A cultured human neural network operates a robotic actuator.
The development of bio-electronic prostheses, hybrid human-electronics devices and bionic robots has been the aim of many researchers. Although neurophysiologic processes have been widely investigated and bio-electronics has developed rapidly, the dynamics of a biological neuronal network that receive sensory inputs, store and control information is not yet understood. Toward this end, we have taken an interdisciplinary approach to study the learning and response of biological neural networks to complex stimulation patterns. This paper describes the design, execution, and results of several experiments performed in order to investigate the behavior of complex interconnected structures found in biological neural networks. The experimental design consisted of biological human neurons stimulated by parallel signal patterns intended to simulate complex perceptions. The response patterns were analyzed with an innovative artificial neural network (ANN), called ITSOM (Inductive Tracing Self Organizing Map). This system allowed us to decode the complex neural responses from a mixture of different stimulations and learned memory patterns inherent in the cell colonies. In the experiment described in this work, neurons derived from human neural stem cells were connected to a robotic actuator through the ANN analyzer to demonstrate our ability to produce useful control from simulated perceptions stimulating the cells. Preliminary results showed that in vitro human neuron colonies can learn to reply selectively to different stimulation patterns and that response signals can effectively be decoded to operate a minirobot. Lastly the fascinating performance of the hybrid system is evaluated quantitatively and potential future work is discussed.
Pizzi
R M R
RM
Living Networks Lab, Department of Information Technologies, University of Milan, Via Bramante 65, 26013 Crema (CR), Italy. pizzi@dti.unimi.it
Rossetti
D
D
Cino
G
G
Marino
D
D
A L Vescovi
Baer
W
W
D004558:Electric Stimulation; D006801:Humans; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D009474:Neurons / Q000378:metabolism*; D012371:Robotics / Q000379:methods*