Abstract—This Paper describes a model and an implementation of spiking neurons for embedded microcontrollers with few bytes of memory and very low power consumption. The proposed model consists of an elementary neuron network that used Hebbian Learning to train a robot to respond to the environment implementing Artificial Intelligence (AI) in robot. The model is implemented using ATMEGA8 Microcontroller based on AVR RISC Architecture and tested with an ability to move forward and Backward according to intensity of light without human intervention and external computers.
Index Terms—AI, ATMEGA8 microcontroller, hebbian learning, robots.
V. Ganotra is with Department of Electronics in Videocon, Karnal, Haryana, India (e-mail: firstname.lastname@example.org)
R. Sharma is student in Department of Electronics and Communication Engineering, Maharshi Dayan and University, India (email:email@example.com)
P. Dingra is with Research and Development team in Wipro Technologies, India (e-mail:firstname.lastname@example.org)
S. Sharma is with Robotics Department in Digital Surveillance Inc, Noida, India (email:email@example.com)
N. Kumar is student in Department of Electronics and Communication Engineering, Mahamaya University, Uttar Pradesh, India (e-mail:firstname.lastname@example.org)
Cite: Vimal Ganotra, Rashmi Sharma, Priti Dhingra, Sourabh Sharma, and Navin Kumar, "Intelligent Senses in Robot Based on Neural Networks," International Journal of Information and Electronics Engineering vol. 2, no. 4, pp. 488-492, 2012.