A challenging problem in cognitive neuroscience is to relate the structural connectivity (SC) to the functional connectivity (FC) to better understand how large-scale network dynamics underlying human cognition emerges from the relatively fixed SC architecture. Recent modeling attempts point to the possibility of a single diffusion kernel giving a good estimate of the FC. We highlight the shortcomings of the single-diffusion-kernel model (SDK) and propose a multi-scale diffusion scheme. Our multi-scale model is formulated as a reaction-diffusion system giving rise to spatio-temporal patterns on a fixed topology. We hypothesize the presence of inter-regional co-activations (latent parameters) that combine diffusion kernels at multiple scales to characterize how FC could arise from SC. We formulated a multiple kernel learning (MKL) scheme to estimate the latent parameters from training data. Our model is analytically tractable and complex enough to capture the details of the underlying biological phenomena. The parameters learned by the MKL model lead to highly accurate predictions of subject-specific FCs from test datasets at a rate of 71%, surpassing the performance of the existing linear and non-linear models. We provide an example of how these latent parameters could be used to characterize age-specific reorganization in the brain structure and function.
This paper introduces and motivates the use of artificial neural networks (ANN) for recognition of speaker independent phoneme in voice signal. It shows the utilization of neural network's parallel characteristics as well as self-learning characteristics in phoneme recognition using the Kohonen learning rule. It demonstrates the utility of machine learning algorithms in signal processing by trying to emulate biological neuron arrangements. Therefore, different types of neural networks are used at every stage of the whole process. Artificial neural network's implementation has improved the performance of feature extraction, and matching techniques of phoneme recognition. This solution based on self organizing clustering of speech features on time axis forming phonemes and unsupervised learning of these clusters together attains an accuracy of 97.77 % giving 3 seconds clean speech input and an accuracy of 98.88% giving 15 seconds of clean speech input. Speech samples were taken from 9 speakers.
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