Biology subjects

Morales-Gregorio, A.

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

4 recordsLinked to original sources

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↗