| Spiking neural networks are more biologically plausible than rate-based neural networks. By incorporating the aspect of time into the model itself, spiking networks are more like biological neural circuits. However, learning methods for spiking neural networks are not as well developed as for the rate-based networks. In this thesis, it is shown that spiking neural networks can be trained to solve computer vision problems using biologically plausible learning methods. For this, a Hebbian learning method based on spike time dependent plasticity is developed and implemented on different problems. The algorithms proposed have been used to simulate billions of synapses on a laptop and are shown to be efficient and scalable. The largest simulation on a laptop had about 1.5 billion synapses. This method is implemented in a hierarchical network architecture containing only spiking neurons with simple cells combining to form more specialized cells similar to the visual processing in non-human primates. This is in contrast to other approaches, which do not use only spiking neurons and/or use non-biology-based learning methods. The processing in the present approach is feed-forward. Thus, it is very fast. Experimental evidence supports feed-forward processing at least in the initial stages of visual processing in the primate brain.;The network and the learning method developed in this work is tested on various cases such as trained on Gabor like cells, LED numbers, and also the MNIST database, which consists of handwritten digits. Simulations on simple cells revealed bell-shaped tuning curves of cells similar to those observed experimentally in the V1, and MT/V5 area of cats and non-human primate brains. Results on the MNIST dataset showed that digits such as 1, 2, and 4 were easier to recognize than digits such as 3, 6, and 5, which is probably because the former have simpler features than the latter. An accuracy of 89% on the MNIST dataset is obtained using a semi-supervised learning approach. The results are encouraging considering only spiking neurons were used throughout, including learning. This work is important as it demonstrates that an all-spiking neural-network approach with only spike-time based learning can solve engineering problems without the use of other traditional learning methods.;The network is also extended to process color images. Color is often ignored in visual processing codes due to the complexity involved. The opponent channels theory for color processing is used and preliminary results using color images of fruits are reported. The results suggest that the network is able to correctly identify fruits not just based on shape but also based on color.;For simulations, a new software called CSpike for simulating spiking neural networks is developed in this thesis. The software is developed using C++ in an object oriented manner exploiting object-oriented programming principles of inheritance, polymorphism, and encapsulation so that it is easy to understand, maintain and modify in future. The Qt application development framework is used for handling images. |
