Biology subjects

Issar, D.

Publications and source records attributed to Issar, D..

4 recordsLinked to original sources

bioRxiv2026

Linking reaction time variability to physiological markers of arousal across timescales

Reaction time is a measure of the speed of our response to stimuli in the environment. Even for a well-trained task, a subjects reaction time varies. One source of this variability is internal state fluctuations (such as changes in arousal). There are few studies that systematically quantify the extent to which reaction time varies across different timescales and link this to measures of systemic physiology associated with arousal. In much of the literature, it is assumed but not demonstrated that behavioral and systemic measurements associated with arousal will be consistently linked because both estimate a common underlying arousal process. In this work, we examined this assumption by simultaneously measuring reaction time, heart rate, and pupil diameter in rhesus macaque monkeys performing several visual tasks over hours and across hundreds of sessions. We found a portion of the variability in reaction time could be linked to systemic physiological signatures of arousal on fast timescales from second to second and slower timescales from minute to minute. This link between reaction time and systemic physiology was also present for different biomarkers of arousal (heart rate and pupil). However, the strength of this relationship varied depending on the arousal biomarker. Our findings support the conclusion that there are multiple arousal mechanisms that act simultaneously to influence behavior and multiple timescales at which they operate.

neuroscience↗

bioRxiv2026

From Breath to Behavior: Respiratory Features Predict Visual Detection Performance

Breathing is a continuous bodily rhythm that not only sustains physiology but also shapes brain function and behavior. Here we investigated how respiration interacts with perceptual performance in nonhuman primates performing a visual detection task. Using continuous recordings, we extracted detailed features from each respiratory cycle including timing, duration, phase, depth, and volume, aligned to trial onset. Analyses revealed that timing-related features, such as inhalation onset and the respiration length, were the most reliable markers of trial outcome, whereas amplitude-based measures contributed less consistently. These findings demonstrate that the temporal structure of breathing, rather than its magnitude, plays a dominant role in shaping behavior on a moment-to-moment basis. By uncovering how fine-grained features of respiration align with perceptual success, our work highlights respiration as a strong correlate of cognition and highlights the value of feature-based approaches for linking interoceptive rhythms to behavior.

animal behavior and cognition↗

bioRxiv2020

The eyes reflect an internal cognitive state embedded in the population activity of cortical neurons

Decades of research have shown that global brain states such as arousal can be indexed by measuring the properties of the eyes. Neural signals from individual neurons, populations of neurons, and field potentials measured throughout much of the brain have been associated with the size of the pupil, small fixational eye movements, and vigor in saccadic eye movements. However, precisely because the eyes have been associated with modulation of neural activity across the brain, and many different kinds of measurements of the eyes have been made across studies, it has been difficult to clearly isolate how internal states affect the behavior of the eyes, and vice versa. Recent work in our laboratory identified a latent dimension of neural activity in macaque visual cortex on the timescale of minutes to tens of minutes. This slow drift was associated with perceptual performance on an orientation-change detection task, as well as neural activity in visual and prefrontal cortex (PFC), suggesting it might reflect a shift in a global brain state. This motivated us to ask if the neural signature of this internal state is correlated with the action of the eyes in different behavioral tasks. We recorded from visual cortex (V4) while monkeys performed a change detection task, and the prefrontal cortex, while they performed a memory-guided saccade task. On both tasks, slow drift was associated with a pattern that is indicative of changes in arousal level over time. When pupil size was large, and the subjects were in a heighted state of arousal, microsaccade rate and reaction time decreased while saccade velocity increased. These results show that the action of the eyes is associated with a dominant mode of neural activity that is pervasive and task-independent, and can be accessed in the population activity of neurons across the cortex.

neuroscience↗

bioRxiv2019

A neural network for online spike classification that improves decoding accuracy

Separating neural signals from noise can improve brain-computer interface performance and stability. However, most algorithms for separating neural action potentials from noise are not suitable for use in real time and have shown mixed effects on decoding performance. With the goal of removing noise that impedes online decoding, we sought to automate the intuition of human spike-sorters to operate in real time with an easily tunable parameter governing the stringency with which spike waveforms are classified. We trained an artificial neural network with one hidden layer on neural waveforms that were hand-labeled as either spikes or noise. The network output was a likelihood metric for each waveform it classified, and we tuned the networks stringency by varying the minimum likelihood value for a waveform to be considered a spike. Using the networks labels to exclude noise waveforms, we decoded remembered target location during a memory-guided saccade task from electrode arrays implanted in prefrontal cortex of rhesus macaque monkeys. The network classified waveforms in real time, and its classifications were qualitatively similar to those of a human spike-sorter. Compared to decoding with threshold crossings, in most sessions we improved decoding performance by removing waveforms with low spike likelihood values. Furthermore, decoding with our networks classifications became more beneficial as time since array implantation increased. Our classifier serves as a feasible preprocessing step, with little risk of harm, that could be applied to both offline neural data analyses and online decoding. New & NoteworthyWhile there are many spike-sorting methods that isolate well-defined single units, these methods typically involve human intervention and have inconsistent effects on decoding. We used human classified neural waveforms as training data to create an artificial neural network that could be tuned to separate spikes from noise that impaired decoding. We found that this network operated in real time and was suitable for both offline data processing and online decoding.

neuroscience↗