Specific impairments in cognitive development: a dynamical systems approach

Neuropsychologists have frequently proposed that domain-specific deficits can be observed in developmental disorders (e.g., phonology in dyslexia, theory of mind in autism, grammar in specific language impairment, face recognition in prosopagnosia, mathematics in dyscalculia). These deficits appeal to a modular cognitive architecture. However, specific developmental deficits are at odds with theories that posit a high degree of interactivity between cognitive abilities across development. If there are early deficits, why do these not spread across the cognitive system during development? Or experience compensatory help from other initially intact components? We address these questions within a dynamical systems framework (van der Maas et al., 2006). We explore the conditions for deficit spread and compensation for a range of possible cognitive architectures, from modular to fully distributed. While preliminary, the results point to the importance of specifying precisely the normal developmental architecture of a system prior to characterizing patterns of impairment that might emerge from it

Cognition-Based Networks: A New Perspective on Network Optimization Using Learning and Distributed Intelligence

IEEE Access Volume 3, 2015, Article number 7217798, Pages 1512-1530 Open Access Cognition-based networks: A new perspective on network optimization using learning and distributed intelligence (Article) Zorzi, M.a , Zanella, A.a, Testolin, A.b, De Filippo De Grazia, M.b, Zorzi, M.bc a Department of Information Engineering, University of Padua, Padua, Italy b Department of General Psychology, University of Padua, Padua, Italy c IRCCS San Camillo Foundation, Venice-Lido, Italy View additional affiliations View references (107) Abstract In response to the new challenges in the design and operation of communication networks, and taking inspiration from how living beings deal with complexity and scalability, in this paper we introduce an innovative system concept called COgnition-BAsed NETworkS (COBANETS). The proposed approach develops around the systematic application of advanced machine learning techniques and, in particular, unsupervised deep learning and probabilistic generative models for system-wide learning, modeling, optimization, and data representation. Moreover, in COBANETS, we propose to combine this learning architecture with the emerging network virtualization paradigms, which make it possible to actuate automatic optimization and reconfiguration strategies at the system level, thus fully unleashing the potential of the learning approach. Compared with the past and current research efforts in this area, the technical approach outlined in this paper is deeply interdisciplinary and more comprehensive, calling for the synergic combination of expertise of computer scientists, communications and networking engineers, and cognitive scientists, with the ultimate aim of breaking new ground through a profound rethinking of how the modern understanding of cognition can be used in the management and optimization of telecommunication network