Biology subjects

Bartho, P.

Publications and source records attributed to Bartho, P..

5 recordsLinked to original sources

bioRxiv2025

Local infrared stimulation modulates spontaneous cortical slow wave dynamics in anesthetized rats

Cortical slow waves are hallmark oscillations of deep sleep and certain anesthetic conditions, yet the neurobiological mechanisms controlling their dynamics remain incompletely understood. Here, we investigated the effects of local near-infrared (NIR) stimulation on slow-wave activity in ketamine/xylazine-anesthetized rats. Using a silicon-based multimodal optrode, we simultaneously delivered NIR light and recorded local field potentials (LFPs) and multi-unit activity (MUA) across cortical layers in the primary somatosensory (S1Tr) and parietal association (PtA) cortices. NIR stimulation induced local tissue heating, resulting in reproducible and reversible changes in the properties of slow waves. Specifically, up-state durations were shortened, down-states prolonged, and MUA amplitudes during up-states increased, with steeper slopes at state transitions, indicative of enhanced neuronal synchronization. LFP amplitude and spectral changes varied across cortical regions: PtA exhibited increased slow wave (0.5 - 2 Hz) and high delta (2 - 4 Hz) band activity, while S1Tr showed a trend toward reduction. Our findings demonstrate that local infrared stimulation can reliably modulate cortical slow-wave dynamics, likely through temperature-mediated changes in neuronal excitability. This approach provides a minimally invasive method for precise, local manipulation of cortical network activity and offers new insights into the biophysical regulation of slow oscillations.

neuroscience↗

bioRxiv2021

Huygens synchronization of medial septal pacemaker neurons generates hippocampal theta oscillation

Episodic learning and memory retrieval are critically dependent on a hippocampal 4-12 Hz oscillatory clock signal, the theta oscillation. This clock is largely externally paced, by a network of GABAergic neurons in the medial septum (MS). Theoretical studies suggested a range of hypotheses how this network may achieve theta synchrony; however, experimental evidence is still lacking. By recording multiple single MS neurons and hippocampal local field potential oscillations simultaneously, with both acute and chronically implanted silicon probes, we show that MS pacemaker units oscillate at individual frequencies within the theta range in rodents. Synchronization of MS neuron frequencies, accompanied by an elevation of firing rates, was found to parallel hippocampal theta formation in multiple rodent model systems. This suggests a general mechanism for theta synchronization, akin to the synchronization of weakly coupled pendulum clocks observed by Huygens in the 17th century. We optogenetically identified the MS pacemaker units as parvalbumin-expressing GABAergic neurons, while the previously enigmatic MS glutamatergic neurons were mostly theta-activated non-rhythmic cells. Our data were consistent with a network model of partially connected single-compartment inhibitory pacemaker neurons, in which synchronization and de-synchronization in the frequency domain upon waxing and waning tonic excitatory drive was sufficient to toggle MS network output between theta and non-theta states. These results provide experimental and theoretical support to a frequency-synchronization mechanism for pacing hippocampal theta, which may serve as an inspirational prototype for the countless examples of synchronization processes in the central nervous system from Nematoda to Anthropoda to Chordate and Vertebrate phyla.

neuroscience↗

bioRxiv2020

Decoding neurobiological spike trains using recurrent neural networks; a case study with electrophysiological auditory cortex recordings

Recent advancements in multielectrode methods and spike-sorting algorithms enable the in vivo recording of the activities of many neurons at a high temporal resolution. These datasets offer new opportunities in the investigation of the biological neural code, including the direct testing of specific coding hypotheses, but they also reveal the limitations of present decoder algorithms. Classical methods rely on a manual feature extraction step, resulting in a feature vector, like the firing rates of an ensemble of neurons. In this paper, we present a recurrent neural-network based decoder and evaluate its performance on experimental and artificial datasets. The experimental datasets were obtained by recording the auditory cortical responses of rats exposed to sound stimuli, while the artificial datasets represent preset encoding schemes. The task of the decoder was to classify the action potential timeseries according to the corresponding sound stimuli. It is illustrated that, depending on the coding scheme, the performance of the recurrent-network based decoder can exceed the performance of the classical methods. We also show how randomized copies of the training datasets can be used to reveal the role of candidate spike-train features. We conclude that artificial neural network decoders can be a useful alternative to classical population vector based techniques in studies of the biological neural code.

neuroscience↗

bioRxiv2020

Recording site placement on planar silicon-based probes affects neural signal quality: edge sites enhance acute recording performance

ObjectiveMultisite, silicon-based probes are widely used tools to record the electrical activity of neuronal populations. Several physical features of these devices (e.g. shank thickness, tip geometry) 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. ApproachNeural recordings obtained with five different types of high-density, single-shank, planar silicon probes from anesthetized rats were analyzed. Wideband data were filtered (500 - 5000 Hz) to extract spiking activity, then various quantitative properties (e.g. amplitude distribution of the filtered potential, single unit yield) of the recorded cortical and thalamic activity were compared between sites located at different positions of the silicon shank, focusing particularly on edge and center sites. Main resultsEdge sites outperformed center sites: mean values of the examined properties of the spiking activity were in most cases higher for edge sites (~94%, 33/35) and a large fraction of these differences were also statistically significant (~45%, 15/33) with effect sizes ranging from small to large. Although the single unit yield was similar between site positions, the difference in signal quality was remarkable in the range corresponding to high-amplitude spikes. Furthermore, the advantage of edge sites slightly decreased for probes having a narrower shank. SignificanceThe better signal quality on edge sites might be the result of the reduced shielding effect of the silicon shank providing a larger field of view for edge sites to detect spikes, or the less tissue damage caused near the edges of the shank. Our results might aid the design of novel neural implants in enhancing their recording performance by identifying more efficient recording site placements.

neuroscience↗

bioRxiv2016

UP-DOWN cortical dynamics reflect state transitions in a bistable balanced network

In the idling brain, neuronal circuits often exhibit transitions between periods of sustained firing (UP state) and quiescence (DOWN state). Although these dynamics occur across multiple areas and behavioral conditions, the underlying mechanisms remain unclear. Here we analyze spontaneous population activity from the somatosensory cortex of urethane-anesthetized rats. We find that UP and DOWN periods are variable (i.e. non-rhythmic) and that the population rate shows no significant decay during UP periods. We build a network model of excitatory (E) and inhibitory (I) neurons that exhibits a new bistability between a quiescent state and a balanced state of arbitrarily low rate. Fluctuating inputs trigger state transitions. Adaptation in E cells paradoxically causes a marginal decay of E-rate but a marked decay of I-rate, a signature of balanced bistability that we validate experimentally. Our findings provide evidence of a bistable balanced network that exhibits non-rhythmic state transitions when the brain rests.

neuroscience↗