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There has been a lack of progress in developing spiking neuron models for pattern classification, which can achieve similar performance as state-of-the-art. To pursue this goal of creating powerful spike-based classifiers, the role of dendrites in neuronal information processing is considered. The neurobiological evidence for dendritic processing has been established in the last few years by neuroscientists across the globe. However, computational models of spiking neurons in machine learning systems have not utilized this mechanism yet. Our work attempts to bridge this gap and explore the possible computational benefits of passive delay and active ionic dendritic mechanisms. A spike-based model for pattern classification is presented which employs neurons with functionally distinct multicompartment dendritic branches. In this model, synaptic integration involves location-dependent processing of inputs on each dendritic compartment, followed by nonlinear processing of the total synaptic input on a dendrite and finally linear integration of the total dendritic output at the soma. This gives the neuron a capacity to perform a large number of input-output mappings. Firstly, a spiking neuron model is developed based on modifying delays associated with the spikes arriving at an afferent. The application of this model is demonstrated on memorizing spatio-temporal patterns by updating only a few delays corresponding to the most synchronous part of a spike pattern. This model explores the time-based computing approach to design a novel learning algorithm which provides an alternative to the traditional weight-based learning and offers the advantage of simpler hardware implementation without multipliers or digitalanalog converters (DACs). The classification accuracy of the system with a load (number of patterns relative to the number of synapses) of up to 2 was shown to be about 80−100%. In our pursuit of achieving improved performance and a hardware-friendly learning algorithm, a model is further proposed which consists of nonlinear dendrites and is inspired by the mechanism of

Morphological learning for spiking neurons with nonlinear active dendrites