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Ponencia
Live Demonstration: Neuromorphic Row-by-Row Multi-convolution FPGA Processor-SpiNNaker architecture for Dynamic-Vision Feature Extraction
(IEEE Computer Society, 2019)
In this demonstration a spiking neural network architecture for vision recognition using an FPGA spiking convolution processor, based on leaky integrate and fire neurons (LIF) and a SpiNNaker board is presented. The ...
Ponencia
Deep Spiking Neural Network model for time-variant signals classification: a real-time speech recognition approach
(IEEE Computer Society, 2018)
Speech recognition has become an important task to improve the human-machine interface. Taking into account the limitations of current automatic speech recognition systems, like non-real time cloud-based solutions or ...
Ponencia
Sound Recognition System Using Spiking and MLP Neural Networks
(Springer, 2016)
In this paper, we explore the capabilities of a sound classification system that combines a Neuromorphic Auditory System for feature extraction and an artificial neural network for classification. Two models of neural ...
Ponencia
A Sensor Fusion Horse Gait Classification by a Spiking Neural Network on SpiNNaker
(Springer, 2016)
The study and monitoring of the behavior of wildlife has always been a subject of great interest. Although many systems can track animal positions using GPS systems, the behavior classification is not a common task. For ...
Artículo
NeuroPod: a real-time neuromorphic spiking CPG applied to robotics
(Elsevier, 2020)
Initially, robots were developed with the aim of making our life easier, carrying out repetitive or dangerous tasks for humans. Although they were able to perform these tasks, the latest generation of robots are being ...
Ponencia
Multilayer Spiking Neural Network for Audio Samples Classification Using SpiNNaker
(Springer, 2016)
Audio classification has always been an interesting subject of research inside the neuromorphic engineering field. Tools like Nengo or Brian, and hardware platforms like the SpiNNaker board are rapidly increasing in ...