1 citations · 2 across the 4 of their papers we have counts for
5 papers
A Scalable, Causal, and Energy Efficient Framework for Neural Decoding with Spiking Neural Networks
Georgios Mentzelopoulos, Ioannis Asmanis, Konrad P. Kording +3
Brain-computer interfaces (BCIs) promise to enable vital functions, such as speech and prosthetic control, for individuals with neuromotor impairments. Central to their success are…
The Landscape of Causal Discovery Data: Grounding Causal Discovery in Real-World Applications
Philippe Brouillard, Chandler Squires, Jonas Wahl +4
Causal discovery aims to automatically uncover causal relationships from data, a capability with significant potential across many scientific disciplines. However, its real-world a…
Homogenized Neural Activity and Connectivity Data
Quilee Simeon, Anshul Kashyap, Konrad P Kording +1
There is renewed interest in modeling and understanding the nervous system of the nematode (), as this small model system pro…
Neural decoding from stereotactic EEG: accounting for electrode variability across subjects
Georgios Mentzelopoulos, Evangelos Chatzipantazis, Ashwin G. Ramayya +5
Deep learning based neural decoding from stereotactic electroencephalography (sEEG) would likely benefit from scaling up both dataset and model size. To achieve this, combining dat…
Efficient optimization of ODE neuron models using gradient descent
Ilenna Simone Jones, Konrad Paul Kording
Neuroscientists fit morphologically and biophysically detailed neuron simulations to physiological data, often using evolutionary algorithms. However, such gradient-free approaches…