Neural data

This talk explores how combining neural data from multiple brains with deep networks could benefit both neuroscience and AI. It presents approaches to mapping information across brains without shared observations and considers how collective neural information might guide new discoveries. The same perspective motivates alternative ways to train and interpret artificial networks.

Disentangling independent latent components from neural data is a desirable goal in neuroscience. Independent component analysis (ICA) has been used to identify disentangled latent neural trajectories and their corresponding sub-brain maps from recorded imaging sequences. However, enforcing full independence between all latent components may be overly restrictive, as each independent neural trajectory and sub-brain map results in a rank-1 imaging sequence. Additionally, the connectivity inferred from a rank-1 sub-brain map produces a rank-1 connectivity matrix, which may lack expressiveness. T

…for improving our understanding of neural computation. In this work, we develop a multimodal contrastive learning approach for neural data that can be fine-tuned for different downstream tasks, including inference of cell-type and brain location. We utilize this a

Although each of us was once a baby, infant consciousness remains mysterious and there is no received view about when, and in what form, consciousness first emerges. Some theorists defend a ‘late-onset’ view, suggesting that consciousness requires cognitive capacities which are unlikely to be in place before the child’s first birthday at the very earliest. Other theorists defend an ‘early-onset’ account, suggesting that consciousness is likely to be in place at birth (or shortly after) and may even arise during the third trimester. Progress in this field has been difficult, not just because of

Biological visual systems learn complex representations of the world that support a wide range of cognitive behaviors without using a large number of labelled examples. The efficient coding hypothesis suggests that this is accomplished by adapting the sensory representation to the statistics of the input signal, i.e. in a way that facilitates redundancy reduction. Visual signals have several clear sources of redundancy. They evolve slowly in time, since temporally adjacent inputs typically correspond to different views of the same scene, which in turn are usually more similar than views of dis

Clinical assessment of consciousness is a significant issue, with recent research suggesting some brain-damaged patients who are assessed as unconscious are in fact conscious. Misdiagnosis of consciousness can also be detrimental when it comes to general anaesthesia, causing numerous psychological problems, including post-traumatic stress disorder. Avoiding awareness with overdose of anaesthetics, however, can also lead to cognitive impairment. Currently available objective assessment of consciousness is limited in accuracy or requires expensive equipment with major barriers to translation. I