From view cells and place cells to cognitive map learning: processing stages of the hippocampal system

Gaussier, Philippe; Revel, Arnaud; Banquet, Jean-Paul; Babeau, Vincent

doi:10.1007/s004220100269

Cited by 129 publications

(111 citation statements)

References 52 publications

(77 reference statements)

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“…In the model by Gaussier et al [26,27,28], at each time step during exploration, a visual processing module extracted landmark information from a panoramic visual image. For each detected landmark in turn its type (e.g.…”

Section: Previous Modelsmentioning

confidence: 99%

Spatial Representation and Navigation in a Bio-inspired Robot

Sheynikhovich

Chavarriaga

Strösslin

et al. 2005

Biomimetic Neural Learning for Intelligent Robots

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Abstract.A biologically inspired computational model of rodent representation-based (locale) navigation is presented. The model combines visual input in the form of realistic two dimensional grey-scale images and odometer signals to drive the firing of simulated place and head direction cells via Hebbian synapses. The space representation is built incrementally and on-line without any prior information about the environment and consists of a large population of location-sensitive units (place cells) with overlapping receptive fields. Goal navigation is performed using reinforcement learning in continuous state and action spaces, where the state space is represented by population activity of the place cells. The model is able to reproduce a number of behavioral and neurophysiological data on rodents. Performance of the model was tested on both simulated and real mobile Khepera robots in a set of behavioral tasks and is comparable to the performance of animals in similar tasks.

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Section: Previous Modelsmentioning

confidence: 99%

Spatial Representation and Navigation in a Bio-inspired Robot

Sheynikhovich

Chavarriaga

Strösslin

et al. 2005

Biomimetic Neural Learning for Intelligent Robots

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“…Importantly, no underlying functions were used to bias neuronal unit activity toward spatial tuning. Moreover, unlike robotic systems (34)(35)(36)(37)(38), in which abstract features of the hippocampus were used to drive spatially modulated discharge, Darwin X implements many elements of the macro-and microanatomy characteristic of hippocampal-hippocampal and hippocampalcortical connections.…”

Section: Discussionmentioning

confidence: 99%

Characterizing functional hippocampal pathways in a brain-based device as it solves a spatial memory task

Krichmar

Nitz

Gally

et al. 2005

Proc. Natl. Acad. Sci. U.S.A.

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Analyzing neural dynamics underlying complex behavior is a major challenge in systems neurobiology. To meet this challenge through computational neuroscience, we have constructed a brain-based device (Darwin X) that interacts with a real environment, and whose behavior is guided by a simulated nervous system incorporating detailed aspects of the anatomy and physiology of the hippocampus and its surrounding regions. Darwin X integrates cues from its environment to solve a spatial memory task. Placespecific units, similar to place cells in the rodent, emerged by integrating visual and self-movement cues during exploration without prior assumptions in the model about environmental inputs. Because synthetic neural modeling using brain-based devices allows recording from all elements of the simulated nervous system during behavior, we were able to identify different functional hippocampal pathways. We did this by tracing back from reference neuronal units in the CA1 region of the simulated hippocampus to all of the synaptically connected units that were coactive during a particular exploratory behavior. Our analysis identified a number of different functional pathways within the simulated hippocampus that incorporate either the perforant path or the trisynaptic loop. Place fields, which were activated by the trisynaptic circuit, tended to be more selective and informative. However, place units that were activated by the perforant path were prevalent in the model and were crucial for generating appropriate exploratory behavior. Thus, in the model, different functional pathways influence place field activity and, hence, behavior during navigation.A nalyzing the complexities of neural dynamics underlying behavior is a difficult task for systems neurobiologists. A number of factors contribute to this complexity: the variability of behavior, the multilevel nature and nonlinearity of neural interactions, and the large number of neurons in different functioning brain regions. These factors challenge the design of experimental approaches as well as the construction of computational models. For this reason, we have constructed brainbased devices (BBDs) whose behavior in a real world environment is guided by a simulated nervous system based on features of vertebrate neuroanatomy and neurophysiology. The power of this approach is that it allows simultaneous recording of the state and interactions of all components of the simulated nervous system at all levels during a behavioral task in the real world. Here, we describe a functional analysis of neural patterns in Darwin X, a BBD incorporating aspects of the detailed anatomy and physiology of the hippocampus and its surrounding regions (1). Over the last 12 years, we have successfully constructed BBDs to test theories of the nervous system having to do with perceptual categorization, primary and secondary conditioning, visual binding, and texture discrimination (2-6).Darwin X can integrate cues from its environment and provide flexible navigation solutions to spatial memory task...

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“…For example, systems based on GPS measurements, triangulation systems via external references, classical SLAM, vision-based SLAM or topological approaches of SLAM could produce the primary data for the algorithms we present. The what and where merging implies that the shape of the place field is homothetic with the shape of the environment [12], [11] (i.e the place fields extend with the distance to the landmarks). Moreover, neither Cartesian nor topological map building is required for the localization.…”

Section: Perac Architecture For Visual Navigationmentioning

confidence: 99%

Human-Robot Interactions as a Cognitive Catalyst for the Learning of Behavioral Attractors

Giovannangeli

Gaussier

2007

RO-MAN 2007 - The 16th IEEE International Symposium on Robot and Human Interactive Communication

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Abstract-We address in this paper the problem of the autonomous online learning of a sensory-motor task, demonstrated by an operator guiding the robot. For the last decade, we have developed a vision-based architecture for mobile robot navigation. Our bio-inspired model of the navigation has already proved to achieve sensory-motor tasks in real time both in unknown indoor and outdoor environments. We propose to bootstrap the underlying PerAc architecture in order to control the sensori-motor learning. The interaction leads the robot to autonomously build a precise sensorymotor dynamic approximating the behavior of the teacher. A real dialog based on actions imposed by the teacher and those proposed by the robot emerges, which catalyzes the learning of the robot. The architecture is finally tested in real indoor and outdoor environments.

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From view cells and place cells to cognitive map learning: processing stages of the hippocampal system

Cited by 129 publications

References 52 publications

Spatial Representation and Navigation in a Bio-inspired Robot

Spatial Representation and Navigation in a Bio-inspired Robot

Characterizing functional hippocampal pathways in a brain-based device as it solves a spatial memory task

Human-Robot Interactions as a Cognitive Catalyst for the Learning of Behavioral Attractors

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