Hippocampal Reactivation of Random Trajectories Resembling Brownian Diffusion.
By Federico Stella, Péter Baracskay, J. O’Neill, J. Csicsvari. 2019
107 citations recorded by the source corpus.
Hippocampal activity patterns representing movement trajectories are reactivated in immobility and sleep periods, a process associated with memory recall, consolidation, and decision making. It is thought that only fixed, behaviorally relevant patterns can be reactivated, which are stored across hippocampal synaptic connections. To test whether some generalized rules govern reactivation, we examined trajectory reactivation following non-stereotypical exploration of familiar open-field environments. We found that random trajectories of varying lengths and timescales were reactivated, resembling that of Brownian motion of particles. The animals' behavioral trajectory did not follow Brownian diffusion demonstrating that the exact behavioral experience is not reactivated. Therefore, hippocampal circuits are able to generate random trajectories of any recently active map by following diffusion dynamics. This ability of hippocampal circuits to generate representations of all behavioral outcome combinations, experienced or not, may underlie a wide variety of hippocampal-dependent cognitive functions such as learning, generalization, and planning.
Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Cellular and Molecular Neuroscience
- Andrea Soltoggio
- Sebastian Risi
- Jeff Clune
- David Tingley
- Stefano Fusi
- Gido M. van de Ven
- Maxim Bazhenov
- Risto Miikkulainen
In Cognitive Neuroscience
- Sebastian Risi
- Andrea Soltoggio
- Jeff Clune
- Risto Miikkulainen
- David Tingley
- J. Krichmar
- Stefano Fusi
- Gido M. van de Ven
Citations in the corpus
- Replay in Deep Learning: Current Approaches and Missing Biological Elements (2021, neutral): “Replay is shown to include activity sampled from past experiences as well as novel activity that corresponds to future possibilities (Johnson & Redish, 2007) and random movements in place cells (Stella, Baracskay, ONeill, & Csicsvari, 2019).”
- On the methods for reactivation and replay analysis (2020, background): “Another approach consists in fixing the number of spikes per bin, and thus to use bins of variable size [32].”
- Biological underpinnings for lifelong learning machines (2022, background): “Intriguingly, a recent study 52 found that hippocampal activation patterns do not always recapitulate waking experiences; seemingly random activation patterns are also observed.”