A new synaptic plasticity rule for networks of spiking neurons

• Published in: IEEE transaction on neural networks and learning systems 2006-01, Vol.17 (1), p.94-105
• Main Authors: Swiercz, W., Cios, K.J., Staley, K., Kurgan, L., Accurso, F., Sagel, S.
• Format: Article
• Language: English
• Subjects: Algorithms; Analytical models; Applied sciences; Artificial intelligence; Biological system modeling; Biology computing; Computational modeling; Computer networks; Computer science; control theory; systems; Computer Simulation; Connectionism. Neural networks; Cystic Fibrosis - pathology; Diabetic retinopathy; Diabetic Retinopathy - pathology; Exact sciences and technology; Humans; Image edge detection; Image Processing, Computer-Assisted; Image segmentation; Membrane Potentials - physiology; Modeling; Models, Neurological; Neural networks; Neural Networks (Computer); Neuronal Plasticity - physiology; Neurons; Neurons - physiology; Object detection; Pattern recognition. Digital image processing. Computational geometry; Studies; Synapses - physiology; synaptic plasticity; System testing
• ISSN: 1045-9227; 2162-237X; 1941-0093; 2162-2388
• DOI: 10.1109/TNN.2005.860834

In this paper, we describe a new Synaptic Plasticity Activity Rule (SAPR) developed for use in networks of spiking neurons. Such networks can be used for simulations of physiological experiments as well as for other computations like image analysis. Most synaptic plasticity rules use artificially defined functions to modify synaptic connection strengths. In contrast, our rule makes use of the existing postsynaptic potential values to compute the value of adjustment. The network of spiking neurons we consider consists of excitatory and inhibitory neurons. Each neuron is implemented as an integrate-and-fire model that accurately mimics the behavior of biological neurons. To test performance of our new plasticity rule we designed a model of a biologically-inspired signal processing system, and used it for object detection in eye images of diabetic retinopathy patients, and lung images of cystic fibrosis patients. The results show that the network detects the edges of objects within an image, essentially segmenting it. Our ultimate goal, however, is not the development of an image segmentation tool that would be more efficient than nonbiological algorithms, but developing a physiologically correct neural network model that could be applied to a wide range of neurological experiments. We decided to validate the SAPR by using it in a network of spiking neurons for image segmentation because it is easy to visually assess the results. An important thing is that image segmentation is done in an entirely unsupervised way.