WhiskEye: A Biomimetic Model of Multisensory Spatial Memory Based on Sensory Reconstruction

Knowles, Thomas C.; Stentiford, Rachael; Pearson, Martin J.

doi:10.1007/978-3-030-89177-0_43

Cited by 4 publications

(3 citation statements)

References 16 publications

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“…In this study only the visual frames from the left camera were considered. A simulated model of WhiskEye was integrated into the Human Brain Project's NeuroRobotics Platform (NRP) as part of prior work (Knowles et al, 2021 ; Pearson et al, 2021 ). The NRP integrates robot control and simulation tools, such as ROS and Gazebo, with neural simulators, such as NEST (Falotico et al, 2017 ).…”

Section: Methodsmentioning

confidence: 99%

A Spiking Neural Network Model of Rodent Head Direction Calibrated With Landmark Free Learning

2022

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Maintaining a stable estimate of head direction requires both self-motion (idiothetic) information and environmental (allothetic) anchoring. In unfamiliar or dark environments idiothetic drive can maintain a rough estimate of heading but is subject to inaccuracy, visual information is required to stabilize the head direction estimate. When learning to associate visual scenes with head angle, animals do not have access to the ‘ground truth' of their head direction, and must use egocentrically derived imprecise head direction estimates. We use both discriminative and generative methods of visual processing to learn these associations without extracting explicit landmarks from a natural visual scene, finding all are sufficiently capable at providing a corrective signal. Further, we present a spiking continuous attractor model of head direction (SNN), which when driven by idiothetic input is subject to drift. We show that head direction predictions made by the chosen model-free visual learning algorithms can correct for drift, even when trained on a small training set of estimated head angles self-generated by the SNN. We validate this model against experimental work by reproducing cue rotation experiments which demonstrate visual control of the head direction signal.

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Section: Methodsmentioning

confidence: 99%

A Spiking Neural Network Model of Rodent Head Direction Calibrated With Landmark Free Learning

2022

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“…It is not entirely clear how to connect the body and the simulated or emulated brain, though there is work in spiking robots (e.g. (Knowles et al, 2021)), and virtual agents (e.g. (Huyck and Mitchell, 2018)).…”

Section: Simulating and Emulating Biological Networkmentioning

confidence: 99%

A Spiking Model of Cell Assemblies: Short Term and Associative Memory

Huyck¹

2023

Preprint

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Cell Assemblies (CAs) are the neural basis of both long and short termmemories. CAs, whose neurons persistently fire, are active short termmemories while the neurons are firing, and the memory ceases to beactive when the neurons stop firing. This paper provides simulationsof excitatory spiking neurons with small world topologies that persistfor several hundred milliseconds. Extending this model to includeshort term depression allows the CA to persist for several seconds, areasonable psychological duration. These CAs can be combined in asimple associative memory so that when three CAs are associated,ignition of two causes the third to ignite, while pairs ofunassociated CAs do not lead to the ignition of other CAs. Thismechanism has a larger capacity than a Hopfield net. A discussion ofthe current psychological theories, other mechanisms for short termmemory, the strengths and weaknesses of the paper's simulatedmodels, and proposed challenges are also provided.

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“…To collect this sensory data, we used the WhiskEye robot which was built to mimic whiskered mammals and equipped with both visual (cameras) and tactile (whiskers) sensors [ 26 ]. As seen in Figure 1 , WhiskEye has been replicated in simulation and put within rich virtual environments, enabling the swift gathering of large visuo-tactile datasets using a model of tactile attention [ 27 ] to explore autonomously.…”

Section: Introductionmentioning

confidence: 99%

Ring Attractors as the Basis of a Biomimetic Navigation System

Knowles,

Summerton,

Whiting

et al. 2023

Biomimetics

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The ability to navigate effectively in a rich and complex world is crucial for the survival of all animals. Specialist neural structures have evolved that are implicated in facilitating this ability, one such structure being the ring attractor network. In this study, we model a trio of Spiking Neural Network (SNN) ring attractors as part of a bio-inspired navigation system to maintain an internal estimate of planar translation of an artificial agent. This estimate is dynamically calibrated using a memory recall system of landmark-free allotheic multisensory experiences. We demonstrate that the SNN-based ring attractor system can accurately model motion through 2D space by integrating ideothetic velocity information and use recalled allothetic experiences as a positive corrective mechanism. This SNN based navigation system has potential for use in mobile robotics applications where power supply is limited and external sensory information is intermittent or unreliable.

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WhiskEye: A Biomimetic Model of Multisensory Spatial Memory Based on Sensory Reconstruction

Cited by 4 publications

References 16 publications

A Spiking Neural Network Model of Rodent Head Direction Calibrated With Landmark Free Learning

A Spiking Neural Network Model of Rodent Head Direction Calibrated With Landmark Free Learning

A Spiking Model of Cell Assemblies: Short Term and Associative Memory

Ring Attractors as the Basis of a Biomimetic Navigation System

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