Neural networks : the official journal of the International Neural Network Society
August 1, 2013
James A Reggia
186 citations
Computational models of consciousness, known as artificial consciousness, have been developed over the last two decades with two main goals: to better understand human and animal consciousness and to create machines with conscious awareness. This review categorizes models into five types based on their central focus: global workspace, information integration, internal self-model, higher-level representations, or attention mechanisms. The review concludes that computational modeling is now an accepted scientific method for studying consciousness, and existing models have successfully simulated many neurobiological and cognitive correlates of conscious processing. However, no current approach has convincingly demonstrated phenomenal machine consciousness or provided clear evidence that it will eventually be possible.
Neural networks : the official journal of the International Neural Network Society
March 1, 2017
Stephen Grossberg
Conscious experiences of seeing, hearing, feeling, and knowing arise from resonant states in the brain, according to Adaptive Resonance Theory (ART). ART explains how brains autonomously learn to attend, recognize, and predict objects and events. It specifies mechanistic links between consciousness, learning, expectation, attention, resonance, and synchrony. Not all resonances become conscious, and not all brain dynamics are resonant. The theory classifies brain resonances that support conscious experiences, clarifying psychological and neurobiological data in normal individuals and clinical patients. Complementary and laminar cortical processing figure prominently in explanations of conscious and unconscious processes.
Neural networks : the official journal of the International Neural Network Society
August 1, 2012
Nagendra Marupaka, Laxmi R Iyer, Ali A Minai
Thought arises from the combination of existing concepts through associations, and the structure of semantic networks—small-world, scale-free connectivity—influences the creativity of ideation. A new neural model represents semantic memory as a recurrent network with itinerant dynamics, where conceptual combinations emerge as co-active neural groups and persist as metastable attractors, recognized as ideas. Simulations show that networks with both small-world and scale-free characteristics significantly enhance the generation of unique conceptual combinations, linking the qualitative structure of associations to the effectiveness of spontaneous thought.