Projects & Publications

The goal of my work is to uncover the computational principle connecting biological and artificial intelligence. I use all data available to the modern neuroscience lab to uncover the mysteries of complex thought and translate our findings into more powerful artificial agents. My empirical work is currently mostly based on neuroimaging data of the developing brain, large scale data of human decision making and single cell recordings in non-human primates. My modelling work currently focuses on artificial neural network models, using a combination of supervised training, reinforcement learning, and biologically inspired wiring rules.

Many of my current ideas on this topic are reviewed in the paper: Building artificial neural circuits for domain-general cognition: a primer on brain-inspired systems-level architecture https://arxiv.org/abs/2303.13651 

Ongoing

• I started work on a new theoretical piece on identifying joint computational principles underlying distributed computations in brains and silicon chips. This is at very early stages and no preprint is expected soon. If this is of interest to you, please reach out!

• Uncovering the neuronal algorithms underlying structural inference and reasoning in a large population of PFC neurons. 

  • We are currently working on a preprint. A rough overview of results (presented at CCN 2024) can be found here: Maze project

• We recently introduced the new spatially-embedded Recurrent Neural Network model which can recapitulate numerous neuroscientific findings, from large scale connectome structure down to functional codes used by single neurons. We are in the process of expanding this model with further biophysical constrains and a theoretical analysis of dynamics for optimising network communication. 

  • See details of project here

  • I have ongoing projects to translate these principles into large scale artificial neural networks, capable of open-ended task solving.

Selected publications

• Artificial neural networks: Achterberg, J., Akarca, D., Strouse D., Duncan, J. & Astle, D (2023, preprint). Spatially embedded recurrent neural networks reveal widespread links between structural and functional neuroscience findings. Nature Machine Intelligence. doi: https://www.nature.com/articles/s42256-023-00748-9 

  • I gave a seminar on this paper at SNUFA: https://www.youtube.com/watch?v=DaG92jBCu68&t 

• Highly functional biological networks: Achterberg, J., Kadohisa, M., Watanabe, K., Kusunoki, M., Buckley, M., & Duncan, J. (2021). A One-Shot Shift from Explore to Exploit in Monkey Prefrontal Cortex. The Journal Of Neuroscience, 42(2), 276-287. doi: 10.1523/JNEUROSCI.1338-21.2021 

• Human decision making: Ruggeri, K., Panin, A., Vdovic, M., Veckalov, B., Abdul-Salaam, N, Achterberg, J., [...], & Garcia-Garzon, E. (2022). The globalizability of temporal discounting. Nature Human Behaviour doi: 10.1038/s41562-022-01392-w 

All academic publications

A complete list of my academic publications is available on my Google Scholar profile.

Other publications

• "Domain-general cognition in brains and artificial neural networks". Jascha Achterberg. Presentation and interview as part of the Researcher App Series on AI in Neuroscience 2023. Recording available on: https://www.researcher-app.com/paper/14604093 

• “Intelligence in brain and machines”. Jascha Achterberg. Short video for the Cambridge Science Festival 2021. Hosted on YouTube.

• “Want to Build Intelligent Machines? Mind the Brain!”. Jascha Achterberg. Article in the 2020 version of Gates Cambridge’s magazine “The Scholar”