Towards the modeling of dissociated cortical tissue in the liquid state machine framework
Understanding biological information processing systems is key in overcoming the limitations of traditional engineered systems. The advent of the liquid state machine (LSM) as a computational model for cortical processing, as well as the ability to experiment with dissociated cortical tissue (DCT) cultures in a controlled environment provide new opportunities in advancing our understanding of such systems. In this paper we examine the possibilities for modeling the behavior of the DCT cultures in the LSM framework. We show that the LSM framework has the capability to model the spontaneous activity and burstiness of the DCT cultures. Finally, multifractal measures are used to characterize the long range dependencies of the recorded and simulated data. Though the detrended fluctuation analysis (DFA) used to estimate the long range dependencies in the data does not show wholly consistent similarities, the endogenously active neurons that drive the biological and model networks are found to have similar fractal structure.