stimulus
Neural decoding from stereotactic EEG: accounting for electrode variability across subjects
Deep learning based neural decoding from stereotactic electroencephalography (sEEG) would likely benefit from scaling up both dataset and model size. To achieve this, combining data across multiple subjects is crucial.
Guiding Large Language Models via Directional Stimulus Prompting
T5) to generate an auxiliary directional stimulus prompt for each input instance.
From deep learning to mechanistic understanding in neuroscience: the structure of retinal prediction
Hidenori Tanaka, Aran Nayebi, Niru Maheswaranathan, Lane McIntosh, Stephen Baccus, Surya Ganguli
For eachstimulus,ourextractedcomputational mechanisms areconsistent withprior scientific literature, and in one case yields a new mechanistic hypothesis.
Efficient inference for time-varying behavior during learning
Nicholas G. Roy, Ji Hyun Bak, Athena Akrami, Carlos Brody, Jonathan W. Pillow
Neural Information Processing Systems http://nips.cc/
Generalisation of structural knowledge in the hippocampal-entorhinal system
James Whittington, Timothy Muller, Shirely Mark, Caswell Barry, Tim Behrens
A central problem to understanding intelligence is the concept of generalisation. This allows previously learnt structure to be exploited to solve tasks in novel situations differing in their particularities.
Beauty-in-averageness and its contextual modulations: A Bayesian statistical account
Neural Information Processing Systems http://nips.cc/
Training biologically plausible recurrent neural networks on cognitive tasks with long-term dependencies Supplementary material A Demand for biological RNNs with long time-scale dependencies
Emergence of grid-like representations by training recurrent neural networks to perform spatial localization.
