The rodent hippocampus spontaneously generates bursts of neural activity ("replay") which can depict spatial trajectories to reward locations, suggesting a role in model-based behavioral control. A largely separate literature emphasizes reward revaluation as the litmus test for such control, yet the content of hippocampal replay under revaluation conditions is unknown. We report that the content of awake hippocampal sharp wave-ripple events is biased away from the preferred outcome following reward revaluation, challenging the idea that such sequences reflect recent experience or trajectories toward the preferred goal.

Word Count: 86 Main Text Word Count: approx. 4600 Abstract 1The rodent hippocampus spontaneously generates bursts of neural activity ("replay") which can depict spatial 2 trajectories to reward locations, suggesting a role in model-based behavioral control. A largely separate 3 literature emphasizes reward revaluation as the litmus test for such control, yet the content of hippocampal 4 replay under revaluation conditions is unknown. We report that the content of awake hippocampal sharp 5 wave-ripple events is biased away from the preferred outcome following reward revaluation, challenging the 6 idea that such sequences reflect recent experience or trajectories toward the preferred goal. 7 Main text 8The spatial navigation literature has identified a role for the hippocampus in flexibly navigating toward goal 9 locations. This behavior is thought to rely on knowledge of the environment (a "cognitive map") to flexibly 10 generate and decide between possible trajectories, an example of model-based behavioral control 1 . Se-11 quences of hippocampal place cell activity, colloquially referred to as "replay" when coincident with sharp 12 wave-ripple complexes (SWRs) 2,3 , are a candidate neural underpinning for such planning, as suggested by 13 the bias of such sequences toward goal locations 4 and behavioral impairments resulting from SWR disrup-14 tion 5 . In contrast, the conditioning literature operationalizes model-based control as sensitivity to reward 15 revaluation 6 . Reward revaluation causes rate remapping of hippocampal place cells 7 and hippocampal dam-16 age impairs some behaviors that rely on the discrimination of internal motivational states such as hunger and 17 thirst 8,9,10 . However, it is unknown how reward revaluation affects the content of hippocampal SWRs. A 18 goal-directed bias in hippocampal SWRs predicts that its content favors the currently highly valued outcome, 19 especially following a recent revaluation. Conversely, if hippocampal SWR content is unaffected by reward 20 revaluation, this would call into question a role for SWR activity in model-based control. 21 2To determine how hippocampal SWR content is affected by reward revaluation, we recorded neural ensem-22 bles from the dorsal CA1 area of the hippocampus as rats (n = 4, male) performed a T-maze task, which 23 offered free choice between food (left arm) and water (right arm) outcomes (Figure 1a). The crucial ma-24 nipulation in the experimental design was that prior to daily recording sessions, animals were alternately 25 food-or water-restricted, revaluing the food and water outcomes due to a shift in their motivational state. As 26 a result, animals exhibited a clear overall behavioral preference for the restricted outcome (Figure 1b; .90 27 ± .07 (SEM) food choices on food-restricted days, .19 ± .09 food choices on water-restriction days). To 28 assess the statistical significance of this result, as well as all other major results in the study, we compared 29 the observed difference between food-and water-restriction d...

The decoding of a sensory or motor variable from neural activity benefits from a known ground truth against which decoding performance can be compared. In contrast, the decoding of covert, cognitive neural activity, such as occurs in memory recall or planning, typically cannot be compared to a known ground truth. As a result, it is unclear how decoders of such internally generated activity should be configured in practice. We suggest that if the true code for covert activity is unknown, decoders should be optimized for generalization performance using cross-validation. Using ensemble recording data from hippocampal place cells, we show that this cross-validation approach results in different decoding error, different optimal decoding parameters, and different distributions of error across the decoded variable space. In addition, we show that a minor modification to the commonly used Bayesian decoding procedure, which enables the use of spike density functions, results in substantially lower decoding errors. These results have implications for the interpretation of covert neural activity, and suggest easy-to-implement changes to commonly used procedures across domains, with applications to hippocampal place cells in particular. © 2017 Wiley Periodicals, Inc.

1The decoding of a sensory or motor variable from neural activity benefits from a known ground truth against 2 which decoding performance can be compared. In contrast, the decoding of covert, cognitive neural activity, 3 such as occurs in memory recall or planning, typically cannot be compared to a known ground truth. As a 4 result, it is unclear how decoders of such internally generated activity should be configured in practice. We 5 suggest that if the true code for covert activity is unknown, decoders should be optimized for generalization 6 performance using cross-validation. Using ensemble recording data from hippocampal place cells, we show 7 that this cross-validation approach results in different decoding error, different optimal decoding parameters, 8 and different distributions of error across the decoded variable space. In addition, we show that a minor 9 modification to the commonly used Bayesian decoding procedure, which enables the use of spike density 10 functions, results in substantially lower decoding errors. These results have implications for the interpreta-11 tion of covert neural activity, and suggest easy-to-implement changes to commonly used procedures across 12 domains, with applications to hippocampal place cells in particular. 13 65 (Zhang et al., 1998; Johnson and Redish, 2007; Pfeiffer and Foster, 2013). 66 Materials and Methods 67Overview 68 Our aim is to describe how the output of decoding hippocampal ensemble activity depends on the configu-69 ration of the decoder. In particular, we examine two components: (1) the split between training and testing 70 data, and (2) the parameters associated with the estimation of firing rates and tuning curves (the encoding 71 model). Both are described in the Analysis section. All analyses are performed on multiple single unit data 72 recorded from rats performing a T-maze task, described in the Behavior section. Data acquisition, annotation, 73 and pre-processing steps are described in the Neural data section. 74All preprocessing and analysis code is publicly available on our GitHub repository, https://github. 75 com/vandermeerlab/papers. Data files are available from our lab server on request by e-mail to the 76 corresponding author. 77 Neural data 78 Subjects and overall timeline. Four male Long-Evans rats (Charles River and Harlan Laboratories), weigh-79ing 439-501 g at the start of the experiment, were first introduced to the behavioral apparatus (described 80 below; 3-11 days) before being implanted with an electrode array targeting the CA1 area of the dorsal hip-81 5 pocampus (details below). Following recovery (4-9 days) rats were reintroduced to the maze until they ran 82 proficiently (0-3 days), at which point daily recording sessions began. On alternate days, rats were water-83 or food-restricted. In parallel with the maze task, some rats (R042, R044, R050) were trained on a simple 84 Pavlovian conditioning task in a separate room (data not analyzed). 85Behavioral task. The apparatus was an elevated T-maze, constructed from wood, paint...