Ash Robbins

PULSE, a foundation model trained on neural recordings, reveals how disease variants affect network development and how circuits encode information in brain organoids.

The paper presents a cloud-based pipeline for analyzing and visualizing neuronal electrophysiology data from HD-MEAs, demonstrating its utility in simplifying data analysis and understanding neural activity.

SpikeLab is a text-to-analysis framework for neural spike data that ensures correct and reproducible results using agentic systems, outperforming other models in a controlled benchmark.

This study uses mouse forebrain organoids to show how variations in cellular composition influence the self-organization of neural circuits, revealing intrinsic principles of forebrain circuit assembly.

This study demonstrates goal-directed learning in cortical organoids using a closed-loop electrophysiology framework and artificial reinforcement learning.

This paper introduces an automated brain organoid platform with IoT integration for continuous monitoring and control, showing increased neural activity and stable firing rates during automated media exchange.

This study shows that the identity of MGE-derived cortical interneurons can be dynamically influenced by their environment, with human 3D models promoting Pvalb expression over Sst.

RT-Sort detects and sorts action potentials in real time with millisecond latencies using propagation patterns and a convolutional neural network.

The paper uses high-density electrophysiology to study epileptiform activity in human hippocampal slices from epilepsy patients, revealing unexpected network oscillations in dentate gyrus slices.