can exploit the massively parallel processing capabilities of GPUs via deep learning frameworks by processing multiple batches in parallel, with the potential to be used on other cutting-edge AI-specific hardware such as TPUs, enabling the development of integrated, portable and real-time spike sorting systems with similar performance to offline sorters.

Microscale Physiological Events on the Human Cortical Surface.

Despite ongoing advances in our understanding of local single-cellular and network-level activity of neuronal populations in the human brain, extraordinarily little is known about their "intermediate" microscale local circuit dynamics. Here, we utilized ultra-high-density microelectrode arrays and a rare opportunity to perform intracranial recordings across multiple cortical areas in human participants to discover three distinct classes of cortical activity that are not locked to ongoing natural brain rhythmic activity. The first included fast waveforms similar to extracellular single-unit activity. The other two types were discrete events with slower waveform dynamics and were found preferentially in upper cortical layers. These second and third types were also observed in rodents, nonhuman primates, and semi-chronic recordings from humans via laminar and Utah array microelectrodes. The rates of all three events were selectively modulated by auditory and electrical stimuli, pharmacological manipulation, and cold saline application and had small causal co-occurrences. These results suggest that the proper combination of high-resolution microelectrodes and analytic techniques can capture neuronal dynamics that lay between somatic action potentials and aggregate population activity. Understanding intermediate microscale dynamics in relation to single-cell and network dynamics may reveal important details about activity in the full cortical circuit.

Recording site placement on planar silicon-based probes affects signal quality in acute neuronal recordings.

Multisite, silicon-based probes are widely used tools to record the electrical activity of neuronal populations. Several physical features of these devices are designed to improve their recording performance. Here, our goal was to investigate whether the position of recording sites on the silicon shank might affect the quality of the recorded neural signal in acute experiments. Neural recordings obtained with five different types of high-density, single-shank, planar silicon probes from anesthetized rats were analyzed. Wideband data were filtered to extract spiking activity, then the amplitude distribution of samples and quantitative properties of the recorded brain activity (single unit yield, spike amplitude and isolation distance) were compared between sites located at different positions of the silicon shank, focusing particularly on edge and center sites. Edge sites outperformed center sites: for all five probe types there was a significant difference in the signal power computed from the amplitude distributions, and edge sites recorded significantly more large amplitude samples both in the positive and negative range. Although the single unit yield was similar between site positions, the difference in spike amplitudes was noticeable in the range corresponding to high-amplitude spikes. Furthermore, the advantage of edge sites slightly decreased with decreasing shank width. Our results might aid the design of novel neural implants in enhancing their recording performance by identifying more efficient recording site placements.