![]() ![]() Here, we combine these two tasks to ask how a network would solve them simultaneously and to probe how this combination of tasks relates to remapping in navigational circuits. In isolation, neither of these tasks is novel to the RNN literature-e.g., Cueva and Wei, 2018 trained RNNs to path integrate in complex environments while Sussillo and Barak, 2013 trained RNNs on a ‘1-bit flip-flop’ memory task akin to our latent state inference task. We trained recurrent neural network models (RNNs) to maintain an estimate of position in a simple environment, while at the same time reporting a changing, transiently-cued latent state variable. changes in behavioral state, task conditions, etc. Specifically, we tested the normative hypothesis that remapping occurs when a population of neurons must maintain its local navigational processing, while at the same time responding to global latent state changes (e.g. To bridge the gap between existing theoretical models and biological observations of remapping in the entorhinal cortex, we sought to establish a minimal set of task constraints that could reproduce the essential dynamics of remapping in a computational model. theories for why remapping occurs Levenstein et al., 2020) do not address how a biological system might implement this strategy. This process could enable animals to form distinct memories or choose appropriate actions for a given set of circumstances. Theoretical models of this phenomenon propose that remapping occurs because these cells are responding to global contextual cues (like arousal or attention) in order to decorrelate related experiences with distinct contextual relevance ( Colgin et al., 2008 Sanders et al., 2020). It is difficult to pinpoint the reason for these spontaneous remapping events-i.e., remapping not driven by changes in navigational features. At the same time, these neurons change their firing rates and shift their spatial firing positions-or ‘remap’-under a variety of circumstances, even when navigational cues remain stable ( Bant et al., 2020 Boccara et al., 2019 Butler et al., 2019 Campbell et al., 2021 Campbell et al., 2018 Hardcastle et al., 2017b Low et al., 2021). This raises a question: how can individual brain regions with specialized functions integrate global state changes without compromising their local processing dynamics?įor example, neurons in the medial entorhinal cortex typically represent one or more features such as spatial position, heading direction, and environmental landmarks and are therefore thought to support navigation ( Diehl et al., 2017 Gil et al., 2018 Hafting et al., 2005 Hardcastle et al., 2017a Høydal et al., 2019 Moser et al., 2014 Sargolini et al., 2006 Solstad et al., 2008). Internal state changes, such as shifts in attention ( Fenton et al., 2010 Kentros et al., 2004 Pettit et al., 2022), thirst ( Allen et al., 2019), arousal ( Stringer et al., 2019), and impulsivity ( Cowley et al., 2020), can profoundly alter neural activity across multiple brain areas. Neural circuit computations throughout the brain, from the primary sensory cortex ( Bennett et al., 2013 Niell and Stryker, 2010 Vinck et al., 2015 Zhou et al., 2014) to higher cognitive areas, ( Boccara et al., 2019 Butler et al., 2019 Hardcastle et al., 2017b Hulse et al., 2017 Pettit et al., 2022) are shaped by combinations of internal and external factors. ![]() We thus provide a simple, general, and experimentally-grounded model of remapping as one neural circuit performing both navigation and context inference. Furthermore, the models identify a solution that generalizes to more complex navigation and inference tasks. We show that these combined task constraints (navigation and context inference) produce activity patterns that are qualitatively similar to population-wide remapping in the entorhinal cortex, a navigational brain region. How can navigational circuits preserve their local computations while responding to global context changes? To investigate this question, we trained recurrent neural network models to track position in simple environments while at the same time reporting transiently-cued context changes. These cells also change their firing patterns (‘remap’) in response to changing contextual factors such as environmental cues, task conditions, and behavioral states, which influence neural activity throughout the brain. Neurons in navigational brain regions provide information about position, orientation, and speed relative to environmental landmarks. ![]()
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