Cliques in Neural Ensembles as Perception Carriers

Cortical ensembles of neurons perform a large class of tasks which require real-time processing of complex temporal inputs. These tasks involve identifying complex structures and relationships in massive quantities of low precision, ambiguous noisy data. To get some insights into neural mechanisms responsible for the performance of these tasks in a relatively large ensemble of cortical cells, we use a natural closed-loop computational framework. By incorporating functions of readout, reward and feedback, we apply such a framework of neural ensembles to classification and recognition tasks of real-time temporal data. This approach is inspired by neurobiological findings from ex-vivo multicellular electrical recordings and injection of dopamine to the neural culture. Analysis of this functional system provides a new insight into the structure of the spatio-temporal firing patterns, coined "Cliques", that are in a way the neurophysiological carriers of higher-level task-dependent functions and perceptual elements. Such cliques are generated by neural ensembles that interface with the external environment by means of complementary networks that serve the function of readout. Cliques are thus functions of both, network's state and computational task. To observe the cliques, a readout network must be incorporated into the computational system in a closed-loop manner. The emergence of cliques is mediated by spatio-temporal dynamical liquid-currents, through a selective process of Co-Evolutionary learning of two neural ensembles. Liquid-currents from presented inputs to cliques are the dynamic realizations by means of the neural ensembles. The definition of liquid-current intensity leads to a conclusion that the emergence of cliques occurs in cortical ensembles of cells regardless of the realizations at the cellular level and of the intrinsic dimensions of the problem.