Article

Hippocampal place-cell sequences depict future paths to remembered goals

Solomon H Snyder Department of Neuroscience, Johns Hopkins University School Of Medicine, Baltimore, Maryland 21205, USA.
Nature (Impact Factor: 41.46). 04/2013; 497(7447). DOI: 10.1038/nature12112
Source: PubMed

ABSTRACT

Effective navigation requires planning extended routes to remembered goal locations. Hippocampal place cells have been proposed to have a role in navigational planning, but direct evidence has been lacking. Here we show that before goal-directed navigation in an open arena, the rat hippocampus generates brief sequences encoding spatial trajectories strongly biased to progress from the subject's current location to a known goal location. These sequences predict immediate future behaviour, even in cases in which the specific combination of start and goal locations is novel. These results indicate that hippocampal sequence events characterized previously in linearly constrained environments as 'replay' are also capable of supporting a goal-directed, trajectory-finding mechanism, which identifies important places and relevant behavioural paths, at specific times when memory retrieval is required, and in a manner that could be used to control subsequent navigational behaviour.

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    • "Evidence suggests trajectory events can pre-play through previously explored space [2,3], and even through unexplored space, when the path leads to a visible reward [4]. However, can trajectory events occur without any experience or knowledge of the environment? "
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    ABSTRACT: Decades of research have established two central roles of the hippocampus – memory consolidation and spatial navigation. Recently, a third function of the hippocampus has been proposed: simulating future events. However, claims that the neural patterns underlying simulation occur without prior experience have come under fire in light of newly published data.
    Full-text · Article · Jan 2016 · Trends in Cognitive Sciences
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    • "The present results were limited to theta-related behaviors (i.e., active movement). However, sequences also activate in a temporally compressed manner during sharp wave-ripples (SWRs) (Diba and Buzsá ki, 2007;Lee and Wilson, 2002;Ná dasdy et al., 1999), and sequences replayed during awake SWRs were recently shown to predict animals' future trajectories toward goal locations (Pfeiffer and Foster, 2013). Interestingly, slow gamma increases during awake SWRs, with stronger slow gamma coinciding with higher-fidelity replay (Carr et al., 2012). "
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    ABSTRACT: Spatiotemporal trajectories are coded by "theta sequences," ordered series of hippocampal place cell spikes that reflect the order of behavioral experiences. Theta sequences are thought to be organized by co-occurring gamma rhythms (∼25-100 Hz). However, how sequences of locations are represented during distinct slow (∼25-55 Hz) and fast (∼60-100 Hz) gamma subtypes remains poorly understood. We found that slow gamma-associated theta sequences activated on a compressed timescale and represented relatively long paths extending ahead of the current location. Fast gamma-associated theta sequences more closely followed an animal's actual location in real time. When slow gamma occurred, sequences of locations were represented across successive slow gamma phases. Conversely, fast gamma phase coding of spatial sequences was not observed. These findings suggest that slow gamma promotes activation of temporally compressed representations of upcoming trajectories, whereas fast gamma supports coding of ongoing trajectories in real time. Zheng et al. show that place cells code sequences of locations differently during slow and fast gamma rhythms. Upcoming, relatively long paths are coded in a time-compressed manner during slow gamma, whereas representations closely follow current locations during fast gamma.
    Full-text · Article · Jan 2016 · Neuron
    • "We believe our work complements this approach. Short-wave ripple activity have been suggested to guide navigation (Johnson and Redish, 2007; Pfeiffer and Foster, 2013), but it occurs during sleep or when the rat is still (Foster and Wilson, 2006). Thus, while Erdem and Hasselmo (2014) work focuses on high level planning during key decision points, our model focuses on the decision making that takes place while the rat is in motion. "
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    ABSTRACT: There has been extensive research in recent years on the multi-scale nature of hippocampal place cells and entorhinal grid cells encoding which led to many speculations on their role in spatial cognition. In this paper we focus on the multi-scale nature of place cells and how they contribute to faster learning during goal-oriented navigation when compared to a spatial cognition system composed of single scale place cells. The task consists of a circular arena with a fixed goal location, in which a robot is trained to find the shortest path to the goal after a number of learning trials. Synaptic connections are modified using a reinforcement learning paradigm adapted to the place cells multi-scale architecture. The model is evaluated in both simulation and physical robots. We find that larger scale and combined multi-scale representations favor goal-oriented navigation task learning.
    No preview · Article · Nov 2015 · Neural networks: the official journal of the International Neural Network Society
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