Biology subjects

Ghuman, A.

Publications and source records attributed to Ghuman, A..

4 recordsLinked to original sources

bioRxiv2026

Face-selective responses correlate with deep networks that learn from environment feedback

AO_SCPLOWBSTRACTC_SCPLOWDeep neural networks have shown high accuracy in modeling neural responses in the visual system, but most models rely on supervised learning, which requires training on ground-truth labels that are typically unavailable in real-world settings. While unsupervised models can address this limitation, they miss another key aspect: visual representations are shaped by feedback from the environment. We introduce a reinforcement learning (RL) model of face perception that incorporates both input stimuli and feedback from the environment. Inspired by human interactions, we train the model to approach faces yielding positive interactions and avoid faces yielding negative interactions. Using intracortical electroencephalography (iEEG) data and Representational Dissimilarity Matrices (RDMs), we evaluate the models ability to account for neural responses. Our RL model performs at the same level as supervised and unsupervised models, capturing neural responses to complex visual stimuli. The findings suggest that RL models are a promising approach for understanding perception. Significance StatementUnderstanding how the brain encodes faces is central to vision science. Existing models rely on supervised learning, which requires ground-truth labels that are often unavailable in real-world settings, or on unsupervised learning, which ignores the role of environmental-feedback in shaping visual representations. We introduce a reinforcement learning (RL) model that learns through environmental feedback, simulating human interactions by associating approaching faces with positive interactions and avoiding faces with negative interactions. Using intracortical electroencephalography (iEEG) data from face-selective regions, we show that an RL model with a variational DenseNet encoder accounts for neural representations comparably to supervised and unsupervised models. Task and architecture jointly shaped representational geometry, highlighting the importance of both learning objective and encoder design. These findings suggest the potential of RL-based approaches to understand neural representations of naturalistic faces.

neuroscience↗

bioRxiv2024

Functional Heterogeneity of Voice-Encoding Cortex Revealed by Clinical Language Mapping

Regions in the superior temporal sulcus and gyrus have been heavily implicated in voice-selective responses in human auditory cortex. Despite an apparent specialization for the encoding of human voice, research outside the auditory domain suggests that these areas likely participate in additional neural processes including speech processing and production. The aim of the current study was to combine results of electrophysiological recording and clinical stimulation mapping procedures in patients undergoing stereoelectroencephalography (sEEG) to explore potential functional heterogeneity in voice-encoding cortex. Both channels that demonstrated voice-encoding properties and channels critically implicated in language functioning were heavily concentrated in the left STG/S. Analysis of functional overlap revealed channels in the posterior STG/S that appear to be involved in both voice encoding and language. Strength of voice encoding in these functionally diverse sites was not significantly different from sites that were implicated in voice encoding alone. Our findings add to prior observations of functional heterogeneity in the STG/S and contribute to proposed models of speech perception. We discuss these results in the context of the utility of electrophysiological methods in mapping cortical networks and identifying regions essential for functioning.

neuroscience↗

bioRxiv2021

Categorical encoding of voice in human superior temporal cortex

The ability to recognize abstract features of voice during auditory perception is a complex, yet poorly understood, feat of human audition. For the listener, this occurs in near-automatic fasion to seamlessly extract complex cues from a highly variable auditory signal. Voice perception depends on specialized regions of auditory cortex, including superior temporal gyrus (STG) and superior temporal sulcus (STS). However, the nature of voice encoding at the cortical level remains poorly understoood. We leverage intracerebral recordings across human auditory cortex during presentation of voice and non-voice acoustic stimuli to examine voice encoding in auditory cortex, in eight patient-participants undergoing epilepsy surgery evaluation. We show that voice-selectivity increases along the auditory hierarchy from supratemporal plane (STP) to the STG and STS. Results show accurate decoding of vocalizations from human auditory cortical activity even in the complete absence of linguistic content. These findings show an early, less-selective temporal window of neural activity in the STG and STS followed by a sustained, strongly voice-selective window. We then developed encoding models that demonstrate divergence in the encoding of acoustic features along the auditory hierarchy, wherein STG/STS responses were best explained by voice category as opposed to the acoustic features of voice stimuli. This is in contrast to neural activity recorded from STP, in which responses were accounted for by acoustic features. These findings support a model of voice perception that engages categorical encoding mechanisms within STG and STS. Significance StatementVoice perception occurs via specialized networks in higher order auditory cortex, yet how voice features are encoded remains a central unanswered question. With human intracerebral recordings of auditory cortex, we provide evidence for categorical encoding of voice in STG and STS and occurs in the absence of linguistic content. This selectivity strengthens after an initial onset response and cannot be explained by simple acoustic features. Together, these data support the existence of sites within STG and STS that are specialized for voice perception.

neuroscience↗

bioRxiv2018

Sensorimotor cortical-subthalamic network dynamics during force generation

The subthalamic nucleus (STN) is proposed to participate in pausing, or alternately, in dynamic scaling of behavioral responses, roles that have conflicting implications for understanding STN function in the context of deep brain stimulation (DBS) therapy. To examine the nature of event-related STN activity and subthalamic-cortical dynamics, we performed primary motor and somatosensory electrocorticography while subjects (n=10) performed a grip force task during DBS implantation surgery. The results provide the first evidence from humans that STN gamma activity can predict activity in the cortex both prior to and during movement, consistent with the idea that the STN participates in both motor planning and execution. We observed that STN activity appeared to facilitate movement: while both movement onset and termination both coincided with STN-cortical phase-locking, narrow-band gamma power was positively correlated with grip force, and event-related causality measures demonstrated that STN gamma activity predicted cortical gamma activity during movement. STN participation in somatosensory integration also was demonstrated by casual analysis. Information flow from the STN to somatosensory cortex was observed for both beta and gamma range frequencies, specific to particular movement periods and kinematics. Interactions in beta activity between the STN and somatosensory cortex, rather than motor cortex, predicted PD symptom severity. Thus, the STN contributes to multiple aspects of sensorimotor behavior dynamically across time.

neuroscience↗