@article {68938,
	title = {Learning produces an orthogonalized state machine in the hippocampus.},
	journal = {Nature},
	volume = {640},
	year = {2025},
	month = {2025 February 12},
	abstract = {<p>Cognitive maps confer animals with flexible intelligence by representing spatial, temporal and abstract relationships that can be used to shape thought, planning and behaviour. Cognitive maps have been observed in the hippocampus1, but their algorithmic form and learning mechanisms remain obscure. Here we used large-scale, longitudinal two-photon calcium imaging to record activity from thousands of neurons in the CA1 region of the hippocampus while mice learned to efficiently collect rewards from two subtly different linear tracks in virtual reality. Throughout learning, both animal behaviour and hippocampal neural activity progressed through multiple stages, gradually revealing improved task representation that mirrored improved behavioural efficiency. The learning process involved progressive decorrelations in initially similar hippocampal neural activity within and across tracks, ultimately resulting in orthogonalized representations resembling a state machine capturing the inherent structure of the task. This decorrelation process was driven by individual neurons acquiring task-state-specific responses (that is, {\textquoteright}state cells{\textquoteright}). Although various standard artificial neural networks did not naturally capture these dynamics, the clone-structured causal graph, a hidden Markov model variant, uniquely reproduced both the final orthogonalized states and the learning trajectory seen in animals. The observed cellular and population dynamics constrain the mechanisms underlying cognitive map formation in the hippocampus, pointing to hidden state inference as a fundamental computational principle, with implications for both biological and artificial intelligence.</p>
},
	doi = {10.1038/s41586-024-08548-w},
	url = {https://doi.org/10.1038/s41586-024-08548-w},
	author = {Sun, Weinan and Winnubst, Johan and Natrajan, Maanasa and Lai, Chongxi and Kajikawa, Koichiro and Michaelos, Michalis and Gattoni, Rachel and Stringer, Carsen and Flickinger, Daniel and Fitzgerald, James E and Spruston, Nelson}
}
@article {70183,
	title = {Stability through plasticity: Finding robust memories through representational drift.},
	journal = {Proc Natl Acad Sci U S A},
	volume = {122},
	year = {2025},
	month = {2025 Nov 11},
	pages = {e2500077122},
	abstract = {<p>Memories are believed to be stored in synapses and retrieved by reactivating neural ensembles. Learning alters synaptic weights, which can interfere with previously stored memories that share the same synapses, creating a trade-off between plasticity and stability. Interestingly, neural representations change even in stable environments, without apparent learning or forgetting-a phenomenon known as representational drift. Theoretical studies have suggested that multiple neural representations can correspond to a memory, with postlearning exploration of these representation solutions driving drift. However, it remains unclear whether representations explored through drift differ from those learned or offer unique advantages. Here, we show that representational drift uncovers noise-robust representations that are otherwise difficult to learn. We first define the nonlinear solution space manifold of synaptic weights for fixed input-output mappings, which allows us to disentangle drift from learning and forgetting and simulate drift as diffusion within this manifold. Solutions explored by drift have many inactive and saturated neurons, making them robust to weight perturbations due to noise or continual learning. Such solutions are prevalent and entropically favored by drift, but their lack of gradients makes them difficult to learn and nonconducive to future learning. To overcome this, we introduce an allocation procedure that selectively shifts representations for new stimuli into a learning-conducive regime. By combining allocation with drift, we resolve the trade-off between learnability and robustness.</p>
},
	keywords = {Humans, Learning, Memory, Models, Neurological, Neuronal Plasticity, Neurons, Synapses},
	issn = {1091-6490},
	doi = {10.1073/pnas.2500077122},
	url = {https://doi.org/10.1073/pnas.2500077122},
	author = {Natrajan, Maanasa and Fitzgerald, James E}
}
