Stochastic and asynchronous spiking dynamic neural fields

Vangel, Benoît Chappet de; Torres-Huitzil, César; Girau, Bernard

doi:10.1109/ijcnn.2015.7280776

Cited by 2 publications

(1 citation statement)

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“…This implies that the first state component of neuron i reflects the i -th neural field unit, and the rest of the neuron's state components remain idle. This methodology has been previously used to implement spiking neural fields (Vazquez et al, 2011 ; de Vangel et al, 2015 ).…”

Section: Resultsmentioning

confidence: 99%

Neural and Synaptic Array Transceiver: A Brain-Inspired Computing Framework for Embedded Learning

et al. 2018

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Embedded, continual learning for autonomous and adaptive behavior is a key application of neuromorphic hardware. However, neuromorphic implementations of embedded learning at large scales that are both flexible and efficient have been hindered by a lack of a suitable algorithmic framework. As a result, most neuromorphic hardware are trained off-line on large clusters of dedicated processors or GPUs and transferred post hoc to the device. We address this by introducing the neural and synaptic array transceiver (NSAT), a neuromorphic computational framework facilitating flexible and efficient embedded learning by matching algorithmic requirements and neural and synaptic dynamics. NSAT supports event-driven supervised, unsupervised and reinforcement learning algorithms including deep learning. We demonstrate the NSAT in a wide range of tasks, including the simulation of Mihalas-Niebur neuron, dynamic neural fields, event-driven random back-propagation for event-based deep learning, event-based contrastive divergence for unsupervised learning, and voltage-based learning rules for sequence learning. We anticipate that this contribution will establish the foundation for a new generation of devices enabling adaptive mobile systems, wearable devices, and robots with data-driven autonomy.

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Section: Resultsmentioning

confidence: 99%

Neural and Synaptic Array Transceiver: A Brain-Inspired Computing Framework for Embedded Learning

et al. 2018

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In the quest of efficient hardware implementations of dynamic neural fields: An experimental study on the influence of the kernel shape

Vangel¹,

Fix²

2016

2016 International Joint Conference on Neural Networks (IJCNN)

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Dynamic neural field (DNF) is a popular mesoscopic model for cortical column interactions. It is widely studied analytically and successfully applied to physiological modelling, bioinspired computation and robotics. DNF behavior emerges from distributed and decentralized interactions between computing units which makes it an interesting candidate as a cellular building-block for unconventional computations. That is why we are studying the hardware implementation of DNF on digital substratum (eg. FPGA). As shown in previous papers, this implementation requires several modifications to the equations in order to obtain decent hardware surface utilisation and clock speed. Here we show that the modification of the lateral weights kernel function is possible as long as certain conditions, enumerated in Amari's seminal work are respected. Thank to metaheuristic optimisation it is possible to find the right parameters for two behavioral scenarii of bio-inspired computation interest. We show that the two most hardware-friendly kernels (difference of linear functions and piece-wise function) are as easy to tune as the traditional Mexican hat kernel. However the difference of exponential kernel is more difficult to tune.

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Stochastic and asynchronous spiking dynamic neural fields

Cited by 2 publications

References 33 publications

Neural and Synaptic Array Transceiver: A Brain-Inspired Computing Framework for Embedded Learning

Neural and Synaptic Array Transceiver: A Brain-Inspired Computing Framework for Embedded Learning

In the quest of efficient hardware implementations of dynamic neural fields: An experimental study on the influence of the kernel shape

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