Biology subjects

Gepshtein, S.

Publications and source records attributed to Gepshtein, S..

3 recordsLinked to original sources

bioRxiv2026

Reaction-time asymmetry covaries with shifts in hand choice in macaques

How primates choose which hand to reach with remains poorly understood. Humans prefer the hand on the target's side of the body midline, and that preference shifts with biomechanical cost and with head and gaze direction. Earlier monkey studies used only two targets, one per side, too few to fit a psychometric function. Reaction time may also reflect the choice; in humans it is longest at the point of subjective equality (PSE), the target position at which either hand is equally likely to be chosen. We measured hand choice at ten horizontal target positions in two male rhesus macaques (Macaca mulatta) that chose freely on each trial. For peripheral targets, both animals used the hand on the same side of the body midline, with a well-defined transition in between. Displacing the hands, rotating the head, or displacing gaze to one side each moved the PSE toward that side. Reaction time was longest near the PSE. Hand displacement slowed the hand on the displaced side, and gaze displacement slowed the hand opposite the gaze. In every condition, the farther the PSE lay toward one side, the slower that side's hand was relative to the other. Macaque hand choice thus resembles human hand choice, making the macaque a tractable model for studying how the brain chooses a hand.

neuroscience↗

bioRxiv2026

Intrinsic Organization of Contrast Sensitivity in Human Vision

Natural vision operates over an enormous range of luminance while preserving sensitivity to both fine and coarse spatial structure. As luminance increases, the peak of the contrast sensitivity function shifts toward higher spatial frequencies. Existing accounts explain how luminance rescales the amplitude of neural responses through gain control, but not why preferred spatial frequency should reorganize systematically with luminance. Here we propose that recurrent excitatory-inhibitory interactions generate an intrinsic spatial frequency, set by the coupling structure of the circuit, that shapes the network's resonance and thereby predicts changes in preferred spatial frequency. Changes in mean luminance shift this intrinsic spatial frequency by altering the effective balance between excitation and inhibition, thereby moving the network between dynamical regimes rather than simply rescaling its responses. To test this framework, we measured human contrast sensitivity across a finely sampled range of luminance. Preferred spatial frequency followed the predicted course: approximately constant at low luminance, then rising sharply over a narrow range in an S-shaped transition. These results support a circuit-based account in which changes in cortical state reorganize spatial selectivity, linking luminance-dependent perception to the dynamics of cortical computation.

neuroscience↗

bioRxiv2021

Spatially distributed computation in cortical circuits

The traditional view of neural computation in the cerebral cortex holds that sensory neurons are specialized, i.e., selective for certain dimensions of sensory stimuli. This view was challenged by evidence of contextual interactions between stimulus dimensions in which a neurons response to one dimension strongly depends on other dimensions. Here we use methods of mathematical modeling, psychophysics, and electrophysiology to address shortcomings of the traditional view. Using a model of a generic cortical circuit, we begin with the simple demonstration that cortical responses are always distributed among neurons, forming characteristic waveforms, which we call neural waves. When stimulated by patterned stimuli, circuit responses arise by interference of neural waves. Resulting patterns of interference depend on interaction between stimulus dimensions. Comparison of these modeled responses with responses of biological vision makes it clear that the framework of neural wave interference provides a useful alternative to the standard concept of neural computation. TeaserInvestigating interference of neural waves helps to overcome limitations of the traditional view of cortical computation.

neuroscience↗