Exchange of local and global neuronal information processing by recurrent fractal neural networks

Abstract

The regulation of neural networks relies significantly on convergent feedback signalling loops that render a global output locally accessible. Ideally, the recurrent connectivity within these systems is self‐organized by a time‐dependent phase‐locking mechanism. This study analyzes recurrent fractal neural networks (RFNNs), which utilize a self‐similar or fractal branching structure of dendrites and downstream networks for phase‐locking of reciprocal feedback loops: output from outer branch nodes of the network tree enters inner branch nodes of the dendritic tree in single neurons. This structural organization enables RFNNs to amplify re‐entrant input by over‐the‐threshold signal summation from feedback loops with equivalent signal travelling times. The columnar organization of pyramidal neurons in the neocortical layers V and III is discussed as␣the structural substrate for this network architecture. RFNNs self‐organize spike trains and render the entire neural network output accessible to the dendritic tree of each neuron within this network. As the result of a contraction mapping operation, the local dendritic input pattern contains a downscaled version of the network output coding structure. Contraction mapping may even proceed to the molecular level and program neurotransmitter release or receptor activation by a downscaled, global coding pattern. RFNNs perform robust, fractal data compression, thereby providing a strategy for the exchange of global and local information processing in the human brain. This property may reconcile neuronal computation in separate neurons with a unified instance of experience in consciousness, which has been discussed as significant step toward the solution of the brain/mind dichotomy.