Biology subjects

Navas Zuloaga, M. G.

Publications and source records attributed to Navas Zuloaga, M. G..

3 recordsLinked to original sources

bioRxiv2026

Resonance-driven enhancement of sleep spindles using thalamic temporal interference stimulation

Sleep spindles are hallmarks of non-rapid eye movement sleep and support memory consolidation yet remain difficult to modulate non-invasively. Combining computational modeling and human sleep recordings, we show that thalamus-targeted temporal interference stimulation (TIS) with a 5Hz envelope increases spindle density via subthreshold resonance in thalamocortical relay neurons. Our results demonstrate a mechanistic framework for the rational design of interventions to selectively augment sleep spindles.

neuroscience↗

bioRxiv2026

Data-Constrained Recurrent Network Neural Model Uncovers the Circuit Mechanism of Olfactory OFF Responses

Sensory neural circuits must encode both the presence and termination of a stimulus. Following odor offset, projection neurons (PNs) in the insect antennal lobe (AL) exhibit transient increases in firing rate, termed OFF responses, yet the circuit mechanisms that generate them in recurrent excitatory-inhibitory networks remain poorly understood. Here, we constructed a biologically-constrained firing rate-based recurrent neural network (RNN) model of the locust AL and trained it on electrophysiological recordings from 110 in vivo PNs to reconstruct their odor-evoked temporal dynamics across five odorants. The trained model faithfully reproduced the firing rates of constrained neurons, while unconstrained PNs and LNs developed biologically plausible temporal response patterns and response-type diversity. Using targeted input and connectivity perturbations, we found that OFF responses arise through two mechanistically distinct pathways. A feedforward pathway transmits offset-type olfactory receptor neuron (ORN) input directly to downstream PNs, while a recurrent pathway generates post-stimulus excitation independently of offset input. Selective perturbation of individual recurrent connections identified LN-LN mutual inhibition as the dominant recurrent pathway, and decomposition of excitatory and inhibitory inputs revealed that it produces net excitation through the transient release of inhibition rather than through increased drive. The two pathways recruit largely non-overlapping PN populations, indicating that OFF response identity is an emergent property of the network state rather than a cell-intrinsic feature. These findings provide a circuit-level account of OFF response generation and demonstrate how data-constrained RNNs can dissect circuit mechanisms directly from in vivo recordings.

neuroscience↗

bioRxiv2026

Age-related sleep changes in the human brain: insights from a large-scale thalamocortical model

AbstractSleep-dependent memory consolidation relies on slow oscillations (SOs) that coordinate thalamocortical-hippocampal dynamics during slow-wave sleep (SWS). Aging disrupts SO properties, reducing SO amplitude, density, and slope, yet the circuit-level mechanisms linking structural brain changes to these disruptions remain poorly understood. Here we present a multi-scale, whole-brain thalamocortical network model incorporating biologically grounded human connectivity derived from diffusion MRI tractography, comprising over 10,000 cortical columns per hemisphere with spiking pyramidal and inhibitory neurons and an anatomically differentiated thalamic network. Simulating progressive synaptic loss, we find that selective degradation of recurrent excitatory connectivity, but not excitatory-inhibitory projections, reproduces empirically observed age-related SO changes. Increased SO duration was driven primarily by prolonged Down states, while Up state duration and spike density were reduced, suggesting a possible mechanism for impaired memory consolidation. These results suggest that aging selectively disrupts the temporal structure of SWS critical for interference-free memory consolidation, providing mechanistic insight into cognitive decline in the aging brain. Supported by: NIH (grants 1R01MH125557, 1RF1NS132913, 1R01AG099626 to MB)

neuroscience↗