Neuromorphic self-driving robot with retinomorphic vision and spike-based processing/closed-loop control

Fischl, Kate D.; Tognetti, Gaspar; Mendat, Daniel R.; Orchard, Garrick; Rattray, John; Sapsanis, Christos; Campbell, Laura F.; Elphage, Laxaviera; Niebur, Tobias E.; Pasciaroni, A.; Rennoll, Valerie; Romney, Heather; Walker, Shamaria; Pouliquen, Philippe O.; Andreou, Andreas G.

doi:10.1109/ciss.2017.7926179

Cited by 7 publications

(5 citation statements)

References 9 publications

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“…The bioinspired nature of the proposed system comes from its inspiration in the brain regions of the hippocampus and the PPC, not in the application of ANN-to-SNN conversion methods, as in ref. [17] The proposed system has not only been simulated in software and on ideal environments as in ref. [8,15], but also on more complex environments and, in addition, on a robotic platform in physical environments.…”

Section: Discussionmentioning

confidence: 99%

“…Some authors have attempted to develop navigation systems using spiking but not bioinspired networks, refer other studies. [ 17 ] Others propose fully bioinspired navigation systems (including all the stages in the process), such as other studies, [ 14 ] which do not present any learning phase and, therefore, the environment is hard‐coded in a static memory, [ 8 ] which is only able to map three different possible states.…”

Section: Introductionmentioning

confidence: 99%

“…There are also other works [ 17 ] that offer a different approach using SNN, but they are quite distant from their biological counterpart and are rather focused on ANN techniques. It makes use of a convolutional neural network (CNN) network to generate the relevant motor commands via spiking‐coded images from datasets.…”

Section: Introductionmentioning

confidence: 99%

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Bioinspired Spike‐Based Hippocampus and Posterior Parietal Cortex Models for Robot Navigation and Environment Pseudomapping

Casanueva-Morato,

Ayuso-Martinez,

Dominguez-Morales

et al. 2023

Advanced Intelligent Systems

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The brain has great capacity for computation and efficient resolution of complex problems, far surpassing modern computers. Neuromorphic engineering seeks to mimic the basic principles of the brain to develop systems capable of achieving such capabilities. In the neuromorphic field, navigation systems are of great interest due to their potential applicability to robotics, although these systems are still a challenge to be solved. This work proposes a spike‐based robotic navigation and environment pseudomapping system formed by a bioinspired hippocampal memory model connected to a posterior parietal cortex (PPC) model. The hippocampus is in charge of maintaining a representation of an environment state map, and the PPC is in charge of local decision‐making. This system is implemented on the SpiNNaker hardware platform using spiking neural networks. A set of real‐time experiments are applied to demonstrate the correct functioning of the system in virtual and physical environments on a robotic platform. The system is able to navigate through the environment to reach a goal position starting from an initial position, avoiding obstacles and mapping the environment. To the best of the authors’ knowledge, this is the first implementation of an environment pseudomapping system with dynamic learning based on a bioinspired hippocampal memory.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Bioinspired Spike‐Based Hippocampus and Posterior Parietal Cortex Models for Robot Navigation and Environment Pseudomapping

Casanueva-Morato,

Ayuso-Martinez,

Dominguez-Morales

et al. 2023

Advanced Intelligent Systems

View full text Add to dashboard Cite

show abstract

“…Genetic or evolutionary approaches have been commonly used to produce neuromorphic solutions to a variety of control tasks, including robotic control [2,28], drone control [21], video games [37], and engine control [45]. Imitation learning has also been popularly used for control of neuromorphic systems, especially for self-driving robots [17,22]. These cases typically implement a convolutional neural network to map observations to actions.…”

Section: Background and Related Workmentioning

confidence: 99%

Evolutionary vs imitation learning for neuromorphic control at the edge*

Schuman

Patton

Kulkarni

et al. 2022

Neuromorph. Comput. Eng.

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Neuromorphic computing offers the opportunity to implement extremely low power artificial intelligence at the edge. Control applications, such as autonomous vehicles and robotics, are also of great interest for neuromorphic systems at the edge. It is not clear, however, what the best neuromorphic training approaches are for control applications at the edge. In this work, we implement and compare the performance of evolutionary optimization and imitation learning approaches on an autonomous race car control task using an edge neuromorphic implementation. We show that the evolutionary approaches tend to achieve better performing smaller network sizes that are well-suited to edge deployment, but they also take significantly longer to train. We also describe a workflow to allow for future algorithmic comparisons for neuromorphic hardware on control applications at the edge.

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“…The chip supports deep feedback algorithms so that it can be practically applied in fields such as image recognition and speech processing. The vision application system 161, composed of 3 million neurons implemented by the TrueNorth chip, consumes about 200 mW [107]. The DARPA SyNAPSE system consists of 48 TrueNorth chips interconnected in an array [108], which can realize a neural network with a scale of 50 million neurons.…”

Section: B Pure Digital Neuromorphic Chipmentioning

confidence: 99%

A Survey of Intelligent Chip Design Research Based on Spiking Neural Networks

2022

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The traditional neural network Intelligent chip has the problem of high power consumption due to classical computing architecture, limiting the development of neural network Intelligent chips. Stochastic computing (SC) encodes binary numbers into stochastic pulse sequences in operation, taking advantage of low power consumption and high performance. The application of SC in spiking neural networks (SNNs) Intelligent chips is beneficial to solving the high power consumption of traditional neural network chips. This article first summarizes the basic elements of SNNs and the basic principles of SC. Then, we review the development trends of the stochastic computation-based neural network chips and existing SNN chips under research at home and abroad, respectively, and analyze the current problems. Finally, a review of SNN chips based on SC is highlighted. This paper aims to provide new research directions and to learn ideas for the field of SNN chips through systematic summaries.

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Neuromorphic self-driving robot with retinomorphic vision and spike-based processing/closed-loop control

Cited by 7 publications

References 9 publications

Bioinspired Spike‐Based Hippocampus and Posterior Parietal Cortex Models for Robot Navigation and Environment Pseudomapping

Bioinspired Spike‐Based Hippocampus and Posterior Parietal Cortex Models for Robot Navigation and Environment Pseudomapping

Evolutionary vs imitation learning for neuromorphic control at the edge*

A Survey of Intelligent Chip Design Research Based on Spiking Neural Networks

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