Hippocampal replay of experience at real-world speeds

Abstract

Representations of past and possible future experiences play a critical role in memory and decision-making processes. The hippocampus expresses these types of representations during sharp-wave ripple (SWR) events, and previous work identified a minority of SWRs that contain "replay" of spatial trajectories at ~20x real-world speeds. Efforts to understand replay typically make multiple assumptions about which events to examine and what sorts of representations constitute replay. We therefore lack a clear understanding of both the prevalence and the range of representational dynamics associated with replay. Here we develop a state space model that uses a combination of movement dynamics of different speeds to capture the spatial content and time evolution of replay during SWRs. Using this model, we find that the large majority of replay events contain spatially coherent, interpretable content. Furthermore, most events progress at real-world, rather than accelerated, movement speeds, consistent with actual experiences.

Hippocampal replay reflects specific past experiences rather than a plan for subsequent choice

“…For simplicity and computational efficiency, we first linearized the 2D maze environment into nine 1D segments: the central “box” segment and eight arms (Figure 2A). Our clusterless decoding algorithm incorporates two main components: a marked point process encoding model relating spike amplitudes during movement times (>4 cm/s) to linearized position, and a state space movement model that allows for both smooth, spatially continuous changes in estimated position over time as well as spatially discontinuous jumps between distant locations (Denovellis et al, 2019; Denovellis et al, 2020). The model generates a joint posterior probability of position over the two movement states, thus providing both an estimate of position and an estimate of the movement state for each time bin.…”

“…We used a state space model to simultaneously decode the “mental” spatial position of the animal, and whether the position was consistent with a spatially continuous or discontinuous movement model, from unsorted “clusterless” spiking data (Deng et al, 2015; Denovellis et al, 2019; Denovellis et al, 2020). To do this, we define two latent variables: first, x k , a continuous latent variable that corresponds to the position represented by the population of CA1 cells at time t k and second, I k , a discrete latent variable that is an indicator for the two movement states we wish to compare: spatially continuous and spatially discontinuous.…”

“…As described in Denovellis et al 2020, to estimate the posterior probability, we apply a recursive causal filter, starting with initial conditions p(x 0 , I 0 ) and iterating from time 1 to time T : and then apply an acausal smoother, starting from the last estimate of the casual filter and recursively iterating back to time 1: where: …”

“…Additionally, we specify that between time steps (2 ms), transitions between the continuous states should reflect the continuous state transition matrix, while transitions between the two states and between discontinuous states should be uniform across all positions. The discrete transition matrix Pr(I k | I k−1 ) defines how likely the movement state is to switch to the other state versus persist in the same state; we set the probability of persisting in a state to 0.98 and the probability of switching states to 0.02 (Denovellis et al, 2020).…”

“…We categorized SWRs as spatially continuous or discontinuous based on the probability of each movement state during the event. An event was spatially continuous if the probability of continuous state exceeded a 0.8 threshold (Denovellis et al, 2020) at any point during the event and the discontinuous state did not. In the cases when both states crossed the 0.8 threshold during an event, the event was considered spatially continuous if more time bins were described by the continuous state than by the discontinuous state (for example, see Supplementary Figure 3, SWR 839).…”