The capacity to remember temporal relationships between different events is essential to episodic memory, but little is currently known about its underlying mechanisms. We performed time-lapse imaging of thousands of neurons over weeks in the hippocampal CA1 of mice as they repeatedly visited two distinct environments. Longitudinal analysis exposed ongoing environment-independent evolution of episodic representations, despite stable place field locations and constant remapping between the two environments. These dynamics time-stamped experienced events via neuronal ensembles that had cellular composition and activity patterns unique to specific points in time. Temporally close episodes shared a common timestamp regardless of the spatial context in which they occurred. Temporally remote episodes had distinct timestamps, even if they occurred within the same spatial context. Our results suggest that days-scale hippocampal ensemble dynamics could support the formation of a mental timeline in which experienced events could be mnemonically associated or dissociated based on their temporal distance.DOI: http://dx.doi.org/10.7554/eLife.12247.001

SummaryCa2+ imaging techniques permit time-lapse recordings of neuronal activity from large populations over weeks. However, without identifying the same neurons across imaging sessions (cell registration), longitudinal analysis of the neural code is restricted to population-level statistics. Accurate cell registration becomes challenging with increased numbers of cells, sessions, and inter-session intervals. Current cell registration practices, whether manual or automatic, do not quantitatively evaluate registration accuracy, possibly leading to data misinterpretation. We developed a probabilistic method that automatically registers cells across multiple sessions and estimates the registration confidence for each registered cell. Using large-scale Ca2+ imaging data recorded over weeks from the hippocampus and cortex of freely behaving mice, we show that our method performs more accurate registration than previously used routines, yielding estimated error rates <5%, and that the registration is scalable for many sessions. Thus, our method allows reliable longitudinal analysis of the same neurons over long time periods.

Measuring neuronal tuning curves has been instrumental for many discoveries in neuroscience but requires a priori assumptions regarding the identity of the encoded variables. We applied unsupervised learning to large-scale neuronal recordings in behaving mice from circuits involved in spatial cognition and uncovered a highly-organized internal structure of ensemble activity patterns. This emergent structure allowed defining for each neuron an ‘internal tuning-curve’ that characterizes its activity relative to the network activity, rather than relative to any predefined external variable, revealing place-tuning and head-direction tuning without relying on measurements of place or head-direction. Similar investigation in prefrontal cortex revealed schematic representations of distances and actions, and exposed a previously unknown variable, the ‘trajectory-phase’. The internal structure was conserved across mice, allowing using one animal’s data to decode another animal’s behavior. Thus, the internal structure of neuronal activity itself enables reconstructing internal representations and discovering new behavioral variables hidden within a neural code.

Highlights d Hippocampal neurons switch between distinct maps of the same familiar environment d Alternations between maps occur only across separate visits to the same environment d Remapping is simultaneous and coherent across place and non-place cells d The distinct maps are spatially informative and stable over weeks

Measuring neuronal tuning curves has been instrumental for many discoveries in 1 neuroscience but requires a-priori assumptions regarding the identity of the 2 encoded variables. We applied unsupervised learning to large-scale neuronal 3 recordings in behaving mice from circuits involved in spatial cognition, and 4 uncovered a highly-organized internal structure of ensemble activity patterns. This 5 emergent structure allowed defining for each neuron an 'internal tuning-curve' that 6 characterizes its activity relative to the network activity, rather than relative to any 7 pre-defined external variable -revealing place-tuning in the hippocampus and 8 head-direction tuning in the thalamus and postsubiculum, without relying on 9 measurements of place or head-direction. Similar investigation in prefrontal cortex 10 revealed schematic representations of distances and actions, and exposed a 11 previously unknown variable, the 'trajectory-phase'. The structure of ensemble 12 activity patterns was conserved across mice, allowing using one animal's data to 13 decode another animal's behavior. Thus, the internal structure of neuronal activity 14 itself enables reconstructing internal representations and discovering new 15 behavioral variables hidden within a neural code. 16 17 Most neurons in the brain do not receive direct inputs from the external world; rather, 18 their activity is governed by their interactions with other neurons within and across brain 19 circuits. Despite this fact, studies in neuroscience typically focus on neuronal 20 responsiveness to an examined external variable (i.e., a neuronal tuning curve). Rooted 21 in the emergence in the 1950's of electrophysiological techniques for recording from 22 single neurons in vivo, this 'neural correlate' approach opened the door to studying how 23 specific brain circuits form internal representations, and has led to seminal 24 breakthroughs. Examples of such breakthroughs include the discoveries of orientation 25 tuning in the visual cortex 1 , hippocampal place cells 2 , and entorhinal grid cells 3,4 . 26However, while such analyses remain invaluable for many neuroscientific studies, they 27 are limited to a-priori defined external variables, overlooking other variables that were not 28 measured, considered relevant, or observable 5-7 . With traditional electrophysiological 29 techniques that permitted recordings from only small numbers of neurons, no feasible 30 alternatives to this approach existed. Recent advances in multi-electrode and optical 31 imaging technologies enable simultaneous readout of activity from large neuronal 32 populations, permitting a qualitatively different approach to study the neural code-via 33 the attributes of neuronal activity itself. 34We hypothesized that the relationships between neuronal population activity patterns 35 would give rise to a structure within the neuronal activity space that differs across brain 36 circuits according to their distinct computational roles. Thus, studying the internal 37 structure of neuronal activ...