5 papers
Neuromorphic Energy-Aware Learning for Adaptive Deep Brain Stimulation
Binh Nguyen, Colleen Josephson, Mircea Teodorescu +2
Neuromorphic and edge computing research has focused on reducing the inference cost of neural network controllers, yet in physical closed-loop systems the actuator can rival or exc…
Implicit Behavioral Decoding from Next-Step Spike Forecasts at Population Scale
John R. Minnick, Jesus Gonzalez-Ferrer, Kamran Hussain +6
Closed-loop brain-computer interfaces often require both a forecast of upcoming neural population activity and a readout of the animal's behavioral state. A single Mamba forecaster…
SpikeProphecy: A Large-Scale Benchmark for Autoregressive Neural Population Forecasting
John R. Minnick, Jinghui Geng, Kamran Hussain +6
Neural population models, which predict the joint firing of many simultaneously recorded neurons forward in time, are typically evaluated by a single aggregate Pearson correlation…
NeuroAI and Beyond: Bridging Between Advances in Neuroscience and ArtificialIntelligence
Anthony Zador, Jean-Marc Fellous, Terrence Sejnowski +28
Neuroscience and Artificial Intelligence (AI) have made impressive progress in recent years but remain only loosely interconnected. Based on a workshop convened by the National Sci…
Learnable Sparsification of Die-to-Die Communication via Spike-Based Encoding
Joshua Nardone, Ruijie Zhu, Joseph Callenes +3
Efficient communication is central to both biological and artificial intelligence (AI) systems. In biological brains, the challenge of long-range communication across regions is ad…