Researches On Handwritten Numeral Recognition With Revised Liquid State Machine

Handwritten numeral recognition technology, which is the core of the usual systems including literature search, office automation, mail sorting and bank-note processing, is an automatic digits recognizing method based on the computer. This method stays in general pattern recognition phase without using knowledge and simulating the human brain thinking. So the study of handwritten numeral recognition technology based on the brain like structure is of great practical significance.As a typical brain like model, liquid state machine(LSM) opens a new way for handwriting digits recognition and the reservoir is the key processing unites to improve the accuracy of LSM. Therefore, centering with handwritten digits recognition, we studied the self-organization of two bionic spiking neural networks with multi-cluster and self-organization as their features to modify the LSM: self-organized multi-clustered neural network and multi-clustered self-organized neural network.Self-organized multi-clustered neural network is based on the controllable firing frequency under periodic current injection and the synapses among neighboring neurons in a similar location which receive the same input tend to be strengthened by the symmetric spike-timing-dependent plasticity(STDP)learning rule, leading to the clustered structure; multi-clustered self-organized neural network is built on the basis of multi-clustered network produced by the Kaiser's developmental time window algorithm, and then, each cluster synaptic weights are further refined through the asymmetric STDP learning rule. As the brain network and dynamics is closely related, we show the superiority of the two networks by analyzing their dynamics. And the emergence of multi-clustered structure of self-organized neural network with different neuronal firing patters, i.e., bursting or spiking, has been investigated. Then the revised LSM with multi-clustered self-organized neural network is built by parameters setting by a benchmark task named bionic signal reconstruction.At last, the weight of each neuron is trained individually with series of pulse input that is transformed form images of handwritten digits from MNIST database(Mixed National Institute of Standards and Technology database) by signal reconstruction. The images are normalized in order to reduce computation cost. The method proposed has obvious advantages than the first attempt to solve handwritten digits recognition with SNN by Mass in 2013. The key of realizing handwritten digits recognition is classifying the input pulse appropriately, and from this perspective, the model has considerable potentials and space for improvement.