CloudBrain: Online neural computation in the cloud

Larsen, Leon Bonde; Stagsted, Rasmus Karnøe; Strohmer, Beck; Christensen, Anders Lyhne

doi:10.1101/2021.01.21.427662

Cited by 2 publications

(2 citation statements)

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“…The event-based demonstration on the CloudBrain platform indicates that implementation on neuromorphic hardware is possible. A closed-loop mixed network should be further studied in CloudBrain since it can communicate with a robot in realtime (Larsen et al, 2021). This provides the opportunity for investigation of a mixed network adaptive controller using environmental interaction.…”

Section: Resultsmentioning

confidence: 99%

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Integrating Non-spiking Interneurons in Spiking Neural Networks

et al. 2021

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Researchers working with neural networks have historically focused on either non-spiking neurons tractable for running on computers or more biologically plausible spiking neurons typically requiring special hardware. However, in nature homogeneous networks of neurons do not exist. Instead, spiking and non-spiking neurons cooperate, each bringing a different set of advantages. A well-researched biological example of such a mixed network is a sensorimotor pathway, responsible for mapping sensory inputs to behavioral changes. This type of pathway is also well-researched in robotics where it is applied to achieve closed-loop operation of legged robots by adapting amplitude, frequency, and phase of the motor output. In this paper we investigate how spiking and non-spiking neurons can be combined to create a sensorimotor neuron pathway capable of shaping network output based on analog input. We propose sub-threshold operation of an existing spiking neuron model to create a non-spiking neuron able to interpret analog information and communicate with spiking neurons. The validity of this methodology is confirmed through a simulation of a closed-loop amplitude regulating network inspired by the internal feedback loops found in insects for posturing. Additionally, we show that non-spiking neurons can effectively manipulate post-synaptic spiking neurons in an event-based architecture. The ability to work with mixed networks provides an opportunity for researchers to investigate new network architectures for adaptive controllers, potentially improving locomotion strategies of legged robots.

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Section: Resultsmentioning

confidence: 99%

“…In order to confirm the NSI's compatibility with a neuromorphic architecture, the network is also simulated on CloudBrain (Larsen et al, 2021). CloudBrain is a scalable event-based SNN simulation platform utilizing event stream processing technologies to communicate between neurons implemented as microservices on a cluster.…”

Section: Event-driven Experimentationmentioning

confidence: 99%

Integrating Non-spiking Interneurons in Spiking Neural Networks

et al. 2021

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Integrating Non-Spiking Interneurons in Spiking Neural Networks

Strohmer

Manoonpong

Larsen

2020

Preprint

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Researchers working with neural networks have historically focused on either non-spiking neurons tractable for running on computers or more biologically plausible spiking neurons requiring special hardware. However, in nature homogeneous networks of neurons do not exist. Instead, spiking and non-spiking neurons cooperate, each bringing a different set of advantages. A well researched biological example of such a mixed network is the sensorimotor pathway, responsible for mapping sensory inputs to behavioral changes. This pathway is also well researched in robotics where it is applied to achieve closed-loop operation of legged robots by adapting amplitude, frequency, and phase of the motor output. In this paper we investigate how spiking and non-spiking neurons can be combined to create a sensorimotor neuron pathway capable of shaping network output based on analog input. We propose sub-threshold operation of an existing spiking neuron model to create a non-spiking neuron able to interpret analog information and communicate with spiking neurons. The validity of this methodology is confirmed through a simulation of a closed-loop amplitude regulating network. Although it is currently not possible to run non-spiking neurons on most neuromorphic hardware platforms, we expect that mixed networks can be achieved through combining specialized hardware with conventional tools. The ability to work with mixed networks provides an opportunity for researchers to investigate new network architectures for adaptive controllers, potentially improving locomotion strategies of legged robots.

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CloudBrain: Online neural computation in the cloud

Cited by 2 publications

References 39 publications

Integrating Non-spiking Interneurons in Spiking Neural Networks

Integrating Non-spiking Interneurons in Spiking Neural Networks

Integrating Non-Spiking Interneurons in Spiking Neural Networks

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