Hippocampal Remapping as Hidden State Inference

Sanders, Honi; Wilson, Matthew A.; Gershman, Samuel J.

doi:10.1101/743260

Cited by 34 publications

(61 citation statements)

References 86 publications

(157 reference statements)

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“…Finally, these remapping events represent complete transitions between geometrically aligned neural activity manifolds. Together, our results empirically demonstrate that MEC can maintain multiple distinct attractor networks in a single environment, an idea which had previously only been considered theoretically (Sanders et al, 2020) . Network-wide similarity matrices for example double-track full sessions (left) and split into cue rich (middle) and cue poor (right) trial blocks (A, n = 68 cells; B, n = 55 cells)(dashed lines indicate breaks between blocks).…”

Section: Discussionsupporting

confidence: 63%

Dynamic and reversible remapping of network representations in an unchanging environment

Low

Williams

Campbell

et al. 2020

Preprint

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In response to environmental changes, the medial entorhinal cortex alters its single-cell firing properties. This flexibility in neural coding is hypothesized to support navigation and memory by dividing sensory experience into unique contextual episodes. However, it is unknown how the entorhinal circuit transitions between different representations, particularly when sensory information is not delineated into discrete contexts. Here, we describe spontaneous and abrupt transitions between multiple spatial maps of an unchanging task and environment. These remapping events were synchronized across hundreds of medial entorhinal neurons and correlated with changes in running speed. While remapping altered spatial coding in individual neurons, we show that features of the environment were statistically preserved at the population-level, enabling simple decoding strategies. These findings provoke a reconsideration of how medial entorhinal cortex dynamically represents space and broadly suggest a remarkable capacity for higher-order cortical circuits to rapidly and substantially reorganize their neural representations.

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Section: Discussionsupporting

confidence: 63%

Dynamic and reversible remapping of network representations in an unchanging environment

Low

Williams

Campbell

et al. 2020

Preprint

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“…Whereas we used identical transition structures in both tasks, we nevertheless found asymmetric transfer between domains. A key question underlying the nature of transfer is the remapping of representations [81,82], which can be framed as a hidden state-space inference problem. Different levels of prior experience with the spatial and conceptual stimuli could give rise to different preferences for reuse of task structure as opposed to learning a novel structure.…”

Section: Related Workmentioning

confidence: 99%

Similarities and differences in spatial and non-spatial cognitive maps

Schulz

Garvert

et al. 2020

PLoS Comput Biol

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Learning and generalization in spatial domains is often thought to rely on a "cognitive map", representing relationships between spatial locations. Recent research suggests that this same neural machinery is also recruited for reasoning about more abstract, conceptual forms of knowledge. Yet, to what extent do spatial and conceptual reasoning share common computational principles, and what are the implications for behavior? Using a within-subject design we studied how participants used spatial or conceptual distances to generalize and search for correlated rewards in successive multi-armed bandit tasks. Participant behavior indicated sensitivity to both spatial and conceptual distance, and was best captured using a Bayesian model of generalization that formalized distance-dependent generalization and uncertainty-guided exploration as a Gaussian Process regression with a radial basis function kernel. The same Gaussian Process model best captured human search decisions and judgments in both domains, and could simulate realistic learning curves, where we found equivalent levels of generalization in spatial and conceptual tasks. At the same time, we also find characteristic differences between domains. Relative to the spatial domain, participants showed reduced levels of uncertainty-directed exploration and increased levels of random exploration in the conceptual domain. Participants also displayed a one-directional transfer effect, where experience in the spatial task boosted performance in the conceptual task, but not vice versa. While confidence judgments indicated that participants were sensitive to the uncertainty of their knowledge in both tasks, they did not or could not leverage their estimates of uncertainty to guide exploration in the conceptual task. These results support the notion that value-guided learning and generalization recruit cognitive-map dependent computational mechanisms in spatial and conceptual domains. Yet both behavioral and model-based analyses suggest domain specific differences in how these representations map onto actions.

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“…One could imagine that learning would be more likely to generalise if different neurons were activated on each training day (as in our experiments), or even each trial, than if just one pattern was trained for the same duration. Such volatility in the learning environment is indeed thought to change learning dynamics (Behrens, Woolrich, Walton, & Rushworth, 2007;Massi, Donahue, & Lee, 2018;Mcguire, Nassar, Gold, & Kable, 2014) and by extension may influence the level of generalisation at the neural level, as has recently been suggested for hippocampal representations (Plitt & Giocomo, 2019;Sanders, Wilson, & Gershman, 2020).…”

Section: The Perceptual Threshold Is Plastic and Can Generalisementioning

confidence: 86%

How many neurons are sufficient for perception of cortical activity?

Dalgleish

Russell

Packer

et al. 2020

eLife

104

123

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Many theories of brain function assume that information is encoded and behaviour is controlled through sparse, distributed patterns of activity. It is therefore crucial to place a lower bound on the amount of neural activity that can drive behaviour and to understand how neuronal networks operate within these constraints. We use an all-optical approach to test this lower limit by driving behaviour with targeted two-photon optogenetic activation of small ensembles of L2/3 pyramidal neurons in mouse barrel cortex while using two-photon calcium imaging to record the impact on the local network. By precisely titrating the number of neurons in activated ensembles we demonstrate that the lower bound for detection of cortical activity is ~14 pyramidal neurons. We show that there is a very steep sigmoidal relationship between the number of activated neurons and behavioural output, saturating at only ~37 neurons, and that this relationship can shift with learning. By simultaneously measuring activity in the local network, we show that the activation of stimulated ensembles is balanced by the suppression of neighbouring neurons. This surprising behavioural sensitivity in the face of potent network suppression supports the sparse coding hypothesis and suggests that perception of cortical activity balances a trade-off between minimizing the impact of noise while efficiently detecting relevant signals.

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Hippocampal Remapping as Hidden State Inference

Cited by 34 publications

References 86 publications

Dynamic and reversible remapping of network representations in an unchanging environment

Dynamic and reversible remapping of network representations in an unchanging environment

Similarities and differences in spatial and non-spatial cognitive maps

How many neurons are sufficient for perception of cortical activity?

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