Toward data representation with spiking neurons

Notable advances in the understanding of neural processing were made when sensory systems were investigated from the viewpoint of adaptation to the statistical structure of their input space. For this purpose, mathematical methods for data representation were used. Here, we point out that emphasis on the input structure has been at the cost of the biological plausibility of the corresponding neuron models which process the natural stimuli. The signal transformation of the data representation methods does not correspond well to the signal transformations happening at the single-cell level in neural systems. Hence, we now propose data representation by means of spiking neuron models. We formulate the data representation problem as an optimization problem and derive the fundamental quantities for an iterative learning scheme. This work was presented in part at the 12th International Symposium on Artificial Life and Robotics, Oita, Japan, January 25–27, 2007