Biology subjects

Heys, J. G.

Publications and source records attributed to Heys, J. G..

4 recordsLinked to original sources

bioRxiv2026

Spatial and temporal representations are organized along a stable coding gradient in the medial entorhinal cortex

The medial entorhinal cortex (MEC) has classically been viewed as a spatial coding region, but growing evidence indicates that it also contains temporal representations. However, how spatial and temporal codes are organized within the MEC remains unclear. Here we recorded MEC neurons with high-density silicon probes while mice performed a MEC-dependent timing task and a virtual reality spatial navigation task in a similar head-fixed setup. We found that spatial and temporal representations were partially overlapping but systematically biased across the MEC population. Grid cells and non-grid cells with strong spatial tuning were less likely to show reliable time-locked activity during the timing task. In contrast, neurons with weaker spatial tuning more flexibly shifted their coding scheme to temporal coding during timing task. Moreover, spatial tuning strength and its negative relationship with temporal tuning were preserved in a distinct open field environment, indicating that the coding preferences of individual neurons are constrained by a stable network-level organization. Together, these findings suggest MEC is organized along a coding gradient, ranging from dedicated stable spatial coding neurons to more flexible spatial or temporal coding neurons which represent information according to cognitive demands.

neuroscience↗

bioRxiv2025

Hippocampal time cell dynamics evolve with learning to reflect cognitive demands

The hippocampus creates cognitive maps, or internal representations that reflect knowledge of the external world. Hippocampal time cells are thought to represent the temporal structure of experiences, or temporal context. However, it remains unknown whether hippocampal time cells display learning dynamics that reflect increased knowledge of the temporal relationships that define a context. To address this gap, we utilized a behavioral paradigm with a shaping curriculum that allows animals to systematically acquire knowledge of temporal structure, ultimately enabling them to perform a temporal Delayed Non-Match to Sample (tDNMS) task. We conducted two-photon calcium imaging on large populations of CA1 neurons as mice progressed through the curriculum--from their initial exposure to the task structure to their successful discrimination of context at the end of training. Time cells were present from the outset, yet their activity evolved with experience, both at the single-cell level and across the population. Notably, at key moments in the curriculum, time cell dynamics adapted to reflect whether mice generalized across contexts or discriminated between them. Our findings suggest that CA1 time cells not only represent temporal context but may also reflect the processes by which temporal relationships are utilized. Hippocampal time cells therefore serve as a cognitive map, representing temporal relationships in a manner that reflects cognitive demands.

neuroscience↗

bioRxiv2025

Structured experience shapes strategy learning and neural dynamics in the medial entorhinal cortex

Animals can solve new, complex tasks by reusing and adapting what theyve learned before. This kind of flexibility depends not just on having prior experience, but on how that experience was structured in the first place. The design of early training curriculum is especially important: poorly structured experiences can hinder abstraction and limit generalization, while carefully structured training promotes more flexible and adaptive behavior. Yet, the neural mechanisms supporting this process remain unclear. To investigate how early training shapes learning we first trained recurrent neural networks (RNNs) on variants of an odor-timing task previously used to study complex timing behavior in mice. We then tested the RNN predictions on how previous experience affects generalization using behavioral and electrophysiological recordings in mice trained on the same task using staged training sequences. RNNs and mice trained without well-structured early experience developed rigid strategies and made repeated errors. In contrast, those given more balanced early training were better able to generalize and showed similar neural activity patterns that reflected the tasks underlying temporal structure. Using dynamical systems approaches, we reveal a mechanism for this effect: networks trained with appropriately structured curricula developed distinct dynamical motifs that support the correct abstractions when complexity was increased. Networks that lacked early training or received remedial curricula developed single fixed-point solutions that failed to generalize beyond the training stimuli. Together, these findings demonstrate that it is not just the presence of prior experience, but its structure, that governs how flexible and generalizable knowledge emerges in both biological systems and computational models.

neuroscience↗

bioRxiv2023

Medial entorhinal cortex plays a specialized role in learning of flexible, context-dependent interval timing behavior

Episodic memory requires encoding the temporal structure of experience and relies on brain circuits in the medial temporal lobe, including the medial entorhinal cortex (MEC). Recent studies have identified MEC time cells, which fire at specific moments during interval timing tasks, collectively tiling the entire timing period. It has been hypothesized that MEC time cells could provide temporal information necessary for episodic memories, yet it remains unknown whether MEC time cells display learning dynamics required for encoding different temporal contexts. To explore this, we developed a novel behavioral paradigm that requires distinguishing temporal contexts. Combined with methods for cellular resolution calcium imaging, we find that MEC time cells display context-dependent neural activity that emerges with task learning. Through chemogenetic inactivation we find that MEC activity is necessary for learning of context-dependent interval timing behavior. Finally, we find evidence of a common circuit mechanism that could drive sequential activity of both time cells and spatially selective neurons in MEC. Our work suggests that the clock-like firing of MEC time cells can be modulated by learning, allowing the tracking of various temporal structures that emerge through experience.

neuroscience↗