Biology subjects

Jacob, S. N.

Publications and source records attributed to Jacob, S. N..

8 recordsLinked to original sources

bioRxiv2026

Complementary prefrontal and thalamic representational dynamics during response planning

Advance contextual information can guide behavioral responses, but how prospective action representations unfold across prefrontal and thalamic neuronal populations of the cognitive control network remains unclear. Here, we trained mice on a contextual response-planning task in which auditory cues either predicted the upcoming instructed movement or left response identity unresolved until a later instruction. Predictive contexts improved accuracy and produced subthreshold, directionally biased movements, indicating that mice used contextual information before instruction onset. Extracellular recordings revealed prospective response-side information in prelimbic cortex (PL) and mediodorsal thalamus (MD), but with distinct dynamics. MD represented prospective response side earlier during the context epoch and showed a planning-related temporal advance after instruction onset. In contrast, PL exhibited enhanced instruction-epoch coding and stronger cross-epoch generalization, which could be localized to sparse neuronal subpopulations. These complementary representational dynamics suggest distinct but coordinated roles for prefrontal-thalamic circuits in using advance information to support flexible action planning.

neuroscience↗

bioRxiv2026

Long-term stability of cellular-resolution brain-computer interface recordings after stroke

Implantable brain-computer interfaces (iBCIs) with single-neuron resolution are showing great promise for restoring mobility and communication in individuals with spinal cord injury or motor neuron disease. Stroke is the most common cause of acquired brain injury and a major contributor to long-term disability, making chronic stroke a highly relevant indication for iBCIs. However, whether stable intracortical recordings can be obtained from the structurally lesioned human brain is unknown. We report recordings from four 64-channel microelectrode arrays implanted in a participant with chronic aphasia after a large left-hemispheric stroke. The arrays targeted right-hemispheric frontoparietal regions homotopic to the damaged left-hemispheric language network. Across 111 sessions spanning 1,240 days, unit yield and signal quality remained stable. Waveform-based tracking reliably identified individual units across sessions, including across extended recording gaps. Short- and long-term unit stability was comparable to previous reports from iBCI participants without structural brain lesions, and tracked units showed consistent spiking properties across sessions. Our findings provide the first evidence that single-neuron recordings can remain stable over the long term in the stroke-lesioned human brain. They establish the feasibility of chronic, cellular-resolution iBCIs after stroke and support the development of neurorestorative applications for deficits caused by structural brain injury.

neuroscience↗

bioRxiv2026

Closed-loop sensory feedback enables fast and reliable instrumental acquisition in head-fixed mice

Instrumental learning typically requires hundreds to thousands of trials in which subjects learn to link motor responses to sensory cues. In standard rodent protocols, response accuracy is reported only at trial end, preventing subjects from correcting erroneously initiated responses. We hypothesized that within-trial, closed-loop sensory feedback would accelerate instrumental learning by providing real-time information about response correctness. Head-fixed mice performed a two-alternative forced-choice task by rotating a choice wheel in response to sensory cues. Mice received either no feedback (n = 18), auditory feedback (n = 16), or audiovisual feedback (n = 4) coupled to wheel movements. Feedback-receiving mice required significantly fewer trials to reach 70 % accuracy criterion (median: 3186, 4918 and 7329 trials for multimodal, unimodal and no feedback, respectively; p = 0.0245) and showed higher accuracy when modifying choices (expert stage: 17 %, 11 % and 9 % accuracy in trials with modified choices for multimodal, unimodal and no feedback, respectively; p=1.04x10-). Only feedback mice displayed movement refinements across training (p = 8.02x10-, p = 4.07x10-{superscript 1} and p = 0.2602 for multimodal, unimodal and no feedback, respectively). In summary, closed-loop sensory feedback accelerated instrumental acquisition, demonstrating its value as routine training protocol. HIGHLIGHTSO_LIMice provided with feedback require fewer trials to reach expert stage in an instrumental learning task C_LIO_LIMice provided with feedback perform with higher accuracy in trials involving changes of mind C_LIO_LIMovement trajectories of mice provided with feedback undergo refinement as training advances C_LIO_LISensory feedback can be used as a training aid to accelerate instrumental learning C_LI

animal behavior and cognition↗

bioRxiv2026

Learning stabilizes temporal activity but not neuronal selectivity in prefrontal cortex

Intelligent behavior requires neural representations to change with new demands while preserving learned structure. The prefrontal cortex is central to this ability, but the mechanisms are unclear. Here, we tracked medial prefrontal neurons for months as mice learned an association task with successive rule switches. Learning progressively stabilized when individual neurons were active during a trial, but not what task variables they responded to. Neurons repeatedly gained, lost, or changed selectivity even after their activity profile had stabilized. We developed Sparse Tensor Component Analysis to show that, rather than reflecting random drift, dynamic single-neuron selectivity arose through rule-dependent recombination of a fixed set of task representations. Thus, learning established a stable temporal scaffold in the prefrontal cortex within which neurons participated flexibly in different representations.

neuroscience↗

bioRxiv2026

A right-hemispheric language network at single-neuron resolution

Human language depends on highly specialized left-hemispheric brain networks. Damage to these networks causes severe language impairments (aphasia), one of the most common, debilitating and costly consequences of left-hemispheric brain injury, especially stroke. The limited recovery of aphasia despite intensive rehabilitation efforts emphasizes the need to understand the basis of residual language abilities at the single-neuron level, which has remained unexplored so far. Here, we report large-scale microelectrode recordings with single-unit resolution over a period of ten months from the right-hemispheric prefrontal and parietal association cortex of an individual with stroke-induced chronic non-fluent aphasia. Single neurons exhibited regionally specific responses during comprehension, retrieval and articulation of words, the core operations of language. Distinct subpopulations encoded linguistic information in a task-specific manner, despite correlated firing patterns across tasks. Both single-neuron activity and temporally coordinated population dynamics were predicted by semantic and phonological embeddings derived from large language models (LLMs), revealing a regional dissociation in which semantic features preferentially accounted for prefrontal activity and phonological features for parietal activity. Our findings suggest that right-hemispheric circuits, homotopic to the left language network, can support language processing through structured, functionally organised activity at the level of single neurons. This study opens an avenue for developing mechanistically specific neurorehabilitation and neurorestorative strategies for aphasia, such as brain-computer interfaces (BCIs), that leverage right-hemispheric language resources.

neuroscience↗

bioRxiv2024

Parcellation of the primate prefrontal cortex by cognitive control operations

Modular organization, the division of the cerebral cortex into functionally distinct subregions, is well established in the primate sensorimotor cortex, but debated in the cognitive association cortex, including the prefrontal cortex (PFC). Here, we obtained microelectrode recordings with broad spatial coverage from the lateral PFC of two rhesus monkeys performing a working memory task with distractors. We found that neighboring electrodes shared task-related oscillatory neural dynamics that were stable across recording sessions and formed spatially continuous, mesoscale clusters that also segregated by local and long-range frontoparietal connectivity, spiking activity, involvement in working memory processing stages and influence on behavioral accuracy. Remarkably, the degree of parcellation reflected the animals individual mnemonic abilities and strategies. Our findings support functional organization of the PFC by cognitive control operations rather than by the type of processed information, indicating that modularity may be a fundamental architectural principle across the primate cortex.

neuroscience↗

bioRxiv2023

The neurocellular implementation of representational geometry in primate prefrontal cortex

Modern neuroscience has seen the rise of a population-doctrine that represents cognitive variables using geometrical structures in activity space. Representational geometry does not, however, account for how individual neurons implement these representations. Here, leveraging the principle of sparse coding, we present a framework to dissect representational geometry into biologically interpretable components that retain links to single neurons. Applied to extracellular recordings from the primate prefrontal cortex in a working memory task with interference, the identified components revealed disentangled and sequential memory representations including the recovery of memory content after distraction, signals hidden to conventional analyses. Each component was contributed by small subpopulations of neurons with distinct electrophysiological properties and response dynamics. Modelling showed that such sparse implementations are supported by recurrently connected circuits as in prefrontal cortex. The perspective of neuronal implementation links representational geometries to their cellular constituents, providing mechanistic insights into how neural systems encode and process information.

neuroscience↗

bioRxiv2022

Human acute microelectrode array recordings with broad cortical access, single-unit resolution and parallel behavioral monitoring

Human single-unit studies currently rely on neurosurgical procedures that provide only limited brain coverage and on recording devices that do not integrate easily into established surgical routines. Here, we report reliable and robust acute multi-channel recordings with broad cortical access using planar microelectrode arrays (MEA) implanted intracortically in awake brain surgery. We provide a comprehensive characterization of extracellular neuronal activity acquired intraoperatively in tumor patients with large open craniotomies. MEA implantation was fast, safe and yielded high-quality signals at the microcircuit, local field potential level, and at the cellular, single-unit level. Recording from parietal association cortex, a region previously unexplored in human single-unit studies, we demonstrate applications on these complementary spatial scales and describe travelling waves of oscillatory activity as well as single-neuron and neuronal population responses during numerical cognition including operations with uniquely human number symbols. Intraoperative MEA recordings are practicable and can be scaled up to explore cellular and microcircuit mechanisms of a wide range of human brain functions.

neuroscience↗