Attractor Dynamics in the Hippocampal Representation of the Local Environment

Wills, Thomas J.; Lever, Colin; Cacucci, Francesca; Burgess, Neil; O’Keefe, John

doi:10.1126/science.1108905

Cited by 599 publications

(623 citation statements)

References 27 publications

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“…One possibility is that animals have two mechanisms through which the state classification can be changed: a prefrontal mechanism providing the addition of new variables to the classification problem (set shifting: Robbins, 2005; dimension augmentation: Grossberg, 1976) and a hippocampal mechanism providing a change in the systemic representation of the underlying context (remapping: C. A. Barnes, Suster, Shen, & McNaughton, 1997;Bostock, Muller, & Kubie, 1991;Redish, 1999;Sharp, Blair, Etkin, & Tzanetos, 1995;Wills, Lever, Cacucci, Burgess, & O'Keefe, 2005). In any case, we suggest that the flexibility of representations in the prefrontal cortex and hippocampus provides the animal with an ability to change the state classification function, which provides the animal with the ability to associate similar situations with new states with which new values can be associated.…”

Section: Anatomical Instantiationsmentioning

confidence: 99%

Reconciling reinforcement learning models with behavioral extinction and renewal: Implications for addiction, relapse, and problem gambling.

Redish

Jensen

Johnson

et al. 2007

Psychological Review

326

459

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Because learned associations are quickly renewed following extinction, the extinction process must include processes other than unlearning. However, reinforcement learning models, such as the temporal difference reinforcement learning (TDRL) model, treat extinction as an unlearning of associated value and are thus unable to capture renewal. TDRL models are based on the hypothesis that dopamine carries a reward prediction error signal; these models predict reward by driving that reward error to zero. The authors construct a TDRL model that can accommodate extinction and renewal through two simple processes: (a) a TDRL process that learns the value of situation-action pairs and (b) a situation recognition process that categorizes the observed cues into situations. This model has implications for dysfunctional states, including relapse after addiction and problem gambling.

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Section: Anatomical Instantiationsmentioning

confidence: 99%

Reconciling reinforcement learning models with behavioral extinction and renewal: Implications for addiction, relapse, and problem gambling.

Redish

Jensen

Johnson

et al. 2007

Psychological Review

326

459

View full text Add to dashboard Cite

show abstract

“…Experimental evidence for pattern completion is that CA3 pyramidal neurons generate consistent activity patterns after small changes in the environment [49]. Experimental evidence for pattern separation is that CA3 cells show abrupt and coordinated place field remapping when the environment is changed to a larger extent [51, 77,85]. Finally, it is believed that the hippocampus is able to store sequences of places.…”

Section: Differential Synaptic Transmission At Mossy Fiber-pyramidal mentioning

confidence: 99%

Timing and efficacy of transmitter release at mossy fiber synapses in the hippocampal network

Bischofberger

Engel

Frotscher

et al. 2006

Pflugers Arch - Eur J Physiol

115

110

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It is widely accepted that the hippocampus plays a major role in learning and memory. The mossy fiber synapse between granule cells in the dentate gyrus and pyramidal neurons in the CA3 region is a key component of the hippocampal trisynaptic circuit. Recent work, partially based on direct presynaptic patch-clamp recordings from hippocampal mossy fiber boutons, sheds light on the mechanisms of synaptic transmission and plasticity at mossy fiber synapses. A high Na + channel density in mossy fiber boutons leads to a large amplitude of the presynaptic action potential. Together with the fast gating of presynaptic Ca 2+ channels, this generates a large and brief presynaptic Ca 2+ influx, which can trigger transmitter release with high efficiency and temporal precision. The large number of release sites, the large size of the releasable pool of vesicles, and the huge extent of presynaptic plasticity confer unique strength to this synapse, suggesting a large impact onto the CA3 pyramidal cell network under specific behavioral conditions. The characteristic properties of the hippocampal mossy fiber synapse may be important for pattern separation and information storage in the dentate gyrus-CA3 cell network.Keywords Presynaptic recording . Mossy fiber boutons . Mossy fiber synapses . Hippocampus . Autoassociative networks . Episodic memory . Synaptic efficacyThe mossy fiber synapse: a key connection in the hippocampal networkThe hippocampal formation consists of three types of principal neurons: granule cells in the dentate gyrus, CA3 pyramidal cells, and CA1 pyramidal neurons. These cells are interconnected by glutamatergic synapses, forming the classical trisynaptic circuit (Fig. 1a,b). Dentate gyrus granule cells receive excitatory glutamatergic input from layer 2 pyramidal cells of the entorhinal cortex and project to CA3 pyramidal cells. CA3 pyramidal cells project to CA1 cells, which in turn project to the subiculum and back to the entorhinal cortex [4]. In addition to this trisynaptic circuit, there is also a direct input from the entorhinal cortex to both CA3 and CA1 pyramidal cells. Furthermore, CA3 pyramidal neurons are extensively connected to each other via recurrent collateral synapses.The nonmyelinated axons of the granule cells, the socalled mossy fibers, show several structural properties that distinguish them from the other synaptic pathways [39]. They project to the CA3 region, mainly traveling within a narrow band termed "stratum lucidum." Several (∼15) large "giant" boutons (∼3-5-μm diameter) emerge from a single mossy fiber axon, either arranged in an en passant manner or attached to the main axon via a short perpendicular axonal branch [1,8,29,31] (Fig. 1c,d). A large mossy fiber bouton typically contacts only a single CA3 pyramidal neuron [21], and a mossy fiber axon contacts a given CA3 pyramidal neuron only once, as boutons are ∼150 μm apart. As the rat hippocampus contains ∼1 million granule cells

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“…That is, they encode the problem at hand in terms of qualitative features, and recognize it via an association to the past experience that most closely matches such features. To model these processes, we rely on the formal apparatus of associative neural networks (Hopfield 1982, Amit et al 1994, which are thought to capture the basic properties of the neural processes involved in associative memory (see Miyashita 1988, Fuster 1995, Poucet and Save 2005, and Wills et al 2005 for neurophysiological demonstrations of such properties and Amit et al 1994 andMcRae et al 1997 for early attempts to reproduce experimental observations of human memory by associative neural network models). The success of such models in predicting individual behavior in multiple-cues decision making and probabilistic inference has been demonstrated by Glöckner and Betsch (2008) and Glöckner et al (2010).…”

Section: Toward a Modelmentioning

confidence: 99%

A Model of Collective Interpretation

Gavetti

Warglien

2015

Organization Science

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W e propose a cognitively plausible formal model of collective interpretation. The model represents how members of a collective interact to interpret their environment. Current theories of collective interpretation focus on how heedful communication among members of a collective (i.e., how much individuals pay attention to others' interpretations) improves interpretive performance; their general assumption is that heed tends to be uniformly beneficial. By unpacking the micromechanisms that underlie such performance, our model reveals a more complex story. Heedfulness can benefit interpretive performance. It can help collectives properly interpret situations that are especially ambiguous, unknown, or novel. Conversely, heedfulness also generates conformity pressures that induce agents to give too much weight to others' interpretations, even if erroneous, thereby potentially degrading interpretive performance. These two effects join into a nonmonotonic trajectory that represents how heed relates to interpretive performance: due to its beneficial properties, performance increases with heed until it peaks before degrading due to conformity pressures. The form of this nonmonotonic relationship is contingent on the nature of the task: ambiguous situations make collectives vulnerable to too much heed: ambiguity ignites conformism; novel situations make collectives dependent on heed: novelty requires multiple eyes to be seen. In addition to these results, our model offers a flexible platform that future work can use to explore collective interpretation in a variety of organizational and supraorganizational contexts.

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Attractor Dynamics in the Hippocampal Representation of the Local Environment

Cited by 599 publications

References 27 publications

Reconciling reinforcement learning models with behavioral extinction and renewal: Implications for addiction, relapse, and problem gambling.

Reconciling reinforcement learning models with behavioral extinction and renewal: Implications for addiction, relapse, and problem gambling.

Timing and efficacy of transmitter release at mossy fiber synapses in the hippocampal network

A Model of Collective Interpretation

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