Hippocampal place and time cells encode spatial and temporal aspects of experience. Both have the same neural substrate, but have been modeled as having different functions and mechanistic origins, place cells as continuous attractors, and time cells as leaky integrators. Here, we show that both types emerge from two dynamical regimes of a single recurrent network (RNN) modeling hippocampal CA3 as predictive autoencoder. During spatial navigation, the network generates stable attractor-like place fields. But trained on temporally structured inputs, the network produces sequentially broadened fields, recapitulating time cells. By varying spatio-temporal input patterning, we observe hidden units transition smoothly between time cell-like and place cell-like representations. These results suggest a shared origin, but task-driven difference, between place and time cells.

Poster presented at the COSYNE (Computational System Neuroscience) 2026.

The splashback radius defines the boundary of a dark matter halo, where infalling dark matter particles reach their first apocenter and turn around. In this study, we define the novel splashback depth $\mathcal{D}$ and width $\mathcal{W}$ to examine how the splashback features of dark matter haloes are affected by the physical properties of haloes themselves. We use the largest simulation run in the hydrodynamic MillenniumTNG project. Our results show that the splashback depth has a strong dependence on the halo mass and the splashback width has the strongest dependence on halo peak height. We provide the fitting functions of the splashback depth and width in terms of halo mass, redshift, peak height, concentrations and halo formation time. The depth and width are therefore considered to be a long term memory tracker of haloes since they depend more on accumulative physical properties, e.g., halo mass, peak height and halo formation time. In contrast, they have little dependence on the short term factors such as halo mass accretion rate and most recent major merger time. They are shaped primarily by the halo’s assembly history, which exerts a stronger influence on the inner density profile than short-term dynamical processes. The splashback depth and width can therefore be used to characterise the halo’s history and inner structure.

The realm of dreams remains relatively uncharted due to the reliance of subjective reports to access dream content. Building a dream decoding model is difficult due to the difficulty in collecting large sample sizes of dream experience. We trained an encoding model to generate EEG signals from deep neural network feature maps of visual images viewed during waking, and used the model to try and predict EEG signals collected during dreaming.

Poster presented at the CNS (Cognitive Neuroscience Society) 2024 Annual Meeting.