Bassett, J. P. | bioRxiv Science

bioRxiv2025

From movement to cognitive maps: recurrent neural networks reveal how locomotor development shapes hippocampal spatial coding

AO_SCPLOWBSTRACTC_SCPLOWThe hippocampus contains neurons whose firing correlates with an animals location and orientation in space. Collectively, these neurons are held to support a cognitive map of the environment, enabling the recall of and navigation to specific locations. Although recent studies have characterised the timelines of spatial neuron development, no unifying mechanistic model has yet been proposed. Moreover, the processes driving the emergence of spatial representations in the hippocampus remain unclear (Tan et al., 2017). Here, we combine computational analysis of postnatal locomotor development with a recurrent neural network (RNN) model of hippocampal function to demonstrate how changes in movement statistics - and the resulting sensory experiences - shape the formation of spatial tuning. First, we identify distinct developmental stages in rat locomotion during open-field exploration using published experimental data. Then, we train shallow RNNs to predict upcoming visual stimuli from concurrent visual and vestibular inputs, exposing them to trajectories that reflect progressively maturing locomotor patterns. Our findings reveal that these changing movement statistics drive the sequential emergence of spatially tuned units, mirroring the developmental timeline observed in rats. The models generate testable predictions about how spatial tuning properties mature - predictions we confirm through analysis of hippocampal recordings. Critically, we demonstrate that replicating the specific statistics of developmental locomotion - rather than merely accelerating sensory change - is essential for the emergence of an allocentric spatial representation. These results establish a mechanistic link between embodied sensorimotor experience and the ontogeny of hippocampal spatial neurons, with significant implications for neurodevelopmental research and predictive models of navigational brain circuits.*

neuroscience↗

bioRxiv2017

Self-organised attractor dynamics in the developing head direction circuit.

Head direction (HD) cells signal the orientation of an animals head relative to its environment. During post-natal development, HD cells are the earliest spatially modulated neurons in the hippocampal circuit to emerge. However, before eye-opening, HD cell responses in rat pups carry low directional information content and are directionally unstable. Using Bayesian decoding, we characterise this instability and identify its source: despite the directional signal being internally coherent, it consistently under-signals angular head velocity (AHV), incompletely shifting in proportion to head turns. We find evidence that geometric cues (corners) can be used to mitigate this under-signalling, and stabilise the directional signal even before eye-opening. Crucially, even when directional firing cannot be stabilised, ensembles of unstable HD cells show short-timescale (1-10 sec) temporal and spatial couplings consistent with an adult-like HD network, through which activity drifts unanchored to landmark cues. The existence of fixed spatial and temporal offsets across co-recorded cells and of an AHV-responsive signal, even before HD responses become spatially stable, suggests that the HD circuit is assembled through internal, self-organising processes, without reference to external landmarks. The HD network is widely modelled as a continuous attractor whose output is one coherent activity peak, updated during movement by angular head velocity (AHV) signals, and anchored by landmark cues. Our findings present strong evidence for this model, and demonstrate that the required network circuitry is in place and functional during development, independent of reference to landmark information.

neuroscience↗