Connectionist cognitive processing for invariant pattern recognition

From classical connectionist and symbolic models, the new field of neurosymbolic integration has emerged, whose aim is to benefit from the advantages of both domains to model human perceptive and cognitive capabilities. To reach this goal, some strategies are envisaged, among which connectionist cognitive processing claims that these desired capabilities can emerge from pure neuronal structures and processes. This approach refers to the substratum of cognition, the human brain and gives rise to perceptually grounded models whose goal is to reach higher cognitive levels. Its principles are presented here and, as an illustration, an application to invariant pattern recognition is described. From basic connectionist models, a biologically inspired model of neuronal networks cooperation is implemented to allow for internal information translation. This mechanism leads to automatic pattern centring in a classical character recognition application with excellent performances.