Non-Classical Connectionist Models of Visual Object Recognition

Dear Editor,It is with great interest that we read the article by Tayet al. on face recognition with quantum associative net-works, which was recently published in Cognitive Compu-tation [1]. In a series of computer simulations, the authorsdemonstrated how non-Turing-based quantum computingcan be harnessed for viewpoint-invariant face recognitionusing Hebb-like storage of image-encoding Gabor waveletsimplemented in a quantum-holographic procedure. Thepresented model was largely motivated by Hopfield’s neuralnetwork [2], which was then transformed into a quantum-holographic process in which the Hebbian memory wasreplaced by multiple self-interferences of quantum planewaves [3].This transformation was achieved by means of a simplesubstitution (see Table 1 for details) of Hopfield’s real-valued variables by the complex-valued variables changingas sinusoids (waves), while simultaneously preserving allinput-to-output transformations in the model. By doingthis, it becomes possible to combine the area of artificialneural networks with that of quantum computing in a rathernatural way. In particular, the authors showed that thequality of reconstructed images can be enhanced by meansof an iterative sampling method using (1) one optimumsampling frequency for which the best distinction amongimages is warranted and (2) by selecting Gabor coefficientswith least activity for correlation in the associative net-work, which is necessary to determine the order parametersin the model.Although the authors meticulously dealt with issuesrelated to quality improvements of reconstructed images,they made much less effort to address two essential issuesintimately linked to their work. First, while a substantialportion of the paper was dedicated to Gabor wavelets asbiologically plausible descriptors of receptive fields, itremains unclear how the implemented quantum-holo-graphic procedure—the central aspect of the introducedmodel—relates to current neurocognitive theories of visualinformation processing. Second, it is hard to see how thepresented results could differ from those yielded by pre-vious versions of quantum neural network models (see e.g.,[4, 5]) and especially, from the performance of classi-cal, that is, non-quantum neural networks for imagerecognition.Non-classical, i.e., quantum-like approaches to cognitionand its underlying neural substrates have attracted muchrecent attention [6–11]. While some areas such as mathe-matical psychology have benefited from alternative non-classical models of cognition [10, 12, 13], the relevance ofquantum neural models [14] and the actual nature of therelationship between quantum and neurophysiologicalprocesses [15, 16] in the wet and 300 Kelvin warm braintissue have remained largely elusive [17], as argumentsfrom both biophysics and computational neurosciencespeak against a possibility of quantum-like neural-levelprocesses [18, 19]. It is therefore far from clear how thepresented [1], neurobiologically implausible quantum-holographic procedure for image storage and recall shouldactually interact with a biologically plausible, non-quantum(Gabor-wavelet) component and at which level should suchinteractions occur in the human brain?