{'fraction_non_alphanumeric': 0.01591128254580521, 'fraction_numerical': 0.0, 'mean_word_length': 5.365030674846626, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Recurrent networks with recursive processing elements: paradigm for dynamical computing

It was shown earlier that models of cortical neurons can, under certain conditions of coherence in their input, behave as recursive processing elements (PEs) that are characterized by an iterative map on the phase interval and by bifurcation diagrams that demonstrate the complex encoding cortical neurons might be able to perform on their input. Here we present results of numerical experiments carried on a recurrent network of such recursive PEs modeled by the logistic map. Network behavior is studied under a novel scheme for generating complex spatio-temporal input patterns that could range from being coherent to partially coherent to being completely incoherent. A nontraditional nonlinear coupling scheme between neurons is employed to incorporate recent findings in brain science, namely that neurons use more than one kind of neurotransmitter in their chemical signaling. It is shown that such network shave the capacity to 'self-anneal' or collapse into period-m attractors that are uniquely related to the stimulus pattern following a transient 'chaotic' period during which the network searches it state-space for the associated dynamic attractor. The network accepts naturally both dynamical or stationary input patterns. Moreover we find that the use of quantized coupling strengths, introduced to reflect recent molecular biology and neurophysiological reports on synapse dynamics, endows the network with clustering ability wherein, depending ont eh stimulus pattern, PEs in the network with clustering ability wherein, depending on the stimulus pattern, PEs in the network divide into phase- locked groups with the PEs in each group being synchronized in period-m orbits. The value of m is found to be the same for all clusters and the number of clusters gives the dimension of the periodic attractor. The implications of these findings for higher-level processing such as feature- binding and for the development of novel learning algorithms are briefly discussed.