Decoding of Neural Data Using Cohomological Feature Extraction

Decoding of neural data using cohomological learning

We introduce a novel data-driven approach to discover and decode features in the neural code coming from large population neural recordings with minimal assumptions, using cohomological learning. We apply our approach to neural recordings of mice moving freely in a box, where we find a circular feature. We then observe that the decoded value corresponds well to the head direction of the mouse. ...

Feature Extraction from web data using Artificial Neural Networks (ANN)

The main ability of neural network is to learn from its environment and to improve its performance through learning. For this purpose there are two types of learning supervised or active learning – learning with an external ‘teacher’ or a supervisor who present a training set to the network. But another type of learning also exists : unsupervised learning[1] . Unsupervised learning is self orga...

Feature Extraction Using an Unsupervised Neural Network

A novel unsupervised neural network for dimensionality reduction that seeks directions emphasizing multimodality is presented, and its connection to exploratory projection pursuit methods is discussed. This leads to a new statistical insight into the synaptic modification equations governing learning in Bienenstock, Cooper, and Munro (BCM) neurons (1982). The importance of a dimensionality redu...

Feature extraction using auto-associative neural networks

Modal analysis is now mature and well accepted in the design of mechanical structures. It determines the vibration mode shapes and the corresponding natural frequencies. However, the validity of modal analysis is limited to structures showing a linear behaviour. In non-linear structural dynamics, it is well known that mode shapes are no longer useful for the characterization of the dynamic resp...

Feature Extraction from Visual Data Feature Extraction from Visual Data