Place cells on a maze encode routes rather than destinations

Grieves, Roddy M.; Wood, Emma R.; Dudchenko, Paul A.

doi:10.7554/elife.15986

Cited by 94 publications

(103 citation statements)

References 43 publications

(66 reference statements)

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“…For each observation that is shared between the two routes, we observed that the clone distribution for each of two were completely disjoint, showing that the clones were unique to the specific route. Together, these results encapsulate route representation (Eichenbaum et al, 1999;Frank et al, 2000;Grieves et al, 2016), and prospective coding (Grieves et al, 2016) observed in place cells.…”

Section: Experiments 1: Spatial Representationsupporting

confidence: 80%

“…A recent model based on the concept of successor representation (SR) attempted to capture representational properties of place cells and grid cells (Stachenfeld, Botvinick, & Gershman, 2017) but it fails to explain several experimental observations such as the discovery of place cells that encode routes (Frank, Brown, & Wilson, 2000;Grieves, Wood, & Dudchenko, 2016), remapping in place cells (Colgin, Moser, & Moser, 2008), a recent observation of place cells that do not encode goal value (Duvelle et al, 2019), and flexible planning after learning the environment.…”

Section: Introductionmentioning

confidence: 99%

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Different clones for different contexts: Hippocampal cognitive maps as higher-order graphs of a cloned HMM

Gothoskar¹,

Guntupalli²,

Rikhye³

et al. 2019

Preprint

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nishad@vicarious.com), J. Swaroop Guntupalli (swaroop@vicarious.com), Rajeev V. Rikhye (rajeev@vicarious.com), Miguel Lázaro-Gredilla (miguel@vicarious.com), AbstractHippocampus encodes cognitive maps that support episodic memories, navigation, and planning. Understanding the commonality among those maps as well as how those maps are structured, learned from experience, and used for inference and planning is an interesting but unsolved problem. We propose higher-order graphs as the general principle and present, as a plausible model, a cloned hidden Markov model (HMM) that can learn these graphs efficiently from experienced sequences. In our experiments, we use the cloned HMM for learning spatial and abstract representations. We show that inference and planning in the learned CHMM encapsulates many of the key properties of hippocampal cells observed in rodents and humans. Cloned HMM thus provides a new framework for understanding hippocampal function.

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Section: Experiments 1: Spatial Representationsupporting

confidence: 80%

Section: Introductionmentioning

confidence: 99%

Different clones for different contexts: Hippocampal cognitive maps as higher-order graphs of a cloned HMM

Gothoskar¹,

Guntupalli²,

Rikhye³

et al. 2019

Preprint

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“…In single hippocampal neurons, nodal coding manifests in a wide variety of firing patterns that occur in a general manner across different single experiences: for example, firing in place cells that encodes regular features of a subject's spatial experience (e.g., spatial firing fields in similar or analogous locations across different environments) rather than single locations (e.g., Skaggs & McNaughton, 1998). The existence and ubiquity of this type of coding is reported in a collection of place cell studies reporting two contrasting firing patterns (typically observed in different cells—often CA1 neurons—in the same recording): firing that is specific to a spatial location or spatial behavior (referred to as “context”‐specific place firing; e.g., directional place firing, trajectory or “splitter” place firing, journey place firing) as opposed to firing that occurs across these locations and behaviors (e.g., “pure” place cells, “bidirectional” place cells, “path equivalent” firing; findings reviewed in Eichenbaum et al (); Eichenbaum and Cohen (); example studies include McNaughton et al (); Gothard, Skaggs, Moore, and McNaughton (); Skaggs and McNaughton (); Wood, Dudchenko, and Eichenbaum (); Frank, Brown, and Wilson (); Wood, Dudchenko, Robitsek, and Eichenbaum (); Ferbinteanu and Shapiro (); Battaglia, Sutherland, and McNaughton (); Smith and Mizumori (); Royer, Sirota, Patel, and Buzsaki (); Singer, Karlsson, Nathe, Carr, and Frank (); Grieves, Wood, and Dudchenko (); Geiller et al ()). In addition to this work in the rodent model, it is also important to note that nodal coding encompasses the invariant firing correlates of hippocampal neurons reported in humans (Quiroga, ; Quiroga et al, ).…”

Section: Three Brain States In the Hippocampusmentioning

confidence: 99%

Three brain states in the hippocampus and cortex

Kay

Frank

2019

Hippocampus

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Contemporary brain research seeks to understand how cognition is reducible to neural activity. Crucially, much of this effort is guided by a scientific paradigm that views neural activity as essentially driven by external stimuli. In contrast, recent perspectives argue that this paradigm is by itself inadequate, and that understanding patterns of activity intrinsic to the brain is needed to explain cognition. Yet despite this critique, the stimulus-driven paradigm still dominates - possibly because a convincing alternative has not been clear. Here we review a series of experimental findings in the hippocampus suggesting such an alternative. These findings indicate that hippocampal neural activity occurs in one of three brain states that have radically different anatomical, physiological, representational, and behavioral correlates, together implying different roles in cognition. This three-state framework also indicates that hippocampal neural representations follow a surprising pattern of organization at the timescale of ∼1 s or longer. Lastly, beyond the hippocampus, recent breakthroughs indicate three parallel states in cortex, suggesting shared principles and brain-wide organization of intrinsic neural activity. This article is protected by copyright. All rights reserved.

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“…CHMMs learn paths and represent temporal order when the observed statistics demand it, for example when the observations correspond to an animal repeatedly traveling prototypical routes. In rats, "splitter cells" 160 that represent paths rather than locations emerge when they repeatedly traverse the same sequential routes as opposed to random walks [26]. Figure 4a shows a maze in which the agent can traverse two different routes to reach the same destination.…”

Section: Representation Of Paths and Temporal Ordermentioning

confidence: 99%

Learning cognitive maps as structured graphs for vicarious evaluation

Rikhye¹,

Gothoskar²,

Guntupalli³

et al. 2019

Preprint

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Cognitive maps enable us to learn the layout of environments, encode and retrieve episodic memories, and navigate vicariously for mental evaluation of options. A unifying model of cognitive maps will need to 5 explain how the maps can be learned scalably with sensory observations that are non-unique over multiple spatial locations (aliased), retrieved efficiently in the face of uncertainty, and form the fabric of efficient hierarchical planning. We propose learning higher-order graphs -structured in a specific way that allows efficient learning, hierarchy formation, and inference -as the general principle that connects these different desiderata. We show that these graphs can be learned efficiently from experienced sequences using a 10 cloned Hidden Markov Model (CHMM), and uncertainty-aware planning can be achieved using messagepassing inference. Using diverse experimental settings, we show that CHMMs can be used to explain the emergence of context-specific representations, formation of transferable structural knowledge, transitive inference, shortcut finding in novel spaces, remapping of place cells, and hierarchical planning. Structured higher-order graph learning and probabilistic inference might provide a simple unifying framework for un-15 derstanding hippocampal function, and a pathway for relational abstractions in artificial intelligence.properties of place cells and grid cells [8]. Yet another recent model casts spatial and non-spatial problems as a connected graph with neural responses as efficient representations of this graph [9]. Unfortunately, 30 both these models fail to explain several experimental observations such as the discovery of place cells that encode routes [10,11], remapping in place cells [12], a recent discovery of place cells that do not encode goal value [13], and flexible planning after learning the environment.Here, we propose that learning higher-order graphs of sequential events might be an underlying principle of cognitive maps, and propose a specific representational structure that aids in learning, memory integration, 35 retrieval of episodes, and navigation. In particular, we demonstrate that this representational structure can be represented as a probabilistic sequence model -the cloned Hidden Markov Model (CHMM). We show that sequence learning in CHMMs can explain a variety of cognitive maps phenomena such as discovering spatial maps from random walks under aliased and disjoint sensory experiences, transferable structural knowledge, finding shortcuts, and hierarchical planning and physiological findings such as remapping of place cells, 40 and route-specific encoding. Notably, all these properties emerge from a simple model that is easy to train, scale, and perform inference on. Cloned Hidden Markov Model as a model of cognitive mapsCHMMs are based on Dynamic Markov Coding (DMC) [14], an idea for representing higher-order sequences by splitting, or cloning, observed states. For example, a first order Markov chain representing the 45 sequences A-C-E and B-C-D will also assi...

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Place cells on a maze encode routes rather than destinations

Cited by 94 publications

References 43 publications

Different clones for different contexts: Hippocampal cognitive maps as higher-order graphs of a cloned HMM

Different clones for different contexts: Hippocampal cognitive maps as higher-order graphs of a cloned HMM

Three brain states in the hippocampus and cortex

Learning cognitive maps as structured graphs for vicarious evaluation

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