To flexibly adapt to new situations, our brains must understand the regularities in the world, as well as those in our own patterns of behaviour. A wealth of findings is beginning to reveal the algorithms that we use to map the outside world. However, the biological algorithms that map the complex structured behaviours that we compose to reach our goals remain unknown. Here we reveal a neuronal implementation of an algorithm for mapping abstract behavioural structure and transferring it to new scenarios. We trained mice on many tasks that shared a common structure (organizing a sequence of goals) but differed in the specific goal locations. The mice discovered the underlying task structure, enabling zero-shot inferences on the first trial of new tasks. The activity of most neurons in the medial frontal cortex tiled progress to goal, akin to how place cells map physical space. These 'goal-progress cells' generalized, stretching and compressing their tiling to accommodate different goal distances. By contrast, progress along the overall sequence of goals was not encoded explicitly. Instead, a subset of goal-progress cells was further tuned such that individual neurons fired with a fixed task lag from a particular behavioural step. Together, these cells acted as task-structured memory buffers, implementing an algorithm that instantaneously encoded the entire sequence of future behavioural steps, and whose dynamics automatically computed the appropriate action at each step. These dynamics mirrored the abstract task structure both on-task and during offline sleep. Our findings suggest that schemata of complex behavioural structures can be generated by sculpting progress-to-goal tuning into task-structured buffers of individual behavioural steps.

Mammalian place cells are active at one or a few specific locations in the environment. First described in the rodent hippocampus, and subsequently across the mammalian evolutionary tree, place cells have now been discovered in the larval zebrafish telencephalon.

While a hippocampal-cortical dialogue is generally thought to mediate memory consolidation, which is crucial for engram function, how it works remains largely unknown. Here, we examined the interplay of neural signals from the retrosplenial cortex (RSC), a neocortical region, and from the hippocampus in memory consolidation by simultaneously recording sharp-wave ripples (SWRs) of dorsal hippocampal CA1 and neural signals of RSC in free-moving mice during the delayed spatial alternation task (DSAT) and subsequent sleep. Hippocampal-RSC coordination during SWRs was identified in nonrapid eye movement (NREM) sleep, reflecting neural reactivation of decision-making in the task, as shown by a peak reactivation strength within SWRs. Using modified generalized linear models (GLMs), we traced information flow through the RSC-CA1-RSC circuit around SWRs during sleep following DSAT. Our findings show that after spatial training, RSC excitatory neurons typically increase CA1 activity prior to hippocampal SWRs, potentially initiating hippocampal memory replay, while inhibitory neurons are activated by hippocampal outputs in post-SWRs. We further identified certain excitatory neurons in the RSC that encoded spatial information related to the DSAT. These neurons, classified as splitters and location-related cells, showed varied responses to hippocampal SWRs. Overall, our study highlights the complex dynamics between the RSC and hippocampal CA1 region during SWRs in NREM sleep, underscoring their critical interplay in spatial memory consolidation.

Across species, navigation is crucial for finding both resources and shelter. In vertebrates, the hippocampus supports memory-guided goal-directed navigation, whereas in arthropods the central complex supports similar functions. A growing literature is revealing similarities and differences in the organization and function of these brain regions. We review current knowledge about how each structure supports goal-directed navigation by building internal representations of the position or orientation of an animal in space, and of the location or direction of potential goals. We describe input pathways to each structure - medial and lateral entorhinal cortex in vertebrates, and columnar and tangential neurons in insects - that primarily encode spatial and non-spatial information, respectively. Finally, we highlight similarities and differences in spatial encoding across clades and suggest experimental approaches to compare coding principles and behavioral capabilities across species. Such a comparative approach can provide new insights into the neural basis of spatial navigation and neural computation.