Recovery from amblyopia beyond the critical period remains a major challenge in visual neuroscience. Experimental studies show that transient inactivation of the fellow eye with tetrodotoxin (TTX) produces stronger and more persistent recovery than reverse occlusion (RO), despite both interventions reducing competition from the fellow eye. The circuit mechanisms underlying this difference remain unclear. We develop a minimal cortical circuit model combining excitatory-inhibitory population dynamics, BCM-like metaplasticity, homeostatic regulation, and eye-specific synaptic plasticity. Monocular deprivation drives the network into an amblyopic operating regime with reduced excitatory activity and fellow-eye dominance. Phase-plane and operating-regime analyses show that TTX and RO subsequently place the circuit in distinct dynamical states. TTX lowers inhibitory activity while maintaining excitatory activity above the BCM modification threshold, creating a larger plasticity-permissive regime than RO. These differences produce stronger recovery of amblyopic-eye responsiveness and greater correction of ocular dominance under TTX. A local robustness analysis across 300 perturbed parameter sets shows that the TTX advantage is not dependent on a narrowly tuned parameter choice. These results provide a mechanistic account of the differential efficacy of fellow-eye inactivation and suggest that recovery from sensory deprivation depends not only on changes in sensory input, but also on the dynamical operating regime of the cortical circuit.
Minasi, C. B., Hassan, U., Cline, C. C. et al. · neuroscience
Methods 96%
Abstract
Machine learning has advanced rapidly over the past decade, yet event-locked neural activity is still analyzed predominantly with traditional statistics and linear models. While traditional linear methods provide computationally tractable statistical control for pre-selected features or trial-averaged waveforms, they miss subtle nonlinear and trial-varying neural dynamics. Nonlinear models can capture these complex patterns, but their feature reliance is opaque and notoriously difficult to threshold statistically. We introduce SafeLens (Searchlight Analysis of Fluctuation Events, Leveraging Extensive Null Sampling) to combine the power of nonlinear machine learning with the rigorous false-positive control of traditional statistical inference. At each position of a sliding window, a random forest learns to discriminate event-locked activity from interleaved resting-state neural activity using multiscale temporal derivatives. Randomized feature ablation inverts slope polarity in held-out trials to map the random forest models' reliance on specific rising versus falling excursions across the time-frequency space. SafeLens then establishes an empirical null by running the same pipeline on separate resting-state recordings, training forests to discriminate rest from rest. This empirical null translates each channel's complex nonlinear resting dynamics into a model-matched noise floor on the map's linear reliance scale, allowing a single threshold to control the family-wise error rate against the channel's own background structure rather than a summary statistic of its baseline. When we injected simulated cortico-cortical evoked potentials (CCEPs) into resting intracranial EEG recordings, SafeLens recovered ~2.5 times as many polarity-resolved deflections as trial-average thresholding and the closest data-driven method, maintaining its advantage under trial-to-trial variability while yielding ~5-11 times fewer false components, ~2-5 times lower mean boundary error, and superior single-trial latency tracking as a timing prior. In eight patients, CCEPs remained detectable above chance until 1,040 ms after stimulation, compared with 570 and 455 ms for those benchmarks. Together, these findings establish SafeLens as a novel solution for enabling nonlinear models to uncover subtle, trial-varying neural dynamics under strict statistical control.
Christof Fehrman, Jacob J. Morra, Eva A. Naumann · q-bio.NC, cs.NE
Computational models 93%
Abstract
Neural circuits must maintain functionality to drive effective behavior while their size, topology, geometry, and intrinsic dynamics change across development. To model developing visuomotor circuits and behavior, we investigated whether a single compact generative encoding could produce functional recurrent controllers across such changes. Building on the HyperNEAT algorithm, we represented this program as a compositional pattern-producing network that received developmental time as an input to generate age-specific connectivity. The developmental substrate prescribed neuronal birth, position, sensory organization, and intrinsic time constants. We tested this approach using two geometrically distinct developmental substrates and two visuomotor tasks: optomotor responses and phototaxis. Controllers optimized at only the initial or mature substrate generally failed when evaluated at other ages. In contrast, controllers optimized across multiple developmental snapshots maintained substantially higher and more consistent performance across trained and unseen ages. Optimization at only three developmental snapshots was often sufficient to produce effective behavior across the tested developmental interval. The resulting connectomes changed substantially with age and did not converge on a single trajectory of sparsity or connection-weight statistics. Controllers also differed in whether they relied on neurons born later in development. These results demonstrate that a shared spatiotemporal time-varying connectivity program can produce recurrent neural networks that preserve behavior across substantial developmental changes. Together, these results provide a framework for studying how functional stability can emerge without requiring structural or dynamical stability.
Emmanouil Giannakakis, Victor Buend\'ia, Oleg Vinogradov et al. · cond-mat.dis-nn, q-bio.NC
Computational models 93%
Abstract
Structural modularity is a ubiquitous feature of brain networks that shapes neural dynamics and information processing. Yet excitatory and inhibitory populations can differ in their modular organization, and it remains unclear how this asymmetry affects network dynamics and computation. Here, using dynamical mean-field theory and simulations of networks performing complex temporal tasks, we show that independently tuning excitatory and inhibitory modularity controls the dynamical regimes of balanced E/I networks. In particular, such tuning can generate a dominant spectral outlier despite overall E/I balance, producing a topology-controlled transition between bulk- and low-rank-dominated regimes, with different dynamical bifurcations leading to chaos and bistability, respectively. In chaotic time-series prediction tasks, computational performance is enhanced near these dynamical boundaries, including both the onset of chaos and the transition to bistability. Favorable configurations exhibit strongly modular excitation and comparatively homogeneous inhibition, similar to what is observed in cortical connectivity.
Conventional electroencephalography (EEG) devices are often impractical for ambulatory settings due to their reliance on specialized hardware and expert operation. To address this, we developed a user-friendly eyewear-based EEG system featuring dry titanium electrodes positioned approximately 5 mm inferoposterior to the T7/T8 sites (near the ears), with metal nose pads serving as references. We evaluated the system across 103 recordings spanning five paradigms: alpha reactivity, blink-related transients, fatigue, attention, and video-elicited emotion. Following quality control, 93.2-100.0% of epochs were retained. The system demonstrated high sensitivity to ocular transients, showing significant amplitude increases during the blinking task. The emotion task yielded the most robust state-related features, with statistically significant differences in differential entropy across negative, neutral, and positive video categories, achieving balanced accuracies of 0.882 for two-class and 0.667 for three-class prediction. Despite challenges at the multiple testing correction stage, the alpha reactivity and fatigue contrast tasks were still noteworthy to some extent. The attention paradigm revealed a consistent increase in differential entropy during focused states, and the eyewear system outperformed the Muse headband in group-held-out classification. These findings provide strong evidence for the feasibility of temporal-region eyewear EEG acquisition, highlighting its potential applications in the real-world, particularly for emotion recognition.