Brilliantly Radical Artificially Intelligent Neural Machine
In this project, we are planning to create a digital brain that would use two different concepts of brain functions -mainly neural information propagation and high level cognition- to learn and recall a specific memory upon stimulus. Today’s neural networks and brain simulations are very accurate, yet they either are not sufficient to show intelligent behavior, or lack neuronal basis. In order to tackle this problem, in this project, we are connecting both of these concepts together with a simplistic approach. Thus the project consists of three parts: First, neuronal evaluation of inputs; Second, creation of memory neurons from the evaluated outcomes of inputs; Lastly, intelligent decision-making from the given inputs using the formula of intelligence as proposed by Alex Wissner-Gross (F=T∇S) on a matrix of memory neurons. In order to test whether the neural machine has learned or not, we will have two modes: First, in the learning mode, inputs will be stored in memory neurons after being sieved through the input neurons and each memory neuron will be assigned a random output; then in the test mode, when we give a specific input to the neural machine, it will show the output of the memory neuron that has the closest match to that one using the formula.
Flow Diagrams
Learning Mode
Demo Mode
Support or Contact
Any suggestions or advice? Please feel free to contact by sending an e-mail: (mert.inan@ug.bilkent.edu.tr).