Biology subjects

Arun, S.

Publications and source records attributed to Arun, S..

14 recordsLinked to original sources

bioRxiv2026

Reactivation of neural signatures of food during REM-like sleep in the high-level visual cortex of monkeys

Can we tell what animals are dreaming about from their brain activity? This fascinating question has been explored extensively using the rodent hippocampus, where place cells are sequentially reactivated during sleep, consistent with replay of paths explored while awake. However, dreams involve vivid visual experiences, and whether analogous replays occur in the visual cortex has not been explored, that too in primates. To address this gap, we performed wireless neural recordings from the high-level visual (IT) and motor regions (PMv, PFC) in two monkeys while they viewed food images on a screen while awake, and afterwards while they slept in their natural setting. Decoders trained on sleep/wake neural activity in each region were able to accurately predict the ongoing sleep/wake state, as well as upcoming transitions between states. Interestingly, decoders trained to categorize neural responses to food (but not faces/objects) showed increased probability during REM-like sleep compared to NREM or awake states. Thus, behaviorally relevant visual stimuli seen while awake are reactivated during REM-like sleep. In other words, these monkeys saw food, faces and objects while awake, and dreamt selectively about food while sleeping.

neuroscience↗

bioRxiv2026

High-level visual and motor regions are doubly dissociated in controlled tasks but only at specific times during natural behaviors

Our knowledge of the neural basis of sensory and motor information processing comes largely from recording brain activity in controlled settings. This approach, while insightful, blinds us from understanding natural behaviors, which involve far richer visual inputs and far larger range of movements. The prevailing belief is that, during natural behaviors, movement information would modulate visual cortex, and visual inputs would modulate motor cortex. Alternatively, this mixing might only reflect correlations between sensory inputs and movements present during natural behavior. To resolve this issue, we performed wireless recordings from high-level visual (IT) and motor regions (PMv/PFC) in freely moving monkeys while they performed a natural task and a fixation task. In each task, we extracted visual and movement features and asked to what degree these features explained neural activity. In the fixation task, we found a striking double dissociation whereby visual (but not movement) features predicted neural activity in IT, whereas motor (but not visual) features predicted neural activity in PMv & PFC. This dissociation was far weaker in the natural task but present particularly at key events when the visual and motor features were less correlated. Thus, high-level sensory and motor regions in the brain are doubly dissociated under controlled conditions, and remain so at specific times during natural behavior.

neuroscience↗

bioRxiv2026

Natural behavior elicits reliable neural signatures in high-level sensory and motor regions of freely moving monkeys

Natural behaviors are challenging to study because they involve large variations in sensory inputs and motor outputs across trials. Even the simple act of eating can involve seeing food from many angles and reaching for it differently each time. The prevailing view is that natural behaviors are more noisy compared to controlled tasks, and involve mixing of sensorimotor information. Alternatively, natural behaviors might elicit reliable neural signatures in high-level visual and motor regions which contain invariant representations, and involve correlated sensory and motor information that leads to apparent mixing. Here, we provide evidence for the latter possibility through wireless recordings in freely moving monkeys performing natural tasks as well as controlled screen tasks. During natural interactions with food, neural activity was decodable with striking fidelity, equivalent to that of food images. Key events during these interactions elicited distinct and reliable neural signatures that reflected the encoding priorities of each region. Visual and motor regions were clearly dissociated in screen tasks, but were dissociated only at specific events in the natural task. Neurons active while seeing food were reactivated during sleep. Taken together, our results reveal that reliable neural signatures in high-level sensory and motor regions form the neural substrates for natural behaviors.

neuroscience↗

bioRxiv2026

Dynamic tracking of social variables in simultaneous brain recordings of socially interacting monkeys

Primates are deeply social, but understanding the neural basis has been challenging since it has previously only been possible to record from single monkeys interacting with a social partner. Here, we performed simultaneous wireless neural recordings from two macaque monkeys interacting socially in a natural, unconstrained setting. Neural activity in each monkey encoded key social variables such as allogrooming state, partner identity and joint movements. Interestingly, neurons in the high-level visual cortex of each monkey continuously tracked a social favor signal, i.e. net duration of allogrooming given versus received, providing a neural basis for grooming reciprocity. In each monkey, neural activity was predicted better by his partners joints and neural activity, particularly while giving compared to receiving grooming. Thus, the receiver of grooming drove the social interaction, not the groomer. Taken together, our findings elucidate the rich and dynamic neural basis of primate social interactions in an ecologically valid setting.

neuroscience↗

bioRxiv2025

Orthographic training finetunes a neuronal letter position code in primate visual cortex

Reading is an acquired skill that enhances brain activity in human visual cortex and is thought to repurpose preexisting ventral visual circuitry for the fast parallel processing of letter strings. However, the neuronal code underlying position-invariant written word recognition remains elusive. Here, we examined this issue in a macaque monkey model before and after orthographic training. Based on prior simulations of reading acquisition in a convolutional neural network model of the ventral visual pathway, we hypothesized the existence of neurons sensitive to both specific letters and their ordinal position within the word, which should be enhanced by orthographic training. By wirelessly recorded neural activity from the inferior temporal (IT) cortex of macaque monkeys trained on orthographic tasks over five successive days, we indeed discovered IT neurons tuned to letters and sensitive to either ordinal or retinotopic positions. Ordinal units existed prior to training, but their responses were enhanced after training, matching with behavioral improvements in word recognition. These findings support the neuronal recycling hypothesis and demonstrate, at the single-cell level, how reading refines pre-existing neural circuits to facilitate fluent word recognition.

neuroscience↗

bioRxiv2025

Do monkeys see the way we do? Qualitative similarities and differences between monkey and human perception

Monkeys are widely used as model organisms for human vision and cognition. While their anatomy and physiology strongly correspond to humans, it is unclear to what extent their perception matches with ours. Previous studies have evaluated specific aspects of perception, after extensive training on customized tasks that could have altered their perception. To resolve these issues, we trained monkeys to perform an oddball visual search task on natural images, tested them on carefully controlled images to detect a variety of perceptual phenomena, and compared them with humans. This revealed a number of qualitative similarities and differences. Like humans, monkeys showed similar object relations, Webers law and amodal completion. However, unlike humans, monkeys did not show mirror confusion or a global advantage. These findings represent a first comprehensive evaluation of visual perception in monkeys and humans, revealing the limitations of monkeys as a model organism for human vision. SIGNIFICANCE STATEMENTMonkeys are widely used to study for vision and cognition, but is their perception like ours? Most previous studies have tested only single aspects of perception in monkeys, often after extensive training which could have altered their perception. Here, we overcame these limitations by training monkeys on a highly general oddball visual search task, testing them on many perceptual phenomena and comparing them with humans on the same tasks. Our results show that monkeys do indeed see the way we do, but with important differences that highlight their limitations as a model for human vision.

animal behavior and cognition↗

bioRxiv2024

The tubule-sheet continuum model links the pattern of nuclear envelope assembly to nuclear envelope integrity

Nuclear envelope (NE) fragility is associated with a variety of human diseases. However, major questions about the causes of NE fragility are unresolved. In particular, the relationship between NE fragility and NE assembly is unclear. Here, using a panel of 13 mammalian cell lines, we show that normal cells and some cancer cells assemble the NE using distinct patterns from opposite ends of the "tubule-sheet continuum", and that the NE assembly pattern more utilised in cancer cells is a source of NE fragility. In normal cells, the mitotic endoplasmic reticulum (ER) morphology is dominated by ER tubules or small sheets and the NE assembles by "membrane infiltration". By contrast, in some cancer cells, the mitotic ER morphology is dominated by large ER sheets and the NE assembles by "lateral sheet expansion". Experimental manipulations that promote mitotic ER sheet formation and NE assembly by lateral sheet expansion in normal cells result in NE fragility. Thus, we propose that NE assembly by lateral sheet expansion is a source of NE fragility in cancer cells.

cell biology↗

bioRxiv2022

Visual homogeneity computations in the brain enable solving generic visual tasks

Most visual tasks involve looking for specific object features. But we also often perform property-based tasks where we look for specific property in an image, such as finding an odd item, deciding if two items are same, or if an object has symmetry. How do we solve such tasks? These tasks do not fit into standard models of decision making because their underlying feature space and decision process is unclear. Using well-known principles governing multiple object representations, we show that displays with repeating elements can be distinguished from heterogeneous displays using a property we define as visual homogeneity. In behavior, visual homogeneity predicted response times on visual search, same-different and symmetry tasks. Brain imaging during visual search and symmetry tasks revealed that visual homogeneity was localized to a region in the object-selective cortex. Thus, property-based visual tasks are solved in a localized region in the brain by computing visual homogeneity. SIGNIFICANCE STATEMENTMost visual tasks involve looking for specific features, like finding a face in a crowd. But we also often look for a particular image property - such as finding an odd item, deciding if two items are same, or judging if an object is symmetric. How does our brain solve these disparate tasks? Here, we show that these tasks can all be solved using a simple computation over object representations in higher visual cortex, which we define as visual homogeneity.

neuroscience↗

bioRxiv2021

Visual search for upright bigrams predicts reading fluency in children

Fluent reading is an important milestone in education, but we lack a clear understanding of why children vary so widely in attaining this milestone. Language-related factors such as rapid automatized naming (RAN) and phonological awareness have been identified as important factors that influence reading fluency. Of theoretical interest is also, however, whether aspects of visual processing influence reading fluency. To investigate this issue, we tested primary school children (n = 68) on four tasks: two reading fluency tasks (word reading and passage reading), a RAN task to measure naming speed, and a visual search task using letters and bigrams to measure visual processing. As expected, the RAN score was strongly correlated with reading fluency. In addition, visual processing of bigrams was correlated with reading fluency. Importantly, this association was specific to upright but not inverted bigrams, and to bigrams with normal but not large letter spacing. Thus, reading fluency in children is accompanied by specialized changes in upright bigram processing. We propose that bigram processing during visual search could complement existing measures of language processing to understand individual differences in reading fluency.

neuroscience↗

bioRxiv2020

Turning traditional neurophysiology on its head: A naturalistic environment to study natural behaviors and cognitive tasks in freely moving monkeys

Macaque monkeys are widely used to study the neural basis of cognition. In the traditional approach, the monkey is brought into a lab to perform tasks while it is restrained to obtain stable gaze tracking and neural recordings. This unnatural setting prevents studying brain activity during natural, social and complex behaviors. Here, we designed a naturalistic environment with an integrated behavioral workstation that enables complex task training with viable gaze tracking in freely moving monkeys. We used this facility to train monkeys on a challenging same-different task. Remarkably, this facility enabled a naive monkey to learn the task merely by observing trained monkeys. This social training was faster primarily because the naive monkey first learned the task structure and then the same-different rule. We propose that such hybrid environments can be used to study brain activity during natural behaviors as well as during controlled cognitive tasks.

animal behavior and cognition↗

bioRxiv2020

A separable neural code in monkey IT enables perfect CAPTCHA decoding

Reading distorted letters is easy for us but so challenging for machine vision that it is used on websites as CAPTCHAs (Completely Automated Public Turing test to tell Computers and Humans Apart). How does our brain solve this problem? One solution is to have neurons invariant to letter distortions but selective for letter combinations. Another is for neurons to separately encode letter distortions and combinations. Here, we provide evidence for the latter using neural recordings in the monkey inferior temporal (IT) cortex. Neurons encoded letter distortions as a product of letter and distortion tuning, and letter combinations as a sum of letters. These rules were sufficient for perfect CAPTCHA decoding and were also present in neural networks trained for word recognition. Taken together, our findings suggest that a separable neural code enables efficient letter recognition.

neuroscience↗

bioRxiv2019

Do deep neural networks see the way we do?

Deep neural networks have revolutionized computer vision, and their object representations match coarsely with the brain. As a result, it is widely believed that any fine scale differences between deep networks and brains can be fixed with increased training data or minor changes in architecture. But what if there are qualitative differences between brains and deep networks? Do deep networks even see the way we do? To answer this question, we chose a deep neural network optimized for object recognition and asked whether it exhibits well-known perceptual and neural phenomena despite not being explicitly trained to do so. To our surprise, many phenomena were present in the network, including the Thatcher effect, mirror confusion, Webers law, relative size, multiple object normalization and sparse coding along multiple dimensions. However, some perceptual phenomena were notably absent, including processing of 3D shape, patterns on surfaces, occlusion, natural parts and a global advantage. Our results elucidate the computational challenges of vision by showing that learning to recognize objects suffices to produce some perceptual phenomena but not others and reveal the perceptual properties that could be incorporated into deep networks to improve their performance.

neuroscience↗

bioRxiv2019

How the forest interacts with the trees: Multiscale shape integration explains global and local processing

Hierarchical stimuli (such as a circle made of diamonds) have been widely used to study global and local processing. Two classic phenomena have been observed using these stimuli: the global advantage effect (that we identify the circle faster than the diamonds) and the incongruence effect (that we identify the circle faster when both global and local shapes are circles). Understanding them has been difficult because they occur during shape detection, where an unknown categorical judgement is made on an unknown feature representation.\n\nHere we report two essential findings. First, these phenomena are present both in a general same-different task and a visual search task, suggesting that they may be intrinsic properties of the underlying representation. Second, in both tasks, responses were explained using linear models that combined multiscale shape differences and shape distinctiveness. Thus, global and local processing can be understood as properties of a systematic underlying feature representation.

neuroscience↗

bioRxiv2019

Implicit perceptual memory can increase or decrease with ageing

A decline in declarative or explicit memory has been extensively characterized in cognitive ageing and is a hallmark of cognitive impairments. However, whether and how implicit perceptual memory varies with ageing or cognitive impairment is unclear. Here, we compared implicit perceptual memory and explicit memory measures in three groups of subjects: (1) 59 healthy young volunteers (20-30 years); (2) 238 healthy old volunteers (50-90 years) and (3) 21 patients with mild cognitive impairment MCI (50-90 years). To measure explicit memory, subjects were tested on standard recognition and recall tasks. To measure implicit perceptual memory, we used a classic perceptual priming paradigm. Subjects had to report the shape of a visual search pop-out target. Implicit priming was measured as the speedup in response time for targets with the same vs different color/position on consecutive trials.\n\nOur main findings are as follows: (1) Explicit memory was weaker in old compared to young subjects, and in MCI compared to age-matched controls; (2) Surprisingly, implicit perceptual memory did not always decline with age: color priming was smaller in older subjects but position priming was larger; (3) Position priming was less frequent in the MCI group compared to age-matched controls; (4) Implicit and explicit memory measures were uncorrelated in all three groups. Thus, implicit memory can increase or decrease with age or cognitive impairment, but this decline does not covary with explicit memory. We propose that incorporating explicit and implicit measures can yield a richer characterization of memory.

animal behavior and cognition↗