My research is focused on understanding how information processing and cognition can arise from the collective self-organization of elements interacting across many spatial and temporal scales. Here I will present, first an overview of my data driven research and, second a new principle mechanism for self organized information processing in complex neuronal networks. I will close my talk with an outlook to future research.
Part One: Zero time lag synchronisation has been frequently reported in experimental studies. Remarkably, synchrony of neuronal activity is not limited to short-range interactions within a cortical patch, but often extends over remote cortical sites that are coupled with substantial delays. Therefore, zero time lag synchrony among such distant neuronal ensembles must be established by mechanisms that are able to compensate for the delays involved in the neuronal communication. In this first part I am presenting models that show that a v-shape network motif and certain network topologies (random and scale free networks) cause neural populations to fire in unison despite long coupling delays.
Part Two: Reservoir computing originally introduced in the context of echo state or liquid state machines (LSM) has been proposed as a promising computational model for information processing in complex networks. Reservoir computing is a universal framework for computation, such as prediction, classification and memorization of information contained in time varying input streams. Here, I am going to present an extension of the original concept of the LSM that incorporates self organization based on neuronal plasticity. It considers two types, first spike timing dependent plasticity (STDP) that changes the synaptic strength and has been associated with sequence learning, and second intrinsic plasticity (IP) that changes the excitability of individual neurons to maintain homeostasis. We demonstrate that the combination of both types, first optimizes the information processing, second leads to self-organized criticality of the network dynamics, and third that intrinsic noise introduced by the intrinsic plasticity increases the robustness of information processing in a high noise regime.
Esta web utiliza cookies para la recolección de datos con un propósito estadístico. Si continúas navegando, significa que aceptas la instalación de las cookies.