Biology subjects

Sennesh, E. Z.

Publications and source records attributed to Sennesh, E. Z..

3 recordsLinked to original sources

bioRxiv2026

Circuit and cell-type mechanisms of the spectrolaminar motif of cortical oscillations

Oscillations are a key feature of cortical activity, coordinating spike timing across areas, layers, and cell types. Recent work suggests these rhythms follow a stereotyped organization across cortical depth, the spectrolaminar (SL) motif, with higher-frequency gamma prominent in superficial layers and lower-frequency alpha/beta in deep layers, yet the mechanisms generating it remain poorly understood. Using a bio-realistic model of mouse primary visual cortex, we reproduced the SL motif and found it preserved across diverse visual stimuli. Systematic cell-type- and layer-resolved perturbations showed that the alpha/beta component was intrinsic to every layer, whereas the 50-80 Hz gamma component was predominantly generated within layer 4 (L4) and propagated network-wide through inter-laminar connections. Pvalb interneurons were the principal regulators of gamma, Sst cells shaped low-frequency power, and Vip cells controlled oscillation magnitude. The temporal properties of L4 Pvalb-to-excitatory synapses were especially influential in generating the SL motif. These models and simulation results thus offer a plausible description of circuit mechanisms underlying cortical oscillations and the SL motif and are publicly available to facilitate further studies.

neuroscience↗

bioRxiv2025

Ubiquitous predictive processing in the spectral domain of sensory cortex

The appearance at the anatomical level of a canonical laminar microcircuit suggests that each six-layer column of granular cortex may mediate a canonical computation. Hypotheses for such computations include predictive coding, predictive routing, efficient coding, and others. However, single-neuron recordings capture only the individual elements of the hypothesized laminar microcircuit, while local field potentials (LFPs) from a laminar probe offer insight into the broader population activity. Through the Allen Institutes OpenScope Brain Observatory, data in mice performing a visual oddball task during multi-area laminar recording was used to test predictive processing hypotheses in the spectral domain. Histological labeling of the cortical laminae enabled a fine-grained examination of their roles in the task, and frequency bands capturing both feedforward and feedback effects were analyzed. {gamma}-band local-field potential (LFP) oscillations conveyed feedforward prediction errors in lower sensory areas of cortex; /{beta}-band oscillations weakened in unpredictable conditions compared to predictable ones; and {theta}-band oscillations additionally signalled slower, longer-scale temporal prediction errors. In combination with the previous findings, predictive routing explains these experiments where neither ubiquitous predictive coding nor feedforward adaptation can. SignificanceCortical columns robustly signal perceptual features through the firing rates of spiking neurons. In accordance with this rate coding, predictive processing theories hypothesized that neuronal firing rates ubiquitously signal surprise. However, a recent large-scale study of spike rates did not support this conjecture. An alternate model, predictive routing, suggests that neuronal oscillations rather than spike rates could encode surprise. These neuronal oscillations, which can affect the timing but not rate of spiking, formed coherent {gamma} rhythms which consistently signaled both simpler and more complex forms of surprise in mouse visual cortex. Together with the findings on spike-rates in the same experiment, our findings suggest that cortical circuits encode surprise in the rhythmic timing of spikes rather than in their rate.

neuroscience↗

bioRxiv2020

A computational neural model for mapping degenerate neural architectures

Degeneracy in biological systems refers to a many-to-one mapping between physical structures and their functional (including psychological) outcomes. Despite the ubiquity of the phenomenon, traditional analytical tools for modeling degeneracy in neuroscience are extremely limited. In this study, we generated synthetic datasets to describe three situations of degeneracy in fMRI data to demonstrate the limitations of the current univariate approach. We describe a novel computational approach for the analysis referred to as neural topographic factor analysis (NTFA). NTFA is designed to capture variations in neural activity across task conditions and participants. The advantage of this discovery-oriented approach is to reveal whether and how experimental trials and participants cluster into task conditions and participant groups. We applied NTFA on simulated data, revealing the appropriate degeneracy assumption in all three situations and demonstrating NTFAs utility in uncovering degeneracy. Lastly, we discussed the importance of testing degeneracy in fMRI and the implications of applying NTFA to do so.

neuroscience↗