Biology subjects

Herzog, R.

Publications and source records attributed to Herzog, R..

21 records · Page 2Linked to original sources

bioRxiv2020

A mechanistic model of the neural entropy increaseelicited by psychedelic drugs

Psychedelic drugs, including lysergic acid diethylamide (LSD) and other agonists of the serotonin 2A receptor (5HT2A-R), induce drastic changes in subjective experience, and provide a unique opportunity to study the neurobiological basis of consciousness. One of the most notable neurophysiological signatures of psychedelics, increased entropy in spontaneous neural activity, is thought to be of relevance to the psychedelic experience, encoding both acute alterations in consciousness and mediating long-term effects. However, no clear mechanistic explanation for this entropic phenomenon has been put forward so far. We sought to do this here by building upon a recent whole-brain model of serotonergic neuromodulation, to study the entropic effects of 5HT2A-R activation. Our results reproduce the overall entropy increase observed in previous experiments in vivo, providing the first model-based explanation for this phenomenon. We also found that entropy changes were not uniform across the brain: entropy increased in some regions and decreased in others, suggesting a topographical reconfiguration mediated by 5HT2A-R activation. Interestingly, at the whole-brain level, this reconfiguration was not well explained by 5HT2A-R density, but related closely to the topological properties of the brains anatomical connectivity. These results help us understand the mechanisms underlying the psychedelic state and, more generally, the pharmacological modulation of whole-brain activity.

neuroscience↗

bioRxiv2018

Dimensionality Reduction on Spatio-Temporal Maximum Entropy Models of Spiking Networks

Maximum entropy models (MEM) have been widely used in the last 10 years to characterize the statistics of networks of spiking neurons. A major drawback of this approach is that the number of parameters used in the statistical model increases very fast with the network size, hindering its interpretation and fast computation. Here, we present a novel framework of dimensionality reduction for generalized MEM handling spatio-temporal correlations. This formalism is based on information geometry where a MEM is a point on a large-dimensional manifold. We exploit the geometrical properties of this manifold in order to find a projection on a lower dimensional space that best captures the high-order statistics. This allows us to define a quantitative criterion that we call the \"degree of compressibility\" of the neuronal code. A powerful aspect of this method is that it does not require fitting the model. Indeed, the matrix defining the metric of the manifold is computed directly via the data without parameters fitting. The method is first validated using synthetic data generated by a known statistics. We then analyze a MEM having more parameters than the underlying data statistics and show that our method detects the extra dimensions. We then test it on experimental retinal data. We record retinal ganglion cells (RGC) spiking data using multi-electrode arrays (MEA) under different visual stimuli: spontaneous activity, white noise stimulus, and natural scene. Using our method, we report a dimensionality reduction up to 50% for retinal data. As we show, this is quite a huge reduction compared to a randomly generated spike train, suggesting that the neuronal code, in these experiments, is highly compressible. This additionally shows that the dimensionality reduction depends on the stimuli statistics, supporting the idea that sensory networks adapt to stimuli statistics by modifying the level of redundancy.\n\nAuthor SummaryMaximum entropy models (MEM) have been widely used to characterize the statistics of networks of spiking neurons. However, as the network size increases, the number of model parameters increases rapidly, hindering its interpretation and fast computation. Here, we propose a method to evaluate the dimensionality reduction of MEM, based on the geometrical properties of the manifold best capturing the network high-order statistics. Our method is validated with synthetic data using independent or correlated neural responses. Importantly, we show that dimensionality reduction depends on the stimuli statistics, supporting the idea that sensory networks adapt to stimuli statistics modifying the level of redundancy.

neuroscience↗

bioRxiv2018

Functional Asymmetries between Central and Peripheral Retinal Ganglion Cells in a Diurnal Rodent

The segregated properties of the visual system processing central or peripheral regions of the visual field have been widely studied in the visual cortex and the LGN, but rarely reported in retina. The retina performs complex computational strategies to extract spatial-temporal features that are in coherence with animal behavior and survival. Even if a big effort has been done to functionally characterize different retinal ganglion cell (RGC) types, a clear account of the particular functionality of central and peripheral cells is still missing. Here, using electrophysiological data obtained with a 256-MEA recording system on female diurnal rodent retinas (Octodon degus), we evidenced that peripheral RGCs have larger receptive fields, more sustained, faster and shorter temporal responses and sensitive to higher temporal frequencies with a broader frequency bandwidth than the center. Additionally, we also compared the asymmetries between ON and OFF cell populations present in each region, reporting that these asymmetries are dependent on the eccentricity. Finally, the presence of the asymmetries here reported emphasizes even more the complexity of computational strategies performed by the retina, which could serve as inspiration for the development of artificial visual systems.

neuroscience↗