Intelligent encoding and economical communication in the visual stream.

The theory of computational complexity is used to underpin a recent model of neocortical sensory processing. We argue that encoding into reconstruction networks is appealing for communicating agents using Hebbian learning and working on hard combinatorial problems, which are easy to verify. Computational definition of the concept of intelligence is provided. Simulations illustrate the idea

Towards a unified model of cortical computation II: From control architecture to a model of consciousness

The recently introduced Static and Dynamic State (SDS) Feedback control scheme together with its modified form, the Data Compression and Reconstruction (DCR) architecture that performs pseudoinverse computation, suggests a unified model of cortical processing including consciousness. The constraints of the model are outlined were and the features of the cortical architecture that are suggested and sometimes dictated by these constraints are listed. Constraints are imposed on cortical layers, e.g., (1) the model prescribes a connectivity substructure that is shown to fit the main properties of the `basic neural circuit' of the cerebral cortex (Shepherd and Koch, 1990, Douglas and Martin 1990). In: The synaptic organization of the brain, Oxford University Press, 1990), and (2) the stability requirements of the pseudoinverse method offer an explanation for the columnar organization of the cortex. Constraints are also imposed on the hierarchy of cortical areas, e.g., the proposed control architecture requires computations of the control variables belonging to both the `desired' and the experienced' moves as well as a `sign-proper' separation of feedback channels that fit known properties of the basal ganglia -- thalamocortical loops (Lorincz, 1997). An outline is given as to how the DCR scheme can be extended towards a model for consciousness that can deal with the `homunculus fallacy' by resolving the fallacy and saving he homunculus as an inherited and learnt partially ordered list of preferences