Lindsay, Grace W.

A deep learning framework for neuroscience

Richards, B. A., Lillicrap, T. P., Beaudoin, P., Bengio, Y., Bogacz, R., Christensen, A., Clopath, C., Costa, R. P., de Berker, A., Ganguli, S., et al. (2019) · Nature Neuroscience

Neural Networks and ApplicationsNeural dynamics and brain function

Cited by 1,027

The neuroconnectionist research programme

Doerig, A., Sommers, R. P., Seeliger, K., Richards, B., Ismael, J., Lindsay, G. W., Kording, K. P., Konkle, T., van Gerven, M. A. J., Kriegeskorte, N., et al. (2023) · Nature Reviews Neuroscience

Neural Networks and ApplicationsNeural dynamics and brain function

Cited by 233

Consciousness in Artificial Intelligence: Insights from the Science of Consciousness

Butlin, P., Long, R., Elmoznino, E., Bengio, Y., Birch, J., Constant, A., Deane, G., Fleming, S. M., Frith, C., Ji, X., et al. (2023) · arXiv

Philosophy and Theoretical SciencePsychiatry, Mental Health, Neuroscience

Cited by 196

Identifying indicators of consciousness in AI systems

Butlin, P., Long, R., Bayne, T., Bengio, Y., Birch, J., Chalmers, D., Constant, A., Deane, G., Elmoznino, E., Fleming, S. M., et al. (2026) · Trends in Cognitive Sciences

Philosophy and Theoretical SciencePsychiatry, Mental Health, Neuroscience

Cited by 56

Deep neural networks are not a single hypothesis but a language for expressing computational hypotheses

Golan, T., Taylor, J., Schütt, H., Peters, B., Sommers, R. P., Seeliger, K., Doerig, A., Linton, P., Konkle, T., van Gerven, M., et al. (2023) · Behavioral and Brain Sciences

Visual perception and processing mechanismsVisual Attention and Saliency Detection

Cited by 12

Divergent representations of ethological visual inputs emerge from supervised, unsupervised, and reinforcement learning

Lindsay, G. W., Merel, J., Mrsic-Flogel, T., & Sahani, M. (2021) · arXiv

Reinforcement Learning in RoboticsDomain Adaptation and Few-Shot Learning

Cited by 4