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The human brain processes information showing learning and prediction abilities but the underlying neuronal mechanisms still remain unknown. Recently, many studies prove that neuronal networks are able of both generalizations and associations of sensory inputs. In this paper, following a set of neurophysiological evidences, we propose a learning framework with a strong biological plausibility that mimics prominent functions of cortical circuitries. We developed the Inductive Conceptual Network (ICN), that is a hierarchical bio-inspired network, able to learn invariant patterns by Variable-order Markov Models implemented in its nodes. The outputs of the top-most node of ICN hierarchy, representing the highest input generalization, allow for automatic classification of inputs. We found that the ICN clusterized MNIST images with an error of 5.73% and USPS images with an error of 12.56%.

Small-World Networks (SWNs) represent a fundamental model for the comprehension of many complex man-made and biological networks. In the central nervous system, SWN models have been shown to fit well both anatomical and functional maps at the macroscopic level. However the functional microscopic level, where the nodes of a network are composed of single neurons, is still poorly understood. At this level, although recent evidences suggest that functional connectivity maps exhibit small-world organization, it is not known whether and how these maps, distributed in multiple brain regions, change across different conditions. We addressed these questions by simultaneous multi-array extracellular recordings in three brain regions diversely involved in somatosensory information processing: the ventropostero-lateral thalamic nuclei (VPL), the primary somatosensory cortex (S1) and the centro-median thalamic nuclei (CM). From both spike and Local Field Potential (LFP) recordings, we estimated the functional connectivity maps by using the Normalized Compression Similarity (spikes) and the Phase Synchrony (LFPs). Then, by using graph-theoretical statistics, we characterized the functional map topology both during spontaneous activity and sensory stimulation. Our main results show that: (i) spikes and LFPs show SWN organization during spontaneous activity; (ii) After stimulation onset, while substantial functional map reconfigurations occur both in spike and LFPs, small-worldness is nonetheless preserved (iii) The stimulus triggers a significant increase of inter-area LFP connections without modifying the topology of intra-area functional connections; (iv) Through computer simulations of the fundamental concept of cell assemblies, transient groups of activating neurons can be described by small-world networks.