Biology subjects

Jadi, M. P.

Publications and source records attributed to Jadi, M. P..

12 recordsLinked to original sources

bioRxiv2026

Pre-saccadic suppression and compensatory cortical distortions support predictive visual remapping

Perceptual continuity across saccades relies on the predictive remapping of receptive fields (RFs) of visual neurons, driven by corollary discharge (CD). However, how the brain achieves accurate and coherent remapping within its non-uniform visual representation remains unclear. Here, we present a generalized framework of recurrent 2D network in which neurons conjunctively tuned to retinotopic location and planned saccade direction integrate CD signals to predictively remap. We show that pre-saccadic suppression (PS) stabilizes this process by counteracting direction selectivity distortions, while recurrent connectivity preserves cell-cell RF relationship against input noise by constraining the cortical map to a low-dimensional manifold. Analyzing macaque V2 recordings, we validate saccade direction selectivity, the role of PS, and conserved RF relationships. Lastly, we show that while non-uniform representation introduces eccentricity-dependent remapping errors in the model, countervailing distortions systematically reduce them. Our study reveals how network architecture can reconcile the demands of visual acuity and perceptual continuity.

neuroscience↗

bioRxiv2026

Division of vocal signaling in cooperating marmoset dyads

Communication enables partners to coordinate actions toward shared goals, but how vocal communication is organized during cooperation remains poorly understood. We recorded vocalizations from three common marmoset (Callithrix jacchus) dyads as they learned a mutual cooperation task requiring coordinated lever pulls for reward. Vocal behavior was divided by social hierarchy: dominants produced t-peeps, whereas subordinates produced chirps. Interestingly, dominant t-peeps followed failed cooperative pulls, peaked when cooperative bouts broke down, and declined after learning. By contrast, subordinate chirps followed successful pulls, accumulated during sustained cooperation, and persisted after learning. Each call was linked to the task role of the caller: dominant animals t-peeped more in sessions they led, and subordinates chirped mainly after cooperations they initiated, associations that sharpened with learning. Thus, social rank determined which vocal signal a partner used, whereas task role and the state of cooperation determined when to communicate. These complementary vocal signals of failure and success reveal how hierarchy, task role and task phase organize communication during joint action in cooperating marmosets.

neuroscience↗

bioRxiv2026

State-Dependent Dissociation of Shared Input and Directed Information Flow in the Visual Cortex

Understanding how brain state and sensory input shape inter-laminar communication is essential for interpreting cortical population dynamics. Using laminar recordings in macaque V1, we apply reduced-rank regression to quantify low-dimensional predictive subspaces linking Input and Superficial layers. We find that both visual stimulation and internal state (eyes open vs. closed) modulate the structure and efficacy of these subspaces, but through distinct mechanisms. Visual input induces a directional, feedforward pattern from input to superficial layers, while wakefulness-related modulation enhances coordination more symmetrically. Delay analysis and network simulations confirm that structured, layer-specific inputs produce directional prediction, whereas global fluctuations yield undirected co-activation. Importantly, differential structure across layers predicts the emergence of communication asymmetry. These findings dissociate communication from shared modulation, providing a principled framework for interpreting inter-population correlations. Our results generalize to broader cortical circuits, offering insights into when population coupling reflects genuine information flow versus global state dynamics.

neuroscience↗

bioRxiv2026

Uncovering Dynamic Neural Information Flow with Continuous-Time Weighted Dynamic Bayesian Networks

Understanding how information dynamically flows within neural systems is a crucial problem in neuroscience. Traditional approaches often assume stationary or quasi-stationary functional networks, which fail to capture the time-varying dynamics of interactions among neural variables. To address this limitation, we introduce Continuous-Time weighted Dynamic Bayesian Networks (CTwDBN), a non-stationary graphical modeling framework for uncovering smoothly time-varying conditional dependencies. Validation on synthetic datasets demonstrated that CTwDBN reliably recovers the structure and dynamics of ground-truth information flow. Application to electrophysiological recordings during a guided saccade task revealed temporal fluctuations in conditional dependencies in the cortical network that persisted an order of magnitude longer than the receptive field dynamics. In the resting-state cortex, CTwDBN revealed persistent fluctuations within a low-dimensional dependency space reflecting canonical anatomical motifs. These results highlight CTwDBN as a versatile analytical framework for capturing dynamic information flow in neural systems with broad applicability to complex biological and artificial systems.

neuroscience↗

bioRxiv2025

Canonical decision computations underlie behavioral and neural signatures of cooperation in primates

Successful cooperation requires dynamic integration of social cues. However, the neural mechanisms supporting this complex process remain unknown. Here, we reveal that the primate dorsomedial prefrontal cortex (dmPFC) implements a gaze-dependent social evidence accumulation process to guide cooperative decisions in freely moving marmoset dyads. A drift-diffusion process in which the partners action variability is accumulated by social gaze best explains the cooperative actions of the actor. Single-neuron recordings in dmPFC revealed a direct neural correlate: the slope of predictive ramping activity mapped directly onto the rate of evidence accumulation, while baseline firing, modulated by prior outcomes, mapped onto the initial bias. At the population level, the geometry of dmPFC neural trajectories reflected the strength of social evidence and was linked to cooperative success. Together, these findings establish a multi-level neural mechanism for transforming active sensing into a decision variable, linking a canonical computation to cooperative behavior in a naturalistic setting.

neuroscience↗

bioRxiv2025

Diverse and Flexible Strategies Enable Successful Cooperation in Marmoset Dyads

In humans, cooperation relies on advanced social cognition, but the extent to which these mechanisms support cooperation in nonhuman primates remains unclear. To investigate this, we examined freely moving marmoset dyads in a cooperative lever-pulling task. Marmosets successfully coordinated actions, relying on social vision rather than environmental cues. Blocking visual access or replacing the partner with an automated agent disrupted coordination. Causal dependencies between social gaze and pull actions revealed both gaze-dependent and gaze-independent strategies. Cooperation depended on social relationships, including dominance, kinship, and sex. Remarkably, marmosets adapted strategies based on partner identity, indicating rapid social learning and memory. Altogether, these findings show that flexible, cognitively driven cooperation extends more broadly across primates than previously recognized, informing our understanding of cooperative behaviors mechanisms and evolution.

neuroscience↗

bioRxiv2024

Spatial context non-uniformly modulates inter-laminar communication in the primary visual cortex

Our visual experience is a result of the concerted activity of neuronal ensembles in the sensory hierarchy. Yet how the spatial organization of objects influences neural activity in this hierarchy remains poorly understood. We investigate how the inter-laminar interactions in the primary visual cortex (V1) are affected by visual stimuli in isolation or with flanking stimuli at various spatial configurations that are known to cause non-uniform degradation of perception. By employing dimensionality reduction approaches to simultaneous layer-specific population recordings, we establish that cortical layers interact through a structurally stable communication subspace. Spatial configuration of contextual stimuli differentially modulates inter-laminar communication efficacy, the balance between feedforward and feedback signaling, and contextual signaling in the superficial layers. Remarkably, these modulations mirror the spatially non-uniform aspects of perceptual degradation. Our results suggest a model of retinotopically non-uniform cortical connectivity in the output layers of V1 that influences communication in the sensory hierarchy.

neuroscience↗

bioRxiv2024

Automated 3D analysis of social head-gaze behaviors in freely moving marmosets

Social communication relies on the ability to perceive and interpret the direction of others attention, and is commonly conveyed through head orientation and gaze direction in humans and nonhuman primates. However, traditional social gaze experiments in nonhuman primates require restraining head movements, significantly limiting their natural behavioral repertoire. Here, we developed a novel framework for accurately tracking facial features and three-dimensional head gaze directions of multiple freely moving common marmosets (Callithrix jacchus). By combining deep learning-based computer vision tools with triangulation algorithms, we were able to track the facial features of marmoset dyads within an arena. This method effectively generates dynamic 3D geometrical facial frames while overcoming common challenges like occlusion. To detect the head gaze direction, we constructed a virtual cone, oriented perpendicular to the facial frame. Using this pipeline, we quantified different types of interactive social gaze events, including partner-directed gaze and joint gaze to a shared spatial location. We observed clear effects of sex and familiarity on both interpersonal distance and gaze dynamics in marmoset dyads. Unfamiliar pairs exhibited more stereotyped patterns of arena occupancy, more sustained levels of social gaze across social distance, and increased social gaze monitoring. On the other hand, familiar pairs exhibited higher levels of joint gazes. Moreover, males displayed significantly elevated levels of gazes toward females faces and the surrounding regions, irrespective of familiarity. Our study reveals the importance of two key social factors in driving the gaze behaviors of a prosocial primate species and lays the groundwork for a rigorous quantification of primate behaviors in naturalistic settings.

animal behavior and cognition↗

bioRxiv2023

State-dependent modulation of dependencies in a laminar cortical circuit

Spatial attention is a quintessential example of adaptive information processing in the brain and is critical for recognizing behaviorally relevant objects in a cluttered environment. Object recognition is mediated by neural encoding along the ventral visual hierarchy. How the deployment of spatial attention aids these hierarchical computations is unclear. Prior studies point to two distinct mechanisms: an improvement in the efficacy of information directed from one encoding stage to another1-5, and/or a suppression of shared information within encoding stages6,7. To test these proposals, it is crucial to estimate the attentional modulation of unique information flow across and shared information within the encoding stages of the visual hierarchy. We investigated this in the multi-stage laminar network of visual area V4, an area strongly modulated by attention8-10. Using network-based dependency estimation from multivariate data, we quantified the modulation of inter-layer information flow during a change detection task and found that deployment of attention indeed strengthened unique dependencies between the input and superficial layers. Using the partial information decomposition framework11,12, we estimated the modulation of shared dependencies and found that they are reduced specifically in the putative excitatory subpopulations within a layer. Surprisingly, we found a strengthening of unique dependencies within the laminar populations, a finding not previously predicted. Crucially, these modulation patterns were also observed during successful behavioral outcomes (hits) that are thought to be mediated by endogenous brain state fluctuations13-15, and not by experimentally imposed attentive states. Finally, phases of endogenous fluctuations that were optimal for hits were associated with reduced neural excitability. A reduction in neural excitability, potentially mediated by diminished shared inputs, suggests a novel mechanism for enhancing unique information transmission during optimal states. By decomposing the modulation of multivariate information, and combined with prior theoretical work16, our results suggest common computations of optimal sensory states that are attained by either task demands or endogenous fluctuations.

neuroscience↗

bioRxiv2022

Cell-type specific deletions of Neuroligin 2 reveal a vital role of synaptic excitation-inhibition balance

Synaptic excitation (E) and inhibition (I) stay relatively proportional to each other over different spatiotemporal scales, orchestrating neuronal activity in the brain. This proportionality, referred to as E-I balance, is thought to be critical for neuronal functions because its disruption was observed in many neurological disorders. However, the causal evidence demonstrating its significance is scarce. Here we show that deleting Neuroligin-2 (Nlgn2), a postsynaptic adhesion molecule at inhibitory synapses, from mouse glutamatergic or GABAergic neurons reduces inhibition cell-autonomously without affecting excitation, thereby disrupting E-I balance and causing lethality. In contrast, deleting Nlgn2 constitutively or simultaneously from both glutamatergic and GABAergic neurons results in viable mice. A neural network model shows that reducing inhibition in either neuronal type is detrimental to network activity, but in both types partially re-establishes E-I balance and activity. Together, our results provide evidence for an essential role of E-I balance in brain functions and organism survival.

neuroscience↗

bioRxiv2021

Brain state and cortical layer-specific mechanisms underlying perception at threshold

Identical stimuli can be perceived or go unnoticed across successive presentations, producing divergent behavioral outcomes despite similarities in sensory input. We sought to understand how fluctuations in behavioral state and cortical layer and cell-class specific neural activity underlie this perceptual variability. We analyzed physiological measurements of state and laminar electrophysiological activity in visual area V4 while monkeys were rewarded for correctly reporting a stimulus change at perceptual threshold. Hit trials were characterized by a behavioral state with heightened arousal, greater eye position stability, and enhanced decoding performance of stimulus identity from neural activity. Target stimuli evoked stronger responses in V4 in hit trials, and excitatory neurons in the superficial layers, the primary feed-forward output of the cortical column, exhibited lower variability. Feed-forward interlaminar population correlations were stronger in hits. Hit trials were further characterized by greater synchrony between the output layers of the cortex during spontaneous activity, while the stimulus-evoked period showed elevated synchrony in the feed-forward pathway. Taken together, these results suggest that a state of elevated arousal and stable retinal images allow enhanced processing of sensory stimuli, which contributes to hits at perceptual threshold.

neuroscience↗

bioRxiv2018

Classical-contextual interactions in V1 may rely on dendritic computations

A signature feature of the neocortex is the dense network of horizontal connections (HCs) through which pyramidal neurons (PNs) exchange \"contextual\" information. In primary visual cortex (V1), HCs are thought to facilitate boundary detection, a crucial operation for object recognition, but how HCs modulate PN responses to boundary cues within their classical receptive fields (CRF) remains unknown. We began by \"asking\" natural images, through a structured data collection and ground truth labeling process, what function a V1 cell should use to compute boundary probability from aligned edge cues within and outside its CRF. The \"answer\" was an asymmetric 2-D sigmoidal function, whose nonlinear form provides the first normative account for the \"multiplicative\" center-flanker interactions previously reported in V1 neurons (Kapadia et al. 1995, 2000; Polat et al. 1998). Using a detailed compartmental model, we then show that this boundary-detecting classical-contextual interaction function can be computed with near perfect accuracy by NMDAR-dependent spatial synaptic interactions within PN dendrites - the site where classical and contextual inputs first converge in the cortex. In additional simulations, we show that local interneuron circuitry activated by HCs can powerfully leverage the nonlinear spatial computing capabilities of PN dendrites, providing the cortex with a highly flexible substrate for integration of classical and contextual information.\n\nSignificance StatementIn addition to the driver inputs that establish their classical receptive fields, cortical pyramidal neurons (PN) receive a much larger number of \"contextual\" inputs from other PNs through a dense plexus of horizontal connections (HCs). However by what mechanisms, and for what behavioral purposes, HCs modulate PN responses remains unclear. We pursued these questions in the context of object boundary detection in visual cortex, by combining an analysis of natural boundary statistics with detailed modeling PNs and local circuits. We found that nonlinear synaptic interactions in PN dendrites are ideally suited to solve the boundary detection problem. We propose that PN dendrites provide the core computing substrate through which cortical neurons modulate each others responses depending on context.

neuroscience↗