Back

Hebb's Vision: The Structural Underpinnings of Hebbian Assemblies

Wagner-Carena, J. A.; Kate, S.; Riordan, T.; Abbasi-Asl, R.; Aman, J.; Amster, A.; Bodor, A. L.; Brittain, D.; Buchanan, J.; Buice, M. A.; Bumbarger, D. J.; Collman, F.; da Costa, N. M.; Denman, D. J.; de Vries, S. E.; Joyce, E.; Kapner, D.; King, C. W.; Larkin, J. D.; Lecoq, J.; Mahalingam, G.; Millman, D.; Molter, J.; Morrison, C.; Reid, R. C.; Schneider-Mizell, C. M.; Daniel, S.; Suckow, S.; Takasaki, K. T.; Torres, R.; Vumbaco, D.; Waters, J.; Wyrick, D. G.; Yin, W.; Zhuang, J.; Takeno, M.; Mihalas, S.; Berteau, S.

2025-04-24 neuroscience
10.1101/2025.04.24.649900 bioRxiv
Show abstract

1In 1949, Donald Hebb proposed that groups of neurons that activate stereotypically form the organizational building blocks of perception, cognition, and behavior. Finding the structural underpinning of such assemblies has been technically challenging, due to a lack of large-scale structure-activity maps. Here, we analyze this relation using a novel dataset that links in vivo optical physiology to connectivity using postmortem elec-tron microscopy (EM). From the fluorescence traces, we extract neural assemblies from higher-order correlations in neural activity. Physiologically, we show that these assemblies exhibit properties consistent with Hebbs theory, including more reliable responses to repeated natural movie inputs than size-matched random ensembles and superior decoding of visual stimuli. Structurally, we find that neurons that participate in assemblies are significantly more integrated into the structural network than those that do not. Contrary to Hebbs original prediction, we do not observe a marked increase in the strength of monosynaptic excitatory connections between cells participating in the same assembly. However, we find significantly stronger indirect feed-forward inhibitory connections targeting cells in other assemblies. These results show that assemblies can be useful components of perception, and, surprisingly, they are delineated by mutual inhibition.

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.