Decision making model based on attractor network with binary neurons

Recent neuroscience research provides evidence on how decision making systems can be implemented in cortical networks of human brains. In this paper we investigate the possibility of developing a biophysically-realistic decision making model based on the asynchronous recurrent network with attractors and binary neurons. Success of our approach suggests that decision making is a network phenomenon and biologically plausible performance of the model does not rely on neuron activation dynamics of the leaky integrate-and-fire neurons. Results presented here provide supplementary evidence that the postulates formulated by recent empirical research are indeed essential for the implementation of decision making system into neuronal networks.