Balanced Predictive Attractor Networks: A Compact Excitatory–Inhibitory Testbed for Anytime and Sequential Inference
IEEE Transactions on Cognitive and Developmental Systems · with S. Panwar
PhD scholar
Jeet B. Lahiri
School of Computing and Electrical Engineering · IIT Mandi
About
Jeet is a research scholar working at the intersection of signal processing and computational neuroscience. His research develops compact, deployable, and interpretable methods for clinical EEG; studies the reliability of neural AI beyond benchmark datasets; and explores bio-inspired recurrent systems for efficient sequential inference.
His current interests also include information-preserving representations of multivariate time series and nonlinear descriptions of neural dynamics.
Research interests
Signal processingNeuroscienceEEGNonlinear dynamicsClinical AIBio-inspired computation
Publications
Robust multi-centre interictal EEG biomarker for distinguishing epilepsy from mimickers
Journal of Neural Engineering 23 (3), 036037 · with P. Agarwal, S. Kushwaha, M. Singh, and S. Panwar
Femtomodels for EEG Artifact Removal: A Parameter Lower-Bound for Generalisable EOG Denoising
IEEE ICASSP 2026 · with A. Kulkarni and S. Panwar
Evaluating the clinical readiness of artificial intelligence in EEG-based epilepsy diagnosis
Journal of Neural Engineering 22 (6), 066026 · with P. Agarwal, S. Kushwaha, M. Singh, and S. Panwar
MicroWaveNet: Lightweight CBAM-Augmented Wavelet-Attentive Networks for Robust EEG Denoising
IEEE MLSP 2025 · with A. Kulkarni and S. Panwar