Biology subjects

Morales-Gregorio, A.

Publications and source records attributed to Morales-Gregorio, A..

7 recordsLinked to original sources

bioRxiv2026

Cell-type-specific coupling of single-unit spikes and cortical ripples in macaque and human V1

Cortical ripples are brief high-frequency (80-150 Hz) oscillatory bursts in the extracellular potential, associated with memory and attention. So far, it is unclear whether they are brain-state specific and which neuron types participate in their generation. We report cortical ripples in macaque V1 at rest, present with both eyes open and closed. Using massively parallel Utah array recordings, we identify six single-unit classes and describe their relation to ripple-band oscillations. The classes differ in firing-rate modulation, phase-locking strength, and preferred ripple-band phase. One class of putative deep-layer neurons exhibits non-sinusoidal phase locking, explained by a simple thresholding mechanism in a computational model. During ripples, most units fire irregularly with transient regular bursts. The ripple-associated spiking organisation is largely preserved during natural-image viewing and confirmed in V1 of a blind human. Together, these findings suggest cortical ripples arise from a shared circuit conserved across species and conditions.

neuroscience↗

bioRxiv2025

Retinotopy constrains the topology of neural manifolds in macaque visual cortex

Cortical population activity often evolves along low-dimensional manifolds, shaped by external activity, task-demands and the underlying neuronal network structure. The structure of population activity has been studied across many brain areas and species. However, the influence of retinotopic organization in the structure of population activity of the primate visual cortex in vivo remains understudied. Here, we show that retinotopy in primary visual cortex (V1) determines the topological structure in neural population activity in the macaque. We analysed large-scale extracellular recordings from two macaques implanted with 896-channel Utah arrays in V1 performing a receptive field mapping task, with high-contrast moving bars. Isometric Mapping (isomap) applied to the neural population activity revealed a loop-like manifold. Persistent homology on 10-dimensional embeddings confirmed a single dominant one-dimensional loop. To test whether retinotopy alone can account for this structure, we constructed a minimal neural response model and simulated activity with the same stimuli as the macaque neural data. The model reproduced the loop-like topology observed in the brain. Taken together, the experimental and simulated data show that retinotopic maps naturally induce loop-like neural manifolds in response to moving stimuli, directly linking travelling waves and rotational dynamics. Our results provide a basis for further analysis on how neuronal populations encode and process visual information at the population level.

neuroscience↗

bioRxiv2025

Spontaneous spiking statistics form unique area-specific fingerprints and reflect the hierarchy of cerebral cortex

The cerebral cortex is hierarchically organised from sensory to higher cognitive areas1-4. Several dynamical5-8 and anatomical1-4,8 measures, such as timescales and neurotransmitter receptor expression, have independently been linked to the cortical hierarchy. However, a systematic and quantitative characterisation of the relationship between spontaneous spiking activity and the cortical hierarchy remains elusive. Here, we test the hypothesis that single-neuron spontaneous spiking statistics uniquely characterise each cortical area, and that they quantitatively correlate with the cortical hierarchy. We study the spontaneous activity of neurons in seven macaque cortical areas (V1, V4, DP, 7A, M1, PMd, PFC)9-12 in the eyes-open and eyes-closed conditions recorded in a dim-lit room. First, we uncover that the firing rate, inter-spike interval variation, and cross-correlation form a unique fingerprint of the cortical areas, but only when considering them in combination. Second, we show that the differences between the spiking statistics correlate with multiple anatomical markers1,2,4,13-17 of the cortical hierarchy. This effect is much stronger in the eyes-closed condition, suggesting that visual input or the expectation thereof modulates the hierarchical organisation of spontaneous activity. We also observe an increase in timescales up the hierarchy, in agreement with previous findings5,18,19. In conclusion, we demonstrate that spontaneous single-neuron spiking activity reflects the hierarchical organisation of the cerebral cortex: distinct spiking statistics for hierarchically distant areas; similar statistics for nearby areas. Our results thus add a new dynamical dimension to the concept of the cortical hierarchy.

neuroscience↗

bioRxiv2024

Cortical multi-area model with joint excitatory-inhibitory clusters accounts for spiking statistics, inter-area propagation, and variability dynamics

The primate brain uses billions of interacting neurons to produce macroscopic dynamics and behavior, but current methods only allow neuroscientists to investigate a subset of the neural activity. Computational modeling offers an alternative testbed for scientific hypotheses, by allowing full control of the system. Here, we test the hypothesis that local cortical circuits are organized into joint clusters of excitatory and inhibitory neurons by investigating the influence of this organizational principle on cortical resting-state spiking activity, inter-area propagation, and variability dynamics. The model represents all vision-related areas in one hemisphere of the macaque cortex with biologically realistic neuron densities and connectivities, expanding on a previous unclustered model of this system. Each area is represented by a square millimeter microcircuit including the full density of neurons and synapses, avoiding downscaling artifacts and testing cortical dynamics at the natural scale. We find that joint excitatory-inhibitory clustering normalizes spiking activity statistics in terms of firing rate distributions and inter-spike interval variability. A comparison with data from cortical areas V1, V4, FEF, 7a, and DP shows that the clustering enables the resting-state activity of especially higher cortical areas to be better captured. In addition, we find that the clustering supports signal propagation across all areas in both feedforward and feedback directions with reasonable latencies. Finally, we also show that localized stimulation of the clustered model quenches the variability of neural activity, in agreement with experimental observations. We conclude that joint clustering of excitatory and inhibitory neurons is a likely organizational principle of local cortical circuits, supporting resting-state spiking activity statistics, inter-area propagation, and variability dynamics.

neuroscience↗

bioRxiv2024

Detection and Removal of Hyper-synchronous Artifacts in Massively Parallel Spike Recordings

Contemporary electrophysiology experiments often involve massively parallel recordings of neuronal activity using multi-electrode arrays. While researchers have been aware of artifacts arising from electric cross-talk between channels in setups for such recordings, systematic and quantitative assessment of the effects of those artifacts on the data quality has never been reported. Here we present, based on examination of electrophysiology recordings from multiple laboratories, that multi-electrode recordings of spiking activity commonly contain extremely precise (at the data sampling resolution) spike coincidences far above the chance level. We derive, through modeling of the electric cross-talk, a systematic relation between the amount of such hyper-synchronous events (HSEs) in channel pairs and the correlation between the raw signals of those channels in the multi-unit activity frequency range (500-7500 Hz). We show that whitening the band-pass filtered raw signals removes the above chance HSEs; strongly suggesting they originate from linear mixing of signals. Whitening should therefore be performed prior to spike sorting and any further analysis of precise spike correlation, otherwise analysis results may be considerably affected. Significance StatementArtifacts are ubiquitous in electrophysiological recordings. To mitigate their impact, these artifacts need to be detected and they should be removed from the data without impacting the quality of the data. This work presents measures to identify and quantify the amount of artifacts within a multichannel recording by evaluating the occurrence of hyper-synchronous events i.e., spikes that are synchronous on a sub-millisecond time scale, and further introduces zero-phase component analysis (ZCA) as a method to remove these artifacts from the data. Thus, we recommend to use ZCA as a general preprocessing for electrophysiological recordings.

neuroscience↗

bioRxiv2023

Neural manifolds in V1 change with top-down signals from V4 targeting the foveal region

High-dimensional brain activity is often organized into lower-dimensional neural manifolds. However, the neural manifolds of the visual cortex remain understudied. Here, we study large-scale multielectrode electrophysiological recordings of macaque (Macaca mulatta) areas V1, V4 and DP with a high spatio-temporal resolution. We find, for the first time, that the population activity of V1 contains two separate neural manifolds, which correlate strongly with eye closure (eyes open/closed) and have distinct dimensionalities. Moreover, we find strong top-down signals from V4 to V1, particularly to the foveal region of V1, which are significantly stronger during the eyes-open periods, a previously unknown effect. Finally, in silico simulations of a balanced spiking neuron network qualitatively reproduce the experimental findings. Taken together, our analyses and simulations suggest that top-down signals modulate the population activity of V1, causing two distinct neural manifolds. We postulate that the top-down modulation during the eyes-open periods prepares V1 for fast and efficient visual responses, resulting in a type of visual stand-by state.

neuroscience↗

bioRxiv2022

Ubiquitous lognormal distribution of neuron densities across mammalian cerebral cortex

Numbers of neurons and their spatial variation are fundamental organizational features of the brain. Despite the large corpus of cytoarchitectonic data available in the literature, the statistical distributions of neuron densities within and across brain areas remain largely uncharacterized. Here, we show that neuron densities are compatible with a lognormal distribution across cortical areas in several mammalian species, and find that this also holds true within cortical areas. A minimal model of noisy cell division, in combination with distributed proliferation times, can account for the coexistence of lognormal distributions within and across cortical areas. Our findings uncover a new organizational principle of cortical cytoarchitecture: the ubiquitous lognormal distribution of neuron densities, which adds to a long list of lognormal variables in the brain.

neuroscience↗