The chained activation of neuronal assemblies is thought to support major cognitive processes, including memory. In the hippocampus, this is observed during population bursts often associated with sharp-wave ripples, in the form of an ordered reactivation of neurons. However, the organization and lifetime of these assemblies remain unknown. We used calcium imaging to map patterns of synchronous neuronal activation in the CA1 region of awake mice during runs on a treadmill. The patterns were composed of the recurring activation of anatomically intermingled, but functionally orthogonal, assemblies. These assemblies reactivated discrete temporal segments of neuronal sequences observed during runs and could be stable across consecutive days. A binding of these assemblies into longer chains revealed temporally ordered replay. These modules may represent the default building blocks for encoding or retrieving experience.

The hippocampus plays a critical role in episodic memory: the sequential representation of visited places and experienced events. This function is mirrored by hippocampal activity that self organizes into sequences of neuronal activation that integrate spatiotemporal information. What are the underlying mechanisms of such integration is still unknown. Single cell activity was recently shown to combine time and distance information; however, it remains unknown whether a degree of tuning between space and time can be defined at the network level. Here, combining daily calcium imaging of CA1 sequence dynamics in running head-fixed mice and network modeling, we show that CA1 network activity tends to represent a specific combination of space and time at any given moment, and that the degree of tuning can shift within a continuum from 1 day to the next. Our computational model shows that this shift in tuning can happen under the control of the external drive power. We propose that extrinsic global inputs shape the nature of spatiotemporal integration in the hippocampus at the population level depending on the task at hand, a hypothesis which may guide future experimental studies. hippocampus | space representation | time representation | neural model | attractor network E pisodic memory holds information about spatial (where), nonspatial (what), and temporal (when) components of life experiences (1). While spatial and nonspatial information is available in the environment (e.g., proximity to a wall, presence of a given object), temporal information is an abstract concept anchored in the dynamics of the brain. In rodents, both distance and duration have been found to be represented in the hippocampal formation, a brain area critically involved in episodic memory. This has been reported in CA1 (1-5), CA3 (6, 7), and the medial entorhinal cortex (8,9). Specifically, it has been shown that when rodents run in place on a wheel or a treadmill (in the absence of movement in the laboratory reference frame), hippocampal neurons fire in a sequence whose dynamics can be driven by elapsed time (2) or traveled distance (4).While information about distance is provided primarily by speed and self-motion cues, the sequential firing of neurons in this paradigm is most likely self-organized locally at the circuit level, as it occurs without any ordered arrangement of external inputs. Such internal sequences representing information relative to the past (e.g., elapsed time, traveled distance) must be generated by an integration in time (in the mathematical sense) of spatiotemporal information. Thus, it might not be coincidental that duration and distance internal representations have been observed in the same networks (4). While mathematical models have been able to reproduce the integration of time and space in different experimental paradigms (10), whether the same network structure can switch from encoding distance to duration remains unknown.A particularly relevant model of network structure in this background are continuous attract...

Sensory-guided behavior requires reliable encoding of information (from stimuli to neural responses) and flexible decoding (from neural responses to behavior). In typical decision tasks, a small subset of cells within a large population encode task-relevant stimulus information and need to be identified by later processing stages for relevant information to be transmitted. A statistically optimal decoder (e.g., maximum likelihood) can utilize task-relevant cells for any given task configuration, but relies on complete knowledge of the relationship between the task and the stimulus-response and noise properties of the encoding population. The brain could learn an optimal decoder for a task through supervised learning (i.e., regression), but this typically requires many training trials, and thus lacks the flexibility of humans or animals, that can rapidly adjust to changes in task parameters or structure. Here, we propose a novel decoding solution based on functionally targeted stochastic modulation. Population recordings during different discrimination tasks have revealed that a substantial portion of trial-to-trial variability in cell responses can be explained by stochastic modulatory signals that are shared, and that seem to preferentially target task-informative neurons (Rabinowitz et al., 2015). The variability introduced by these modulators corrupts the encoded stimulus signal, but we propose that it also serves as a label for the informative neurons, allowing the decoder to solve the identification problem. We show in simulations of a modulated Poisson spiking model that a linear decoder with readout weights proportional to the estimated neuron-specific strength of modulation achieves performance close to an optimal decoder.