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Keywords = spatial semantic pointers

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29 pages, 5602 KB  
Article
Biologically-Based Computation: How Neural Details and Dynamics Are Suited for Implementing a Variety of Algorithms
by Nicole Sandra-Yaffa Dumont, Andreas Stöckel, P. Michael Furlong, Madeleine Bartlett, Chris Eliasmith and Terrence C. Stewart
Brain Sci. 2023, 13(2), 245; https://doi.org/10.3390/brainsci13020245 - 31 Jan 2023
Cited by 5 | Viewed by 4772
Abstract
The Neural Engineering Framework (Eliasmith & Anderson, 2003) is a long-standing method for implementing high-level algorithms constrained by low-level neurobiological details. In recent years, this method has been expanded to incorporate more biological details and applied to new tasks. This paper brings together [...] Read more.
The Neural Engineering Framework (Eliasmith & Anderson, 2003) is a long-standing method for implementing high-level algorithms constrained by low-level neurobiological details. In recent years, this method has been expanded to incorporate more biological details and applied to new tasks. This paper brings together these ongoing research strands, presenting them in a common framework. We expand on the NEF’s core principles of (a) specifying the desired tuning curves of neurons in different parts of the model, (b) defining the computational relationships between the values represented by the neurons in different parts of the model, and (c) finding the synaptic connection weights that will cause those computations and tuning curves. In particular, we show how to extend this to include complex spatiotemporal tuning curves, and then apply this approach to produce functional computational models of grid cells, time cells, path integration, sparse representations, probabilistic representations, and symbolic representations in the brain. Full article
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13 pages, 2825 KB  
Article
A Brain-Inspired Goal-Oriented Robot Navigation System
by Qiuying Chen and Hongwei Mo
Appl. Sci. 2019, 9(22), 4869; https://doi.org/10.3390/app9224869 - 14 Nov 2019
Cited by 11 | Viewed by 4643
Abstract
Autonomous navigation in unknown environments is still a challenge for robotics. Many efforts have been exerted to develop truly autonomous goal-oriented robot navigation models based on the neural mechanism of spatial cognition and mapping in animals’ brains. Inspired by the Semantic Pointer Architecture [...] Read more.
Autonomous navigation in unknown environments is still a challenge for robotics. Many efforts have been exerted to develop truly autonomous goal-oriented robot navigation models based on the neural mechanism of spatial cognition and mapping in animals’ brains. Inspired by the Semantic Pointer Architecture Unified Network (SPAUN) neural model and neural navigation mechanism, we developed a brain-like biologically plausible mathematical model and applied it to robotic spatial navigation tasks. The proposed cognitive navigation framework adopts a one-dimensional ring attractor to model the head-direction cells, uses the sinusoidal interference model to obtain the grid-like activity pattern, and gets optimal movement direction based on the entire set of activities. The application of adaptive resonance theory (ART) could effectively reduce resource consumption and solve the problem of stability and plasticity in the dynamic adjustment network. This brain-like system model broadens the perspective to develop more powerful autonomous robotic navigation systems. The proposed model was tested under different conditions and exhibited superior navigation performance, proving its effectiveness and reliability. Full article
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