Spiking Activity Of Single Neurons Research Articles

Spatio-temporal transformers for decoding neural movement control.

Deep learning tools applied to high-resolution neurophysiological data have significantly progressed, offering enhanced decoding, real-time processing, and readability for practical applications. However, the design of artificial neural networks to analyze neural activity in vivo remains a challenge, requiring a delicate balance between efficiency in low-data regimes and the interpretability of the results.
Approach:
To address this challenge, we introduce a novel specialized transformer architecture to analyze single-neuron spiking activity. The model is tested on multi-electrode recordings from the dorsal premotor cortex (PMd) of non-human primates performing a motor inhibition task.
Main Results
The proposed architecture provides an early prediction of the correct movement direction, achieving accurate results no later than 230 ms after the Go signal presentation across animals. Additionally, the model can forecast whether the movement will be generated or withheld before a Stop signal, unattended, is actually presented. To further understand the internal dynamics of the model, we compute the predicted correlations between time steps and between neurons at successive layers of the architecture, with the evolution of these correlations mirrors findings from previous theoretical analyses.
Significance
Overall, our framework provides a comprehensive use case for the practical implementation of deep learning tools in motor control research, highlighting both the predictive capabilities and interpretability of the proposed architecture.

Latent dynamics of primary sensory cortical population activity structured by fluctuations in the local field potential.

As emerging technologies enable measurement of precise details of the activity within microcircuits at ever-increasing scales, there is a growing need to identify the salient features and patterns within the neural populations that represent physiologically and behaviorally relevant aspects of the network. Accumulating evidence from recordings of large neural populations suggests that neural population activity frequently exhibits relatively low-dimensional structure, with a small number of variables explaining a substantial fraction of the structure of the activity. While such structure has been observed across the brain, it is not known how reduced-dimension representations of neural population activity relate to classical metrics of "brain state," typically described in terms of fluctuations in the local field potential (LFP), single-cell activity, and behavioral metrics. Hidden state models were fit to spontaneous spiking activity of populations of neurons, recorded in the whisker area of primary somatosensory cortex of awake mice. Classic measures of cortical state in S1, including the LFP and whisking activity, were compared to the dynamics of states inferred from spiking activity. A hidden Markov model fit the population spiking data well with a relatively small number of states, and putative inhibitory neurons played an outsize role in determining the latent state dynamics. Spiking states inferred from the model were more informative of the cortical state than a direct readout of the spiking activity of single neurons or of the population. Further, the spiking states predicted both the trial-by-trial variability in sensory responses and one aspect of behavior, whisking activity. Our results show how classical measurements of brain state relate to neural population spiking dynamics at the scale of the microcircuit and provide an approach for quantitative mapping of brain state dynamics across brain areas.

Multi-scale spiking network model of human cerebral cortex.

Although the structure of cortical networks provides the necessary substrate for their neuronal activity, the structure alone does not suffice to understand the activity. Leveraging the increasing availability of human data, we developed a multi-scale, spiking network model of human cortex to investigate the relationship between structure and dynamics. In this model, each area in one hemisphere of the Desikan-Killiany parcellation is represented by a $1\,\mathrm{mm^{2}}$ column with a layered structure. The model aggregates data across multiple modalities, including electron microscopy, electrophysiology, morphological reconstructions, and diffusion tensor imaging, into a coherent framework. It predicts activity on all scales from the single-neuron spiking activity to the area-level functional connectivity. We compared the model activity with human electrophysiological data and human resting-state functional magnetic resonance imaging (fMRI) data. This comparison reveals that the model can reproduce aspects of both spiking statistics and fMRI correlations if the inter-areal connections are sufficiently strong. Furthermore, we study the propagation of a single-spike perturbation and macroscopic fluctuations through the network. The open-source model serves as an integrative platform for further refinements and future in silico studies of human cortical structure, dynamics, and function.

Single-neuron mechanisms of neural adaptation in the human temporal lobe

A central function of the human brain is to adapt to new situations based on past experience. Adaptation is reflected behaviorally by shorter reaction times to repeating or similar stimuli, and neurophysiologically by reduced neural activity in bulk-tissue measurements with fMRI or EEG. Several potential single-neuron mechanisms have been hypothesized to cause this reduction of activity at the macroscopic level. We here explore these mechanisms using an adaptation paradigm with visual stimuli bearing abstract semantic similarity. We recorded intracranial EEG (iEEG) simultaneously with spiking activity of single neurons in the medial temporal lobes of 25 neurosurgical patients. Recording from 4917 single neurons, we demonstrate that reduced event-related potentials in the macroscopic iEEG signal are associated with a sharpening of single-neuron tuning curves in the amygdala, but with an overall reduction of single-neuron activity in the hippocampus, entorhinal cortex, and parahippocampal cortex, consistent with fatiguing in these areas.

Recording local field potential and neuronal activity with tetrodes in epileptic patients

BackgroundRecordings with tetrodes have proven to be more effective in isolating single neuron spiking activity than with single microwires. However, tetrodes have never been used in humans. We report on the characteristics, safety, compatibility with clinical intracranial recordings in epileptic patients, and performance, of a new type of hybrid electrode equipped with tetrodes. New method240 standard clinical macroelectrodes and 102 hybrid electrodes were implanted in 28 patients. Hybrids (diameter 800 μm) are made of 6 or 9 macro-contacts and 2 or 3 tetrodes (diameter 70−80 μm). ResultsNo clinical complication or adverse event was associated with the hybrids. Impedance and noise of recordings were stable over time. The design enabled multiscale spatial analyses that revealed physiopathological events which were sometimes specific to one tetrode, but could not be recorded on the macro-contacts. After spike sorting, the single-unit yield was similar to other hybrid electrodes and was sometimes as high as >10 neurons per tetrode. Comparison with existing method(s)This new hybrid electrode has a smaller diameter than other available hybrid electrodes. It provides novel spatial information due to the configuration of the tetrodes. The single-unit yield appears promising. ConclusionsThis new hybrid electrode is safe, easy to use, and works satisfactorily for conducting multi-scale seizure and physiological analyses.

Neural effects of transcranial magnetic stimulation at the single-cell level

Transcranial magnetic stimulation (TMS) can non-invasively modulate neural activity in humans. Despite three decades of research, the spatial extent of the cortical area activated by TMS is still controversial. Moreover, how TMS interacts with task-related activity during motor behavior is unknown. Here, we applied single-pulse TMS over macaque parietal cortex while recording single-unit activity at various distances from the center of stimulation during grasping. The spatial extent of TMS-induced activation is remarkably restricted, affecting the spiking activity of single neurons in an area of cortex measuring less than 2 mm in diameter. In task-related neurons, TMS evokes a transient excitation followed by reduced activity, paralleled by a significantly longer grasping time. Furthermore, TMS-induced activity and task-related activity do not summate in single neurons. These results furnish crucial experimental evidence for the neural effects of TMS at the single-cell level and uncover the neural underpinnings of behavioral effects of TMS.

Place cell assemblies remain intact, despite reduced phase precession, after cholinergic disruption.

The hippocampal theta rhythm is frequently viewed as a clocking mechanism that coordinates the spiking activity of neurons across the hippocampus to form coherent neural assemblies. Phase precession is a form of temporal coding evidencing this mechanism and is degraded following systemic pharmacological disruption of cholinergic signaling. However, whether neural assemblies are commensurately degraded, as would be predicted from a clocking mechanism hypothesis, remains unknown. To address this, we recorded the spiking activity of hippocampal place cells as rats completed laps on a circle track for chocolate drink before versus during the influence of a systemic muscarinic acetylcholine receptor antagonist. We compared the integrity of hippocampal ensembles using three approaches. The first approach used cross-correlogram (CCG) analyses to ask if the relative spike-timing between pairs of cells became less reliable. The second used a general linear model based analysis to ask whether the activity of simultaneously recorded neurons became any less predictive of the spiking activity of single neurons. Finally, the third approach used a reconstruction analysis to ask if the population activity was any less informative regarding the environmental position of the animal and whether theta sequences were impaired. The results of all three analyses paint a consistent picture: systemic cholinergic disruption did not degrade assembly integrity. These data demonstrate that place cell assemblies do not depend upon high quality phase precession.

Estimating Multiscale Direct Causality Graphs in Neural Spike-Field Networks

Neural representations span various spatiotemporal scales of brain activity, from the spiking activity of single neurons to field activity measuring large-scale networks. The simultaneous analyses of spikes and fields to uncover causal interactions in multiscale networks could help understand neural mechanisms. However, assessing causality within spike-field networks is challenging as spikes are binary-valued with a fast time-scale while fields are continuous-valued with slower time-scales. Current causality measures are largely not applicable to mixed discrete-continuous network activity. Here, in this paper, we develop a novel multiscale causality estimation algorithm for spike-field networks. We construct a likelihood function comprised of point process models for spikes and linear Gaussian models for fields. For spikes, firing rates are modeled as a function of the history of both field signals and binary spike events within the network. For fields, to make their linear models consistent with biophysical findings, we use the history of field signals and the history of the latent log-firing rates of neurons as predictors. To resolve the challenge of estimating the network model parameters in the presence of latent firing rates, we develop a sequential maximum-likelihood parameter estimation procedure that extends to large networks. Once models are estimated, we compute directed information as our measure of multiscale causality and devise two statistical tests to assess its significance. Using extensive simulations, we show that the algorithm can accurately reconstruct the true causality graphs of random spike-field networks. Moreover, the algorithm is robust to the number of connections, connection strengths, or exact topology of the network. This multiscale causality estimation algorithm has important implications for studying neural mechanisms and for future neurotechnology design.

Statistical Characteristics of the Spike Activity of Neurons in the Midbrain Auditory Center in Frogs on Exposure to Tones Modulated by Low-Frequency Noise

The time parameters of the spike activity of single neurons in the brainstem part of the auditory system carry basic information on the properties of perceived signals. To characterize these parameters, the activity of single neurons in the midbrain center (semicircular torus) of the common frog (Rana temporaria) was studied on exposure to prolonged tones amplitude-modulated with repeating bursts of low-frequency noise. Time-shuffled autocorrelation functions were calculated, these corresponding to the sum of the correlation functions between responses to all bursts presented with the exception of responses evoked by one of the bursts. This approach excludes the effects of the refractory properties of the neuron being studied, allowing the responses of a single neuron to be used to evaluate the nature of the spike activity in the local population of cells with presumptively identical properties but which are statistically independent of each other. Significant variation in the statistical characteristics of output spike activity in different cells in the semicircular toroid was found, such that signals could be characterized in terms of different time properties. Neurons with highly specific reactions to the time characteristics of the envelope were observed.

Low-dimensional spike rate models derived from networks of adaptive integrate-and-fire neurons: Comparison and implementation

The spiking activity of single neurons can be well described by a nonlinear integrate-and-fire model that includes somatic adaptation. When exposed to fluctuating inputs sparsely coupled populations of these model neurons exhibit stochastic collective dynamics that can be effectively characterized using the Fokker-Planck equation. This approach, however, leads to a model with an infinite-dimensional state space and non-standard boundary conditions. Here we derive from that description four simple models for the spike rate dynamics in terms of low-dimensional ordinary differential equations using two different reduction techniques: one uses the spectral decomposition of the Fokker-Planck operator, the other is based on a cascade of two linear filters and a nonlinearity, which are determined from the Fokker-Planck equation and semi-analytically approximated. We evaluate the reduced models for a wide range of biologically plausible input statistics and find that both approximation approaches lead to spike rate models that accurately reproduce the spiking behavior of the underlying adaptive integrate-and-fire population. Particularly the cascade-based models are overall most accurate and robust, especially in the sensitive region of rapidly changing input. For the mean-driven regime, when input fluctuations are not too strong and fast, however, the best performing model is based on the spectral decomposition. The low-dimensional models also well reproduce stable oscillatory spike rate dynamics that are generated either by recurrent synaptic excitation and neuronal adaptation or through delayed inhibitory synaptic feedback. The computational demands of the reduced models are very low but the implementation complexity differs between the different model variants. Therefore we have made available implementations that allow to numerically integrate the low-dimensional spike rate models as well as the Fokker-Planck partial differential equation in efficient ways for arbitrary model parametrizations as open source software. The derived spike rate descriptions retain a direct link to the properties of single neurons, allow for convenient mathematical analyses of network states, and are well suited for application in neural mass/mean-field based brain network models.

Low-dimensional spike rate dynamics of coupled adaptive model neurons

The spiking activity of single neurons can be well described by a two-dimensional integrate-and-fire model that includes neuronal adaptation [1] caused by slowly decaying potassium currents [2]. For fluctuating inputs sparsely coupled spiking model neurons exhibit stochastic population dynamics which can be effectively characterized using the Fokker-Planck equation (see, e.g., [3-5]). This approach leads to a model with an infinite-dimensional state space and non-standard boundary conditions. However, the spike rate dynamics can be approximated by a low-dimensional ordinary differential equation in different ways [4,6,7]. Although these approximation techniques are interrelated it is not clear which reduced model best reproduces the spike rate of the original spiking network, depending on the statistics of the input. Here we first extend each of these reduction methods to account for neuronal adaptation and then evaluate the reduced models in terms of spike rate reproduction accuracy for a range of biologically plausible input statistics, computational demand and implementation complexity (see, e.g., Figure ​Figure1).1). These reduced descriptions are well suited for (i) application in neural mass/mean-field based brain network models, having a link to single neuron properties retained and being computationally efficient, and (ii) mathematical analyses of, e.g., asynchronous and rhythmic network states. Figure 1 Simulation of a large population of adaptive exponential integrate-and-fire (aEIF) neurons driven by a stochastic current with time-varying moments. Instantaneous spike rate and adaptation current averaged over 200,000 neurons are shown in gray. Overlaid ...

Combined Single Neuron Unit Activity and Local Field Potential Oscillations in a Human Visual Recognition Memory Task.

Activities of neuronal networks range from action potential firing of individual neurons, coordinated oscillations of local neuronal assemblies, and distributed neural populations. Here, we describe recordings using hybrid electrodes, containing both micro- and clinical macroelectrodes, to simultaneously sample both large-scale network oscillations and single neuron spiking activity in the medial temporal lobe structures of human subjects during a visual recognition memory task. We quantify and compare single neuron unit activity (SUA) with high-frequency macrofield oscillations (HFOs) for decoding visual images. SUA and HFOs were recorded using hybrid electrodes containing both micro and macroelectrode contacts, implanted in patients with focal epilepsy. Decoding of image properties in different task trials was performed, analyzing SUA and HFO as point processes to capture the dynamics of neurons and their assemblies at different spatiotemporal scales, ranging from submillisecond discharges of single units to fast oscillations across large neuronal populations. Results highlight the limitations and potential complementary use of SUA and HFOs for decoding of general image properties. The dynamics of SUA and HFOs can be used to explore a wide range of neuronal assembly activities engaged in human memory processing. Hybrid electrodes provide a technological bridge for exploring multiscale activity, spanning individual neurons, their assemblies, and large-scale population activity reflected in local field potentials. Analysis of SUA and HFO dynamics as point processes provides a potentially useful signal processing method for exploring the neuronal correlates operating at different spatial scales.

Single-Trial Decoding of Visual Attention from Local Field Potentials in the Primate Lateral Prefrontal Cortex Is Frequency-Dependent.

Local field potentials (LFPs) are fluctuations of extracellular voltage that may reflect the physiological phenomena occurring within a volume of neural tissue. It is known that the allocation of spatial attention modulates the amplitude of LFPs in visual areas of primates. An issue that remains poorly investigated is whether and how attention modulates LFPs in executive brain areas, such as the lateral prefrontal cortex (LPFC), thought to be involved in the origins of attention. We addressed this issue by recording LFPs from multielectrode arrays implanted in the LPFC of two macaques. We found that the allocation of attention can be reliably decoded on a single-trial basis from ensembles of LFPs with frequencies >60 Hz. Using LFP frequencies <60 Hz, we could not decode the allocation of attention, but we could decode the location of a visual stimulus as well as the endpoint of saccades toward that stimulus. The information contained in the high-frequency LFPs was fully redundant with the information contained in the spiking activity of single neurons recorded from the same electrodes. Moreover, the decoding of attention using γ frequency LFPs was less accurate than using spikes, but it was twice more stable across time. Finally, decorrelating the LFP signals from the different electrodes increased decoding performance in the high frequencies by up to ∼14%. Our findings suggest that LFPs recorded from chronically implanted multielectrode arrays in the LPFC contain information about sensory, cognitive, and motor components of a task in a frequency-dependent manner.

Juxtacellular Monitoring and Localization of Single Neurons within Sub-cortical Brain Structures of Alert, Head-restrained Rats.

There are a variety of techniques to monitor extracellular activity of single neuronal units. However, monitoring this activity from deep brain structures in behaving animals remains a technical challenge, especially if the structures must be targeted stereotaxically. This protocol describes convenient surgical and electrophysiological techniques that maintain the animal's head in the stereotaxic plane and unambiguously isolate the spiking activity of single neurons. The protocol combines head restraint of alert rodents, juxtacellular monitoring with micropipette electrodes, and iontophoretic dye injection to identify the neuron location in post-hoc histology. While each of these techniques is in itself well-established, the protocol focuses on the specifics of their combined use in a single experiment. These neurophysiological and neuroanatomical techniques are combined with behavioral monitoring. In the present example, the combined techniques are used to determine how self-generated vibrissa movements are encoded in the activity of neurons within the somatosensory thalamus. More generally, it is straightforward to adapt this protocol to monitor neuronal activity in conjunction with a variety of behavioral tasks in rats, mice, and other animals. Critically, the combination of these methods allows the experimenter to directly relate anatomically-identified neurophysiological signals to behavior.

Development of synaptic onset latency and threshold in auditory cortex in rats

To study the change of synaptic onset latency and threshold in primary auditory cortex (A1) during the development of SD rat. Extracellular recording was used to locate A1, followed by transferred to loose-patch and whole-cell patch in vivo to record the spike activity, synaptic onset latency and threshold responses respectively. Rats were divided into 4 groups according to ages, postnatal 12-15 days, 16-18 days, 19-24 days and adult (> 3 months). 1. The onset latency of local field potential in A1 of adult rats[(10-20)ms] was shorter than young rats[(20-30)ms]. 2. During development, the onset latency of spikes of a single neuron in response to white noise pulses decreased. And the latency in young rats P12-15 [(40.15 ± 2.67) ms] and P16-18 [(33.86 ± 4.61) ms] were longer than in adults [(22.93 ± 2.94) ms] (ANOVA-test, t = 4.330 and 1.995, P = 0.00 and 0.04) . However, the onset latencies of P19-24 [(24.80 ± 3.63) ms] and adult had no significant difference (P > 0.05). 3.Synaptic onset latencies of both excitation and inhibition were significantly longer in P12-15[ (38.94 ± 1.90) ms, (35.26 ± 2.40) ms] and P16-18[ (32.68 ± 2.52) ms, (30.24 ± 2.18) ms] than in adults [(19.4 ± 1.06) ms, (18.91 ± 0.77) ms] excitation (t = 6.255 and 4.662, P < 0.01) inhibition (t = 8.918 and 4.820, P < 0.01) showed significant difference. Whereas the onset latencies of P19-24[ (23.67 ± 2.46) ms, (21.43 ± 1.80) ms] and adults displayed no prominent difference(P > 0.01). Meanwhile, the difference between the onset latencies of excitation and inhibition became narrower during development[ (3.15 ± 1.02) ms, (2.01 ± 0.73) ms, (1.79 ± 0.85) ms, (0.39 ± 0.48) ms]. P12-15 had notably difference in comparison to adults (t = 1.739, P < 0.01). 4. The thresholds of synaptic response were notably higher in P12-15 (40.0 ± 1.6) dB and P16-18 (41.3 ± 11.6) dB when compared with adults (30.9 ± 0.6) dB (t = 5.284 and 5.867, P < 0.01) . While that of P19-24 (35.0 ± 32.7) dB showed no distinct difference (P > 0.01). Single neuron spiking activity, synaptic onset latency and threshold evoked by sound stimulus gradually mature during the development in rat A1.

Tolerance of macaque middle superior temporal sulcus body patch neurons to shape-preserving stimulus transformations.

Tolerance of macaque middle superior temporal sulcus body patch neurons to shape-preserving stimulus transformations.

140 Cognitive Deficits After Traumatic Brain Injury are Associated With Decreased Hippocampal Bursting and Improved With Burst Stimulation of the Fornix

INTRODUCTION: Traumatic brain injury (TBI) can lead to profound deficits of learning and memory that can be a source of considerable morbidity. We investigated hippocampal firing patterns after TBI and the effect of hippocampal theta burst stimulation (TBS) on bursting and cognitive recovery after TBI. METHODS: Twenty-four male Sprague-Dawley rats were used in the study; twelve underwent mild fluid percussion injury (FPI, 1.5 Atm) and were compared to twelve sham-operated rats. Twelve rats (6 TBI, 6 sham) underwent bilateral implantation of electrodes into the CA1 and CA3 hippocampal subfields, and twelve rats (6 TBI, 6 sham) underwent implantation of a stimulating electrode into the fornix. Rats were trained to perform in a delayed nonmatch-to-place swim T-maze. Single neuron busting activity and maze performance were analyzed during task performance 30-90 days after trauma. Theta burst stimulation (TBS, 200 Hz in 50 ms trains, 5 trains per second, 60 μA biphasic pulses) was delivered to the fornix electrodes during task performance in implanted animals. RESULTS: There were no histological differences between control and mTBI rats, and stereological analysis demonstrated no neuronal loss. Rats subjected to mTBI demonstrated significantly poorer performance on the memory task compared with sham rats. Examination of single-neuron spiking activity during exploratory activity revealed that TBI was associated with significantly less hippocampal bursting (P < .05) with a trend toward longer bursts and lower inter-burst spike frequency in the TBI group. Burst stimulation of the fornix normalized cognitive performance in the TBI group to levels indistinguishable from the sham group. CONCLUSION: Deficits in learning and memory after TBI are associated with decreased hippocampal burst activity and are improved with TBS of the hippocampus.

Reward-Modulated Motor Information in Identified Striatum Neurons

It is widely accepted that dorsal striatum neurons participate in either the direct pathway (expressing dopamine D1 receptors) or the indirect pathway (expressing D2 receptors), controlling voluntary movements in an antagonistically balancing manner. The D1- and D2-expressing neurons are activated and inactivated, respectively, by dopamine released from substantia nigra neurons encoding reward expectation. However, little is known about the functional representation of motor information and its reward modulation in individual striatal neurons constituting the two pathways. In this study, we juxtacellularly recorded the spike activity of single neurons in the dorsolateral striatum of rats performing voluntary forelimb movement in a reward-predictable condition. Some of these neurons were identified morphologically by a combination of juxtacellular visualization and in situ hybridization for D1 mRNA. We found that the striatal neurons exhibited distinct functional activations before and during the forelimb movement, regardless of the expression of D1 mRNA. They were often positively, but rarely negatively, modulated by expecting a reward for the correct motor response. The positive reward modulation was independent of behavioral differences in motor performance. In contrast, regular-spiking and fast-spiking neurons in any layers of the motor cortex displayed only minor and unbiased reward modulation of their functional activation in relation to the execution of forelimb movement. Our results suggest that the direct and indirect pathway neurons cooperatively rather than antagonistically contribute to spatiotemporal control of voluntary movements, and that motor information is subcortically integrated with reward information through dopaminergic and other signals in the skeletomotor loop of the basal ganglia.

Signal Multiplexing and Single-Neuron Computations in Lateral Intraparietal Area During Decision-Making

Previous work has revealed a remarkably direct neural correlate of decisions in the lateral intraparietal area (LIP). Specifically, firing rate has been observed to ramp up or down in a manner resembling the accumulation of evidence for a perceptual decision reported by making a saccade into (or away from) the neuron's response field (RF). However, this link between LIP response and decision formation emerged from studies where a saccadic target was always stimulating the RF during decisions, and where the neural correlate was the averaged activity of a restricted sample of neurons. Because LIP cells are (1) highly responsive to the presence of a visual stimulus in the RF, (2) heterogeneous, and (3) not clearly anatomically segregated from large numbers of neurons that fail selection criteria, the underlying neuronal computations are potentially obscured. To address this, we recorded single neuron spiking activity in LIP during a well-studied moving-dot direction-discrimination task and manipulated whether a saccade target was present in the RF during decision-making. We also recorded from a broad sample of LIP neurons, including ones conventionally excluded in prior studies. Our results show that cells multiplex decision signals with decision-irrelevant visual signals. We also observed disparate, repeating response "motifs" across neurons that, when averaged together, resemble traditional ramping decision signals. In sum, neural responses in LIP simultaneously carry decision signals and decision-irrelevant sensory signals while exhibiting diverse dynamics that reveal a broader range of neural computations than previously entertained.

Mapping the spatio-temporal structure of motor cortical LFP and spiking activities during reach-to-grasp movements.

Grasping an object involves shaping the hand and fingers in relation to the object’s physical properties. Following object contact, it also requires a fine adjustment of grasp forces for secure manipulation. Earlier studies suggest that the control of hand shaping and grasp force involve partially segregated motor cortical networks. However, it is still unclear how information originating from these networks is processed and integrated. We addressed this issue by analyzing massively parallel signals from population measures (local field potentials, LFPs) and single neuron spiking activities recorded simultaneously during a delayed reach-to-grasp task, by using a 100-electrode array chronically implanted in monkey motor cortex. Motor cortical LFPs exhibit a large multi-component movement-related potential (MRP) around movement onset. Here, we show that the peak amplitude of each MRP component and its latency with respect to movement onset vary along the cortical surface covered by the array. Using a comparative mapping approach, we suggest that the spatio-temporal structure of the MRP reflects the complex physical properties of the reach-to-grasp movement. In addition, we explored how the spatio-temporal structure of the MRP relates to two other measures of neuronal activity: the temporal profile of single neuron spiking activity at each electrode site and the somatosensory receptive field properties of single neuron activities. We observe that the spatial representations of LFP and spiking activities overlap extensively and relate to the spatial distribution of proximal and distal representations of the upper limb. Altogether, these data show that, in motor cortex, a precise spatio-temporal pattern of activation is involved for the control of reach-to-grasp movements and provide some new insight about the functional organization of motor cortex during reaching and object manipulation.