Computation by Time

Walter, Florian; Röhrbein, Florian; Knoll, Alois

doi:10.1007/s11063-015-9478-6

Cited by 18 publications

(10 citation statements)

References 68 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Non-spiking neural networks represent activation as a number to mimic the spike rate of a neuron population whereas spiking neurons represent activation as temporal events, communicating through action potentials called spikes. Importantly, spiking neuron models are able to incorporate time (Walter et al, 2016 ) because biological studies indicate that neurons communicate through frequency of spikes as well as precise spike timing (Bohte, 2004 ). Spiking neuron models have also been shown to use fewer neurons for modeling some computations (Maass, 1997 ).…”

Section: Introductionmentioning

confidence: 99%

Flexible Spiking CPGs for Online Manipulation During Hexapod Walking

2020

View full text Add to dashboard Cite

Neural signals for locomotion are influenced both by the neural network architecture and sensory inputs coordinating and adapting the gait to the environment. Adaptation relies on the ability to change amplitude, frequency, and phase of the signals within the sensorimotor loop in response to external stimuli. However, in order to experiment with closed-loop control, we first need a better understanding of the dynamics of the system and how adaptation works. Based on insights from biology, we developed a spiking neural network capable of continuously changing amplitude, frequency, and phase online. The resulting network is deployed on a hexapod robot in order to observe the walking behavior. The morphology and parameters of the network results in a tripod gait, demonstrating that a design without afferent feedback is sufficient to maintain a stable gait. This is comparable to results from biology showing that deafferented samples exhibit a tripod-like gait and adds to the evidence for a meaningful role of network topology in locomotion. Further, this work enables research into the role of sensory feedback and high-level control signals in the adaptation of gait types. A better understanding of the neural control of locomotion relates back to biology where it can provide evidence for theories that are currently not testable on live insects.

show abstract

Section: Introductionmentioning

confidence: 99%

Flexible Spiking CPGs for Online Manipulation During Hexapod Walking

2020

View full text Add to dashboard Cite

show abstract

“…Experimental evidence accumulated during the last few years has indicated that many biological neural systems use the timing of single-action potentials (or “spikes”) to encode information (Maass, 1997 ), rather than the traditional rate-based models. In Walter et al ( 2015a ), it is explained that how the exact modeling of time in spiking neural networks serves as an important basis for powerful computation based on neurobiological principles.…”

Section: Primary Motivation and Frameworkmentioning

confidence: 99%

A Survey of Robotics Control Based on Learning-Inspired Spiking Neural Networks

et al. 2018

Self Cite

View full text Add to dashboard Cite

Biological intelligence processes information using impulses or spikes, which makes those living creatures able to perceive and act in the real world exceptionally well and outperform state-of-the-art robots in almost every aspect of life. To make up the deficit, emerging hardware technologies and software knowledge in the fields of neuroscience, electronics, and computer science have made it possible to design biologically realistic robots controlled by spiking neural networks (SNNs), inspired by the mechanism of brains. However, a comprehensive review on controlling robots based on SNNs is still missing. In this paper, we survey the developments of the past decade in the field of spiking neural networks for control tasks, with particular focus on the fast emerging robotics-related applications. We first highlight the primary impetuses of SNN-based robotics tasks in terms of speed, energy efficiency, and computation capabilities. We then classify those SNN-based robotic applications according to different learning rules and explicate those learning rules with their corresponding robotic applications. We also briefly present some existing platforms that offer an interaction between SNNs and robotics simulations for exploration and exploitation. Finally, we conclude our survey with a forecast of future challenges and some associated potential research topics in terms of controlling robots based on SNNs.

show abstract

“…The spike trains are used to represent and process the neural information in spiking neurons, which can integrate many aspects of neural information, such as time, space, frequency, and phase, etc. (Whalley, 2013; Walter et al, 2016). As a new brain-inspired computational model of the neural network, SNN has more powerful computing power compared with a traditional neural network model (Maass, 1996).…”

Section: Introductionmentioning

confidence: 99%

A Delay Learning Algorithm Based on Spike Train Kernels for Spiking Neurons

2019

View full text Add to dashboard Cite

Neuroscience research confirms that the synaptic delays are not constant, but can be modulated. This paper proposes a supervised delay learning algorithm for spiking neurons with temporal encoding, in which both the weight and delay of a synaptic connection can be adjusted to enhance the learning performance. The proposed algorithm firstly defines spike train kernels to transform discrete spike trains during the learning phase into continuous analog signals so that common mathematical operations can be performed on them, and then deduces the supervised learning rules of synaptic weights and delays by gradient descent method. The proposed algorithm is successfully applied to various spike train learning tasks, and the effects of parameters of synaptic delays are analyzed in detail. Experimental results show that the network with dynamic delays achieves higher learning accuracy and less learning epochs than the network with static delays. The delay learning algorithm is further validated on a practical example of an image classification problem. The results again show that it can achieve a good classification performance with a proper receptive field. Therefore, the synaptic delay learning is significant for practical applications and theoretical researches of spiking neural networks.

show abstract

Computation by Time

Cited by 18 publications

References 68 publications

Flexible Spiking CPGs for Online Manipulation During Hexapod Walking

Flexible Spiking CPGs for Online Manipulation During Hexapod Walking

A Survey of Robotics Control Based on Learning-Inspired Spiking Neural Networks

A Delay Learning Algorithm Based on Spike Train Kernels for Spiking Neurons

Contact Info

Product

Resources

About