Avniel Singh Ghuman

The paper introduces a reinforcement learning model for face perception that incorporates environmental feedback, showing it matches supervised and unsupervised models in capturing neural responses to faces.

This paper evaluates the universality and reliability of a spectrolaminar motif in primate neocortex, finding it not universal and methods unreliable.

The paper uses deep neural networks to model neural responses in human auditory cortex and finds gradients of processing complexity and integration timescales across brain regions.

The paper investigates functional heterogeneity in voice-encoding cortex using electrophysiological and clinical stimulation mapping in patients with sEEG, finding overlapping roles in voice encoding and language.

The paper describes an immersive paradigm for studying natural social behavior and its neural correlates in epilepsy patients using synchronized behavioral and neurophysiological data.

The paper maps gradients of neural timescales and functional connectivity in the cortex and shows their relationship to perceptual behavior.

This study uses machine learning and spectral graph theory to show that DBS increases high beta-gamma synchrony in a cortical network involving motor and cognitive areas.

The paper uses human intracerebral recordings to show that voice is encoded categorically in STG and STS, not by acoustic features, and this selectivity increases along the auditory hierarchy.

This study maps word- and face-selective regions in human ventral temporal cortex using intracranial recordings and fMRI, revealing distinct temporal dynamics and spatial organization.

This study uses intracranial recordings to show that individual face and word representations in human ventral temporal cortex start in domain-selective areas and later involve distributed regions.

The paper shows endogenous neural activity modulates neural tuning and perception in a circuit- and stimulus-specific manner using intracranial recordings from epilepsy patients.

SCOTS maps brain function from neural time series by integrating spatio-temporal dependence through dimensionality reduction and classification, revealing category-specific neural activation patterns.

This study shows that the left mid-ventral temporal cortex (lmVTC) individuates real words through interactions with anterior temporal and visual regions, distinguishing them from pseudowords and other stimuli.

The paper investigates the role of posterior and mid-fusiform regions in facial expression processing using intracranial recordings and multivariate analysis, revealing distinct temporal dynamics in their contribution to expression perception.

This study examines STN activity and subthalamic-cortical dynamics during force generation, finding that STN gamma activity predicts cortical activity and contributes to motor planning and execution.

This paper introduces MCPA to decode information in neural interactions, showing it captures image individuation and face recognition through distributed brain circuits.