Biology subjects

Sommer, F. T.

Publications and source records attributed to Sommer, F. T..

6 recordsLinked to original sources

bioRxiv2026

Single-neuron and population contributions of hippocampal LFPs to spike prediction

Local field potentials (LFPs) contain signals generated by individual neurons and by coordinated population activity, but distinguishing these contributions remains a challenge. We examined how hippocampal LFPs at different frequencies predict single-neuron spiking during spatial navigation in male rats using two datasets. At each frequency, we assessed the spatial distribution of LFP-based prediction across the electrode array and its generalization across behavioral contexts in which a neuron remained active, but its co-active peers changed. For pyramidal cells, spatially distributed LFP features, consistent with population-level activity, contributed primarily to spike prediction at theta ([~]10 Hz) and its harmonics. In contrast, spatially localized signals, consistent with the recorded neurons activity, contributed predominantly at higher frequencies. Notably, gamma-band LFPs (30-80 Hz) provided comparatively little information about pyramidal-cell spiking, while distributed LFP features predicted interneuron spiking across a broader frequency range. Together, this predictive approach separates local and distributed correlates of spiking within the LFP. In hippocampal CA1, these correlates fell into two spatiotemporal regimes: a distributed regime expressed primarily at theta frequencies and a localized regime reflecting single-neuron activity at higher frequencies. Significance StatementBrain waves reflect the activity of neuronal populations across spatiotemporal scales. We asked which features of brain waves recorded in the hippocampus predict an individual neurons spiking activity as a rat navigated a maze. Brain waves predicted spiking activity most accurately at two different regimes: a low-frequency, spatially distributed regime and a high-frequency, local regime. The distributed regime was concentrated in the [~]10 Hz theta band, while the local regime appeared to reflect the recorded neurons activity at high frequencies (>100 Hz). Surprisingly, the gamma band, often linked to neuronal communication and cell assemblies, was the weakest predictor of place cell activity. Our work provides a way to separate individual-neuron and population-level information in hippocampal LFPs.

neuroscience↗

bioRxiv2025

Head-Direction Cells in Postsubiculum Show Systematic Parallax Errors During Visual Anchoring

The rodent head-direction (HD) system provides an allocentric orientation signal for spatial navigation through integration of self-motion inputs and visual landmarks ( cues). Inferring HD from nearby visual cues faces a fundamental challenge: the direction towards the cue shifts with the animals position. If left uncorrected, this introduces a position-dependent parallax error. Here, we show that the HD signal in freely moving mice indeed exhibits a position-dependent bias consistent with parallax in a single-cue environment. This bias is smaller than predicted by geometric parallax and is further reduced in more natural multi-cue settings. Computational modeling revealed that this reduction of parallactic error can be explained by two averaging operations: multi-view averaging - temporal integration of the same cue viewed from different locations, and multi-cue averaging - concurrent weighting of multiple cues. Thus, averaging without explicit position-dependent correction provides a fast and robust heuristic that the HD system seems to employ for the maintenance of the internal compass. Our findings have broader implications for biological and artificial navigation systems. First, accurate allocentric reference frames are a key component of the cognitive map postulated in the extended hippocampal circuit and used for spatial cognition as well as abstract mental manipulation. Second, the employment of a fast heuristic in the HD system echoes heuristic strategies observed in other behaviors, such as decision making. More broadly, these findings highlight a trade-off in neural coding between computational efficiency and positional accuracy.

neuroscience↗

bioRxiv2023

News without the buzz: reading out weak theta rhythms in the hippocampus

Local field potentials (LFPs) reflect the collective dynamics of neural populations, yet their exact relationship to neural codes remains unknown1. One notable exception is the theta rhythm of the rodent hippocampus, which seems to provide a reference clock to decode the animals position from spatiotemporal patterns of neuronal spiking2 or LFPs3. But when the animal stops, theta becomes irregular4, potentially indicating the breakdown of temporal coding by neural populations. Here we show that no such breakdown occurs, introducing an artificial neural network that can recover position-tuned rhythmic patterns (pThetas) without relying on the more prominent theta rhythm as a reference clock. pTheta and theta preferentially correlate with place cell and interneuron spiking, respectively. When rats forage in an open field, pTheta is jointly tuned to position and head orientation, a property not seen in individual place cells but expected to emerge from place cell sequences5. Our work demonstrates that weak and intermittent oscillations, as seen in many brain regions and species, can carry behavioral information commensurate with population spike codes.

neuroscience↗

bioRxiv2023

Local interneurons in the murine visual thalamus have diverse receptive fields and can provide feature selective inhibition to relay cells.

By influencing the type and quality of information that relay cells transmit, local interneurons in thalamus have a powerful impact on cortex. To define the sensory features that these inhibitory neurons encode, we mapped receptive fields of optogenetically identified cells in the murine dorsolateral geniculate nucleus. Although few in number, local interneurons had diverse types of receptive fields, like their counterpart relay cells. This result differs markedly from visual cortex, where inhibitory cells are typically less selective than excitatory cells. To explore how thalamic interneurons might converge on relay cells, we took a computational approach. Using an evolutionary algorithm to search through a library of interneuron models generated from our results, we show that aggregated output from different groups of local interneurons can simulate the inhibitory component of the relay cells receptive field. Thus, our work provides proof-of-concept that groups of diverse interneurons can supply feature-specific inhibition to relay cells.

neuroscience↗

bioRxiv2021

Consciousness is supported by near-critical cortical electrodynamics

Mounting evidence suggests that during conscious states, the electrodynamics of the cortex are poised near a critical point or phase transition, and that this near-critical behavior supports the vast flow of information through cortical networks during conscious states. Here, for the first time, we empirically identify the specific critical point near which conscious cortical dynamics operate as the edge-of-chaos critical point, or the boundary between periodicity/stability and chaos/instability. We do so by applying the recently developed modified 0-1 chaos test to electrocorticography (ECoG) and magne-toencephalography (MEG) recordings from the cortices of humans and macaques across normal waking, generalized seizure, GABAergic anesthesia, and psychedelic states. Our evidence suggests that cortical information processing is disrupted during unconscious states because of a transition of cortical dynamics away from this critical point; conversely, we show that psychedelics may increase the information-richness of cortical activity by tuning cortical electrodynamics closer to this critical point. Finally, we analyze clinical electroencephalography (EEG) recordings from patients with disorders of consciousness (DOC), and show that assessing the proximity of cortical electrodynamics to the edge-of-chaos critical point may be clinically useful as a new biomarker of consciousness. Significance StatementWhat changes in the brain when we lose consciousness? One possibility is that the loss of consciousness corresponds to a transition of the brains electric activity away from edge-of-chaos criticality, or the knifes edge in between stability and chaos. Recent mathematical developments have produced novel tools for testing this hypothesis, which we apply for the first time to cortical recordings from diverse brain states. We show that the electric activity of the cortex is indeed poised near the boundary between stability and chaos during conscious states and transitions away from this boundary during unconsciousness, and that this transition disrupts cortical information processing.

neuroscience↗

bioRxiv2019

NWB:N 2.0: An Accessible Data Standard for Neurophysiology

Neurodata Without Borders: Neurophysiology (NWB:N) is a data standard for neurophysiology, providing neuroscientists with a common standard to share, archive, use, and build common analysis tools for neurophysiology data. With NWB:N version 2.0 (NWB:N 2.0) we made significant advances towards creating a usable standard, software ecosystem, and vibrant community for standardizing neurophysiology data. In this manuscript we focus in particular on the NWB:N data standard schema and present advances towards creating an accessible data standard for neurophysiology.

neuroscience↗