Neural Ensembles in CA3 Transiently Encode Paths Forward of the Animal at a Decision Point

By Adam Johnson, A. David Redish. 2007

964 citations recorded by the source corpus.

Neural ensembles were recorded from the CA3 region of rats running on T-based decision tasks. Examination of neural representations of space at fast time scales revealed a transient but repeatable phenomenon as rats made a decision: the location reconstructed from the neural ensemble swept forward, first down one path and then the other. Estimated representations were coherent and preferentially swept ahead of the animal rather than behind the animal, implying it represented future possibilities rather than recently traveled paths. Similar phenomena occurred at other important decisions (such as in recovery from an error). Local field potentials from these sites contained pronounced theta and gamma frequencies, but no sharp wave frequencies. Forward-shifted spatial representations were influenced by task demands and experience. These data suggest that the hippocampus does not represent space as a passive computation, but rather that hippocampal spatial processing is an active process likely regulated by cognitive mechanisms.

Subfields: Cellular and Molecular Neuroscience, Cognitive Neuroscience

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Citations in the corpus

Replay in Deep Learning: Current Approaches and Missing Biological Elements (2021, neutral): “Replay is shown to include activity sampled from past experiences as well as novel activity that corresponds to future possibilities (Johnson & Redish, 2007) and random movements in place cells (Stella, Baracskay, ONeill, & Csicsvari, 2019).”
Hippocampal replay in the awake state: a potential physiological substrate of memory consolidation and retrieval (2011, neutral): “Awake replay is also very similar to “vicarious trial and error” event”
Lifelong Generative Modeling (2017, background): “While strongly motivated by biological rehearsal processes [119, 53, 58, 110], these generative replay strategies fail to efficiently use previous learning and simply re-learn each new joint task from scratch.”
Hippocampal Replay is Not a Simple Function of Experience (2010, background): “Previous studies have identified sequential representations that may reflect such planning (Wood et al., 2000; Ferbinteanu and Shapiro, 2003; Johnson and Redish, 2007; Diba and Buzsáki, 2007; Ainge et al., 2007; Karlsson and Frank, 2009), but all of these previous studies have only identified…”
Hippocampal replay of extended experience (2009, neutral): “We reasoned that during reactivation of previous experience the position estimate would deviate systematically from the actual position (Johnson and Redish, 2007).”
The Brain's Default Network (2008, background): “In a series of recent studies, Johnson and Redish (2007) focused on the behavior of rats at a critical choice point in a maze where they were confronted with a high-cost decision.”
Hippocampal replays under the scrutiny of reinforcement learning models. (2018, background): “If from a computational point of view, we assume the same level of exploration during the MB-RL trajectory sampling simulations accounting for awake HPC forward reactivations during VTE (Johnson and Redish, 2007), then simulations do not generate a prevalence of replayed trajectories along the rewar”
Goal-directed decision making as probabilistic inference: a computational framework and potential neural correlates. (2012, background): “…making (see, e.g., Daw et al., 2005; Smith, Li, Becker, & Kapur, 2004), and recent single-unit recording data in rodents provide apparent evidence for serial consideration of future actions and outcomes at behavioral choice points (Johnson & Redish, 2007; Johnson, van der Meer, & Redish, 2007).”
On the methods for reactivation and replay analysis (2020, methodology): “(HPC + V1) Ji & Wilson [33] Bayesian decoding Johnson & Redish [26] Davidson et al. [27] Dragoi & Tonegawa [28] Pfeiffer & Foster [29] Grosmark & Buzsaki [30] Bayes+XCorr (HD cells) Brandon et al. [41] Peyrache et al. [42] Bayes (HPC+MEC) Ólafsdóttir et al. [34] O’Neil et al. [35] amygdala / PCA Gir”
A dedicated population for reward coding in the hippocampus (2018, neutral): “For example, animals might explore possible routes in advance of movement (Pfeiffer and Foster, 2013; Johnson and Redish, 2007), in which case activating reward-associated cells would indicate a successful route.”
Switching between internal and external modes: A multiscale learning principle (2017, background): “2007; Johnson & Redish, 2007), when sensory neocortex is in a synchronized “internal mode” (McGinley et al., 2015b).”
Coherent theta oscillations and reorganization of spike timing in the hippocampal- prefrontal network upon learning. (2010, positive): “Indeed, the hippocampus (Johnson and Redish, 2007) generates representations of the two possible choice arms at the maze fork.”
Sequence Learning and the Role of the Hippocampus in Rodent Navigation (2012, positive): “Thus, although they potentially allow information to be accessed relating to places immediately ahead of an animal, and may even allow exploration of alternatives at a choice point up to 1 m [47], they do not allow information to be accessed corresponding to longer”