Associative and predictive hippocampal codes support memory-guided behaviors

Episodic memory involves learning and recalling associations between items and their spatiotemporal context. Those memories can be further used to generate internal models of the world that enable predictions to be made. The mechanisms that support these associative and predictive aspects of memory are not yet understood. In this study, we used an optogenetic manipulation to perturb the sequential structure, but not global network dynamics, of place cells as rats traversed specific spatial trajectories. This perturbation abolished replay of those trajectories and the development of predictive representations, leading to impaired learning of new optimal trajectories during memory-guided navigation. However, place cell assembly reactivation and reward-context associative learning were unaffected. Our results show a mechanistic dissociation between two complementary hippocampal codes: an associative code (through coactivity) and a predictive code (through sequences). Description Editor’s summary Synchronous hippocampal neuronal ensemble activity supports episodic memory. This observation has led to the view that the main function of the hippocampus is to encode associations among different elements of an experience. However, an alternative hypothesis is that the hippocampus generates predictive representations of the world that can guide flexible behaviors. Liu et al. disrupted input from the entorhinal cortex to hippocampal area CA1 (see the Perspective by Steudler and Ólafsdóttir), thus destroying the sequence dynamics of place cells while keeping their coincidental firing intact. Sequence replay was disrupted but assembly reactivations were preserved. Different CA1 codes thus serve corresponding memory operations, with the place code supporting associative memory tasks and the sequence code supporting tasks that require learning about predictive transitions in space. —Peter Stern Coactivation and sequential activation of hippocampal place cells represent distinct coding schemes. INTRODUCTION The brain generates models of the environment that are used to guide flexible behaviors. This process requires learning the states of the world (such as specific locations) as well as the transitional relationships between those states (e.g., successive locations along often-traveled trajectories). The hippocampal cognitive map is believed to be one such internal model, supporting a variety of behaviors, including associative learning, navigational planning, and inference. It remains unknown which facets of hippocampal coding are required for these different behaviors and how they support both associative and predictive memory functions. RATIONALE We hypothesize that two modes of hippocampal activity support learning of world states and state transitions, respectively. On one hand, the synchronous coactivity of groups of hippocampal neurons—cell assemblies—may encode features of individual states, forming an associative code. On the other hand, the ordered activation of these cell assemblies into behaviorally relevant sequences may encode the relational structure between states, forming a predictive code. Previous research has not been able to dissociate these two dynamic codes or provide evidence of their specific functions. We leveraged an optogenetic approach to dissociate these two coding schemes, with the goal of disrupting the predictive code (hippocampal sequences) while preserving the associative code (rate coding and coactivity dynamics) in behaving rats. This dissociation allowed us to examine the different memory functions of these two codes. RESULTS We optogenetically perturbed the fine temporal coordination of hippocampal place cell firing as rats navigated specific spatial trajectories in a novel maze. This manipulation disrupted properties of the predictive code (such as temporally compressed place cell sequences and anticipatory place field shifts), but global network dynamics and single-cell spatial tuning and rate coding properties were preserved. During sleep after the novel experience, we observed that task-related cell assemblies encoding discrete maze locations were reactivated in sharp wave–ripples (SWRs), unaffected by the manipulation. However, their sequential structure did not reproduce the order in which they were active in the task, resulting in impaired sequential replay for the perturbed trajectories. This result shows a dissociation between assembly reactivation and sequence replay, two phenomena previously assumed to reflect the same underlying process. The same manipulation did not disrupt replay of familiar trajectories, suggesting that the precise temporal coordination of place cell firing during learning mediates initial plasticity required for subsequent replay. Computational simulations suggest that distinct Hebbian plasticity mechanisms mediate assembly reactivation and sequence replay. We tested the functional role of the predictive code by deploying our optogenetic manipulation in two different hippocampal-dependent memory tasks. Context-reward associative learning in a conditioned place preference task was unaffected and thus does not require a predictive map or memory replay. On the other hand, flexible memory–guided navigation in a foraging task was perturbed by the manipulation and thus depends on hippocampal predictive coding. CONCLUSION Our results provide a mechanistic and functional dissociation between coactivity and sequence codes in the hippocampus. Hippocampal cells with similar responses to behavioral variables fire together, forming functional assemblies during learning, which are reactivated in SWRs during subsequent sleep. These cell assemblies encode discrete states in the environment, an associative code that is sufficient for some types of episodic memories. As these cell assemblies are activated in a specific order during behavior, they form temporally compressed hippocampal sequences and promote Hebbian plasticity. This process enables the replay of behaviorally relevant sequences during SWRs. Hippocampal sequences thus encode transitional structures of world states, generating a predictive model on top of the associative code of individual assemblies. This new framework contributes to our understanding of how memory associations develop into predictive representations of the world and helps reconcile previously disparate views on hippocampal function. Associative and predictive codes in the hippocampus. Our optogenetic manipulation perturbed hippocampal sequences without affecting cell coactivity, thus selectively disrupting the predictive code. After learning, cell assemblies were reactivated, but their order was not preserved, abolishing sequential replay (right). Perturbing the predictive code had no effect on associative learning (bottom left) but did disrupt the flexible learning of novel optimal trajectories on a maze (bottom right). [Rat illustrations: Yu Kang]