Jascha Achterberg

@achterbrain.bsky.social

2262 Followers 920 Following

Neuroscience & AI at University of Oxford and University of Cambridge | Principles of efficient computations + learning in brains, AI, and silicon 🧠 NeuroAI | Gates Cambridge Scholar www.jachterberg.com

Picture of Central Park, NYC
Screenshot of first page of https://arxiv.org/pdf/2310.13018.pdf
Axiomatic design NorthPole, an architecture and a programming model for neural inference, reimagines (Fig. 1) the interaction between compute and memory by embodying 10 interrelated, synergistic axioms that build on brain-inspired computing (14, 15). Within the broad domain of neural inference, NorthPole co-optimizes architecture, algorithms, and software and enables a simple input-output model (16).
Screenshot from the article front page
Neuro4ML logo
Text on image: The majority of the work presented here was undertaken as part of C. Sharpe’s honours year at the University of Sydney, but was originally conceived by G. F. Lewis. We would like to thank A. Arbey et al. for making their AlterBBN code publicly available as this paper would not have been possible without it. G. F. Lewis received no funding to support this research. C. Sharpe would also like to thank NSW police for ensuring a swift return of his belongings, including his laptop, after having them burgled from his house a few weeks before this paper’s submission. Additionally, he would like to thank his neighbour, Gary, who spotted the burglar and, rather than simply phoning the police and staying put, decided to yell ‘you better run fast mate’ before chasing the man down the street, tackling him, pinning him to the ground, and then calling the police, all while still in his pyjamas and a sleepy daze.
screenshot of graphic and abstract of linked paper
Plot from FT article showing that people who grow up amidst weaker economic conditions tend to have more zero-sum mindsets and demotivating beliefs as adults.
Screenshot of the webpage of the program
Picture of second court of St John’s College Cambridge on a sunny day, taken through a window.
How To Choose a Good Scientific Problem Choosing good problems is essential for being a good scientist. But what is a good problem, and how do you choose one? The subject is not usually discussed explicitly within our profession. Scientists are expected to be smart enough to figure it out on their own and through the observation of their teachers. This lack of explicit discussion leaves a vacuum that can lead to approaches such as choosing problems that can give results that merit publication in valued journals, resulting in a job and tenure.
Figure 3 from the linked paper, showing different learning rules in a small neural network model.
Multimodal units fuse-then-accumulate evidence across channels Key Points We demonstrate that multimodal units are not necessary for accuracy or balancing speed/accuracy in classical multisensory tasks. We introduce a novel set of tasks, based on comodulating the signals from multiple channels, in which multimodal units are critical. We show that multimodal units enable networks to implement a fuse-then-accumulate algorithm, which excels in naturalistic settings, like predator-prey interactions, and is optimal for a wide class of multimodal problems. Finally, we explore the link between single neurons properties and network behaviour.