Context-dependent representations of objects and space in the primate hippocampus during virtual navigation

Gulli, Roberto A.; Duong, Lyndon; Corrigan, Benjamin; Doucet, Guillaume; Williams, Sylvain; Fusi, Stefano; Martinez‐Trujillo, Julio

doi:10.1038/s41593-019-0548-3

Cited by 88 publications

(115 citation statements)

References 54 publications

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“…We have also started using this method to differentiate gaze, head, and reach transformations in frontal cortex during coordinated eye‐head‐hand reaches (Arora et al., 2019; Nacher et al, 2019). There is no reason to not take this further afield, such as the analysis of activity in areas involved in spatial navigation and spatial memory, including the hippocampus and entorhinal cortex, against ego‐ and allocentric models during complex tasks such as natural viewing and free‐moving navigation (Gulli et al., 2020; Meister & Buffalo, 2018).…”

Section: Theoretical Implications: a New Conceptual Model For Gaze Comentioning

confidence: 99%

Spatiotemporal transformations for gaze control

2020

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Sensorimotor transformations require spatiotemporal coordination of signals, that is, through both time and space. For example, the gaze control system employs signals that are time‐locked to various sensorimotor events, but the spatial content of these signals is difficult to assess during ordinary gaze shifts. In this review, we describe the various models and methods that have been devised to test this question, and their limitations. We then describe a new method that can (a) simultaneously test between all of these models during natural, head‐unrestrained conditions, and (b) track the evolving spatial continuum from target (T) to future gaze coding (G, including errors) through time. We then summarize some applications of this technique, comparing spatiotemporal coding in the primate frontal eye field (FEF) and superior colliculus (SC). The results confirm that these areas preferentially encode eye‐centered, effector‐independent parameters, and show—for the first time in ordinary gaze shifts—a spatial transformation between visual and motor responses from T to G coding. We introduce a new set of spatial models (T‐G continuum) that revealed task‐dependent timing of this transformation: progressive during a memory delay between vision and action, and almost immediate without such a delay. We synthesize the results from our studies and supplement it with previous knowledge of anatomy and physiology to propose a conceptual model where cumulative transformation noise is realized as inaccuracies in gaze behavior. We conclude that the spatiotemporal transformation for gaze is both local (observed within and across neurons in a given area) and distributed (with common signals shared across remote but interconnected structures).

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Section: Theoretical Implications: a New Conceptual Model For Gaze Comentioning

confidence: 99%

Spatiotemporal transformations for gaze control

2020

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“…3) Place Cells, PC in Fig.1. A hallmark of spatial navigation, these cells are commonly found in the CA1 hippocampal region (reviewed in Danjo, 2019); they fire when an animal is in a specific spatial location, and do not represent just space but also contextual information (Gulli et al, 2020). In our network, they are responsible for generating a signal when an object is observed with a specific head direction (HD); this output is used to control robot movement.…”

Section: Resultsmentioning

confidence: 99%

“…c) complex scenes, such as those including multiple known objects and contextual cues, cannot be properly processed with the current implementation; they require a mechanism binding together objects and context information, known to be in effect in the hippocampus to represent a scene (see e.g. Gulli et al, 2020); this aspect was outside the scope of this work.…”

Section: Discussionmentioning

confidence: 99%

Sequence learning in a single trial: a spiking neurons model based on hippocampal circuitry

Coppolino

Migliore

2020

Preprint

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In contrast with our everyday experience using brain circuits, it can take a prohibitively long time to train a computational system to produce the correct sequence of outputs in the presence of a series of inputs. This suggests that something important is missing in the way in which models are trying to reproduce basic cognitive functions. In this work, we introduce a new neuronal network architecture that is able to learn, in a single trial, an arbitrary long sequence of any known objects. The key point of the model is the explicit use of mechanisms and circuitry observed in the hippocampus, which allow the model to reach a level of efficiency and accuracy that, to the best of our knowledge, is not possible with abstract network implementations. By directly following the natural system's layout and circuitry, this type of implementation has the additional advantage that the results can be more easily compared to experimental data, allowing a deeper and more direct understanding of the mechanisms underlying cognitive functions and dysfunctions, and opening the way to a new generation of learning architectures.

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“…An X maze is a double-ended version of the classic Y-choice maze (similar to the T choice maze) from rodent literature (Biggan et al, 1991;Botwinick et al, 1963;Ingles et al, 1993;Redish, 2016). It has been shown that spatial tasks in the X maze are associated hippocampal activation (Gulli et al, 2020). To examine SWR distribution in this task, we divided the maze into zones ( Figure 1B).…”

Section: System Setupmentioning

confidence: 99%

“…The structure of the associative learning task has been described elsewhere. (Gulli et al, 2020) Brie y, the NHP needed to associate the context on the maze walls with a hierarchy of visible target colours and choose the target associated with the highest reward. For our analysis, a trial was "correct" if the target associated with higher reward was chosen.…”

Section: Associative Learning Taskmentioning

confidence: 99%

Unexpected changes in learned task contingencies trigger sharp wave ripples in the primate hippocampus during virtual navigation

McIntosh¹,

Corrigan²,

Gulli³

et al. 2020

Preprint

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The hippocampi and mesial temporal lobes play a central role in episodic memory and associative learning. It is unclear how unexpected experience influences learning. Hippocampal sharp wave ripples (SWR) are an electrical biomarker of memory consolidation. We tracked when and where SWR occur during 2 tasks. Local field potentials were recorded in the hippocampi, entorhinal cortices and amygdalae of non-human primates (NHP; n=3) performing reversal and associative learning tasks in a 3D virtual environment. Our results show hippocampal SWR occurred when learned task contingencies were unexpectedly altered. Surprise rewards and reward denial were associated with SWR rates 9.8x and 8.0x greater than expected rewards. The highest density of SWR occurred in zones where errors were made. SWR were preceded by event-related potentials in the amygdala but not entorhinal cortex. Our results suggest that SWR generation in primates may prioritize behaviourally relevant experience for commitment to memory to allow flexible learning.

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Context-dependent representations of objects and space in the primate hippocampus during virtual navigation

Cited by 88 publications

References 54 publications

Spatiotemporal transformations for gaze control

Spatiotemporal transformations for gaze control

Sequence learning in a single trial: a spiking neurons model based on hippocampal circuitry

Unexpected changes in learned task contingencies trigger sharp wave ripples in the primate hippocampus during virtual navigation

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