The hippocampo-cortical loop: Spatio-temporal learning and goal-oriented planning in navigation

Hirel, Julien; Gaussier, Philippe; Quoy, Mathias; Banquet, Jean-Paul; Save, Étienne; Poucet, Bruno

doi:10.1016/j.neunet.2013.01.023

Cited by 28 publications

(32 citation statements)

References 91 publications

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“…In our task, the mice need to make a decision to halt at the target zone for the correct behavioral response, and to make this decision, they have to recognize whether they are approaching or have reached the remembered target zone. Theories and evidence for goal-directed navigation suggest that the hippocampus provides real-time information on the animal's current location to downstream neurons that represent its proximity to the goal (Burgess and O’Keefe 1996; Hok et al, 2005; Hirel et al, 2013; Erdem et al, 2015), as well as that the hippocampus itself represents information on learned goal locations (Hollup et al, 2001b; Hok et al, 2007; Dupret et al, 2010; McKenzie et al, 2013). Notably, it has been reported that sequences of place cell activity that encode future spatial trajectories to a remembered goal emerge in the hippocampus before goal-directed navigation (Pfeiffer and Foster, 2013).…”

Section: Discussionmentioning

confidence: 99%

Hippocampus-Dependent Goal Localization by Head-Fixed Mice in Virtual Reality

Sato

Kawano

Mizuta

et al. 2017

eNeuro

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The demonstration of the ability of rodents to navigate in virtual reality (VR) has made it an important behavioral paradigm for studying spatially modulated neuronal activity in these animals. However, their behavior in such simulated environments remains poorly understood. Here, we show that encoding and retrieval of goal location memory in mice head-fixed in VR depends on the postsynaptic scaffolding protein Shank2 and the dorsal hippocampus. In our newly developed virtual cued goal location task, a head-fixed mouse moves from one end of a virtual linear track to seek rewards given at a target location along the track. The mouse needs to visually recognize the target location and stay there for a short period of time to receive the reward. Transient pharmacological blockade of fast glutamatergic synaptic transmission in the dorsal hippocampus dramatically and reversibly impaired performance of this task. Encoding and updating of virtual cued goal location memory was impaired in mice deficient in the postsynaptic scaffolding protein Shank2, a mouse model of autism that exhibits impaired spatial learning in a real environment. These results highlight the crucial roles of the dorsal hippocampus and postsynaptic protein complexes in spatial learning and navigation in VR.

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Section: Discussionmentioning

confidence: 99%

Hippocampus-Dependent Goal Localization by Head-Fixed Mice in Virtual Reality

Sato

Kawano

Mizuta

et al. 2017

eNeuro

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“…This architecture can be replicated hierarchically in INFERNO with many maps inter-connected through continuous feedback control with top-down and bottom-up dynamics [21, 29, 81]. …”

Section: Discussionmentioning

confidence: 99%

Iterative free-energy optimization for recurrent neural networks (INFERNO)

Pitti¹,

Gaussier²,

Quoy³

2017

PLoS ONE

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The intra-parietal lobe coupled with the Basal Ganglia forms a working memory that demonstrates strong planning capabilities for generating robust yet flexible neuronal sequences. Neurocomputational models however, often fails to control long range neural synchrony in recurrent spiking networks due to spontaneous activity. As a novel framework based on the free-energy principle, we propose to see the problem of spikes’ synchrony as an optimization problem of the neurons sub-threshold activity for the generation of long neuronal chains. Using a stochastic gradient descent, a reinforcement signal (presumably dopaminergic) evaluates the quality of one input vector to move the recurrent neural network to a desired activity; depending on the error made, this input vector is strengthened to hill-climb the gradient or elicited to search for another solution. This vector can be learned then by one associative memory as a model of the basal-ganglia to control the recurrent neural network. Experiments on habit learning and on sequence retrieving demonstrate the capabilities of the dual system to generate very long and precise spatio-temporal sequences, above two hundred iterations. Its features are applied then to the sequential planning of arm movements. In line with neurobiological theories, we discuss its relevance for modeling the cortico-basal working memory to initiate flexible goal-directed neuronal chains of causation and its relation to novel architectures such as Deep Networks, Neural Turing Machines and the Free-Energy Principle.

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“…Walters and Stringer (2010) presented a biologically plausible model for velocity path integration of head direction, which is composed of a linked continuous attractor network and competitive network. Hirel et al (2013) presented a neural network model where the spatial and temporal components of a task are merged and learned in the hippocampus as chains of associations between sensory events, and so on. In the above mentioned studies, the biological mechanism of path integration and the information transmission process between different kinds of cells were simulated, and the outcomes showed the feasibility and availability of path integration by a bionic approach.…”

Section: Introductionmentioning

confidence: 99%

Biologically inspired model of path integration based on head direction cells and grid cells

Zhou

2016

Frontiers Inf Technol Electronic Eng

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Some neurons in the brain of freely moving rodents show special firing pattern. The firing of head direction cells (HDCs) and grid cells (GCs) is related to the moving direction and distance, respectively. Thus, it is considered that these cells play an important role in the rodents' path integration. To provide a bionic approach for the vehicle to achieve path integration, we present a biologically inspired model of path integration based on the firing characteristics of HDCs and GCs. The detailed implementation process of this model is discussed. Besides, the proposed model is realized by simulation, and the path integration performance is analyzed under different conditions. Simulations validate that the proposed model is effective and stable.

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The hippocampo-cortical loop: Spatio-temporal learning and goal-oriented planning in navigation

Cited by 28 publications

References 91 publications

Hippocampus-Dependent Goal Localization by Head-Fixed Mice in Virtual Reality

Hippocampus-Dependent Goal Localization by Head-Fixed Mice in Virtual Reality

Iterative free-energy optimization for recurrent neural networks (INFERNO)

Biologically inspired model of path integration based on head direction cells and grid cells

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