activity
20142024
most citedIntegrating LLM, EEG, and Eye-Tracking Biomarker Analysis for Word-Level Neural State Classification in Semantic Inference Reading Comprehension

2 citations · 5 across the 7 of their papers we have counts for

collaborators

7 papers

cs.NE2024

Towards Chip-in-the-loop Spiking Neural Network Training via Metropolis-Hastings Sampling

Ali Safa, Vikrant Jaltare, Samira Sebt +4

This paper studies the use of Metropolis-Hastings sampling for training Spiking Neural Network (SNN) hardware subject to strong unknown non-idealities, and compares the proposed ap…

cs.HC2024

From Word Embedding to Reading Embedding Using Large Language Model, EEG and Eye-tracking

Yuhong Zhang, Shilai Yang, Gert Cauwenberghs +1

Reading comprehension, a fundamental cognitive ability essential for knowledge acquisition, is a complex skill, with a notable number of learners lacking proficiency in this domain…

eess.SP2023

An Exploration of Optimal Parameters for Efficient Blind Source Separation of EEG Recordings Using AMICA

Gwenevere Frank, Seyed Yahya Shirazi, Jason Palmer +3

EEG continues to find a multitude of uses in both neuroscience research and medical practice, and independent component analysis (ICA) continues to be an important tool for analyzi…

cs.ET2023

Multi-level, Forming Free, Bulk Switching Trilayer RRAM for Neuromorphic Computing at the Edge

Jaeseoung Park, Ashwani Kumar, Yucheng Zhou +9

Resistive memory-based reconfigurable systems constructed by CMOS-RRAM integration hold great promise for low energy and high throughput neuromorphic computing. However, most RRAM…

cs.CL20232 cited

Integrating LLM, EEG, and Eye-Tracking Biomarker Analysis for Word-Level Neural State Classification in Semantic Inference Reading Comprehension

Yuhong Zhang, Qin Li, Sujal Nahata +4

With the recent proliferation of large language models (LLMs), such as Generative Pre-trained Transformers (GPT), there has been a significant shift in exploring human and machine…

cs.NE20171 cited

Membrane-Dependent Neuromorphic Learning Rule for Unsupervised Spike Pattern Detection

Sadique Sheik, Somnath Paul, Charles Augustine +1

Several learning rules for synaptic plasticity, that depend on either spike timing or internal state variables, have been proposed in the past imparting varying computational capab…