A comparison of encoding schemes for haptic object recognition using a biologically plausible spiking neural network

In this paper a biologically inspired spiking neural network based haptic object recognition system is proposed. A number of different encoding schemes to convert the haptic measurements into spike train are proposed and investigated for haptic object recognition and compared with existing encoding schemes. The spiking neural network was trained using a supervised training approach that is based on the steepest descent algorithm. During the training, firing threshold of the hidden layer neurons were modified in such a way that the ability of the system in recognising different objects is maximised. A multiplexing scheme is used to convert the parallel spike train of the hidden layer into a serial stream. To convert the output spike train into a reliable feature that represents the shape of an object, moment of the spikes with respect to a reference time is calculated. A robot hand with three fingers that have the same number of degrees of freedom as the human fingers was used for testing the system. The hand was made to grasp different objects and the joint angles were recorded. These recorded angles were converted into spike train using different encoding schemes and applied as input to the network. Test results show that the performance of the system varies depending on the input encoding scheme and with the best encoring scheme the system can recognise 100% of 7 different objects.