Abstract
Spiking neural networks (SNN) have gained popularity in embedded applications such as robotics and computer vision. The main advantages of SNN are the temporal plasticity, ease of use in neural interface circuits and reduced computation complexity. SNN have been successfully used for image classification. They provide a model for the mammalian visual cortex, image segmentation and pattern recognition. Different spiking neuron mathematical models exist, but their computational complexity makes them ill-suited for hardware implementation. In this paper, a novel, simplified and computationally efficient model of spike response model (SRM) neuron with spike-time dependent plasticity (STDP) learning is presented. Frequency spike coding based on receptive fields is used for data representation; images are encoded by the network and processed in a similar manner as the primary layers in visual cortex. The network output can be used as a primary feature extractor for further refined recognition or as a simple object classifier. Results show that the model can successfully learn and classify black and white images with added noise or partially obscured samples with up to ×20 computing speed-up at an equivalent classification ratio when compared to classic SRM neuron membrane models. The proposed solution combines spike encoding, network topology, neuron membrane model and STDP learning.
Highlights
In the last years, the popularity of spiking neural networks (SNN) and spiking models has increased
Existing simplified bio-inspired neural models [10,11] are focused on spike train generation and real neuron modeling
We propose to use a simplified model with linear membrane potential degradation with similar performance and learning capabilities as the classic spike response model (SRM)
Summary
The popularity of spiking neural networks (SNN) and spiking models has increased. SNN are suitable for a wide range of applications such as pattern recognition and clustering, among others.There are examples of intelligent systems, converting data directly from sensors [1,2], controlling manipulators [3] and robots [4], doing recognition or detection tasks [5,6], tactile sensing [7] or processing neuromedical data [8]. Existing simplified bio-inspired neural models [10,11] are focused on spike train generation and real neuron modeling. These models are rarely applied in practical tasks. Some of the neuronal models are applied only for linearly separable classes [12] and focus on small network simulation
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