Abstract
Connectionist models can be characterized within the more general framework of probabilistic graphical models, which allow to efficiently describe complex statistical distributions involving a large number of interacting variables. This integration allows building more realistic computational models of cognitive functions, which more faithfully reflect the underlying neural mechanisms at the same time providing a useful bridge to higher-level descriptions in terms of Bayesian computations. Here we discuss a powerful class of graphical models that can be implemented as stochastic, generative neural networks. These models overcome many limitations associated with classic connectionist models, for example by exploiting unsupervised learning in hierarchical architectures (deep networks) and by taking into account top-down, predictive processing supported by feedback loops. We review some recent cognitive models based on generative networks, and we point out promising research directions to investigate neuropsychological disorders within this approach. Though further efforts are required in order to fill the gap between structured Bayesian models and more realistic, biophysical models of neuronal dynamics, we argue that generative neural networks have the potential to bridge these levels of analysis, thereby improving our understanding of the neural bases of cognition and of pathologies caused by brain damage.
Highlights
Despite the enormous progress in the prevention and treatment of neuropsychological disorders, traumatic brain injury and stroke are still among the major causes of adult disability and death (Mathers et al, 2008; Feigin et al, 2014)
A distinguishing feature of parallel distributed processing (PDP) models is their ability to adapt to the environment, which allows to simulate behavioral patterns associated with a broad range of cognitive functions and to study how learning mechanisms support cognitive development and knowledge acquisition (e.g., Elman et al, 1996)
The tight link between structure and function in PDP models allows to investigate how changes in the underlying processing mechanisms are reflected by changes in overt behavior, thereby providing a principled way to simulate neuropsychological disorders following brain damage (e.g., Hinton and Shallice, 1991; Plaut and Shallice, 1993; McClelland et al, 1995)
Summary
Despite the enormous progress in the prevention and treatment of neuropsychological disorders, traumatic brain injury and stroke are still among the major causes of adult disability and death (Mathers et al, 2008; Feigin et al, 2014). Besides the need for labeled patterns, classic PDP models usually entail an over-simplistic, ‘‘shallow’’ processing architecture, involving only one layer of hidden units and strictly feed-forward connectivity This is in sharp contrast with well-known properties of cortical circuits, which exhibit a hierarchical organization (Felleman and Van Essen, 1991) where information processing relies on both feed-forward and feedback mechanisms (Sillito et al, 2006; Gilbert and Sigman, 2007). A powerful class of stochastic, recurrent neural networks can be characterized as fully-connected graphical models, where the undirected nature of the edges implies bidirectional flow of information between the nodes (Ackley et al, 1985) This probabilistic interpretation of neural networks provides a useful bridge to more abstract computational descriptions of cognitive processes (Griffiths et al, 2008), suggesting how high-level Bayesian computations might be implemented in neural circuits. A possible role for recurrent feed-forward/feedback loops in the cerebral cortex might be to integrate top-down, contextual priors with bottom-up, sensory observations, so as to implement concurrent probabilistic inference along the whole cortical hierarchy (Lee and Mumford, 2003; McClelland, 2013)
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