On identification of neural correlates of reflection in simple recurrent neural networks

Abstract

Reflection, that in a general sense means internal representation of the external world, refers to one of awareness levels observed in animals. In this paper we demonstrate the ability of a homogeneous recurrent neural network to solve a problem that requires a reflection. The delayed matching to sample test was chosen as a task which is impossible to pass without an internal representation of an external world. Experiments showed that simple recurrent neural networks can form these representations and store them as neuron firing patterns for several clock cycles. Although the trained network was able to distinguish these patterns easily, the identification of certain stimulus by neuron firing was not practically possible due to minor differences in the level of synchronous firing of a given neuron for different stimuli. Neural networks were shown to be applicable for modeling reflexive abilities, so these simple models may also be used for creation of general technique that ultimately can be applied to recognizing neural correlates of human consciousness.