The overlaid network illustrates a mathematical model, which represents the astrocyte as a network of processes that enables multi-synaptic communication and supports associative memory function.

Data credit: Leyka Nagendren, Cagla Eroglu.
Artist Credit: Annette Hui, IBM Research

The raw image was captured in October 2023 using an Olympus FV 3000 confocal microscope with a 60x objective lens and a z-step size of 0.5 μm across a 50–60 μm z-stack for high magnification and resolution.

A custom Imaris extension, 3D Sholl Analysis, was used to quantify the number of process intersections at 5 μm intervals from the cell soma, marked by the sphere, thereby visualizing the highly complex network of processes of the astrocyte.

The image summarizes the main message of our paper: astrocyte = network of processes that engage in information exchange and computation. The image shows a 3D reconstruction of an astrocyte from the visual cortex using Imaris Bitplane 9.9.

Key takeaways:
🔷 Astrocytes compute.
🔷 Dense Associative Memories can be built using astrocytes.
🔷 From the computational perspective astrocyte is a network of processes.
🔷 Memories can be stored, at least partially, in the biochemical machinery inside the astrocyte, as opposed to just synapses.

More info here: nfam.vizhub.ai

Website: nfam.vizhub.ai

Submissions on Open Review: openreview.net/group?id=ICL...

Associative memory is a prominent emerging paradigm in the landscape of mainstream AI and I expect exciting developments in this area in the next few years. Please take a look at the call for papers and submit by the deadline: February 14. It will be interesting! 😀

Please check out the post in the first quote and join us at the Scientific Methods for Understanding Deep Learning workshop at #NeurIPS2024 on December 15 to learn more.

We demonstrate empirically that spurious states exist in conventional DMs. Their existence is a distinct prediction of DenseAM theory, yet these states have been overlooked in the DM literature. Spurious states mark the onset of the memorization-generalization transition in DMs.

In our latest work, we leverage the correspondence between DenseAMs and Diffusion Models (DMs) to make a theoretical prediction: a similar phenomenon must occur during the memorization-to-generalization transition in DMs.

DenseAM theory predicts a distinct phenomenon when the amount of data surpasses the critical memory capacity: spurious states emerge. Spurious states are local minima of the energy function that fail to store each pattern in a unique basin of attraction (two or more patterns sharing the same basin).