Memory and information processing in neuromorphic systems

A striking difference between brain-inspired neuromorphic processors and current von Neumann processors architectures is the way in which memory and processing is organized. As Information and Communication Technologies continue to address the need for increased computational power through the increase of cores within a digital processor, neuromorphic engineers and scientists can complement this need by building processor architectures where memory is distributed with the processing. In this paper we present a survey of brain-inspired processor architectures that support models of cortical networks and deep neural networks. These architectures range from serial clocked implementations of multi-neuron systems to massively parallel asynchronous ones and from purely digital systems to mixed analog/digital systems which implement more biological-like models of neurons and synapses together with a suite of adaptation and learning mechanisms analogous to the ones found in biological nervous systems. We describe the advantages of the different approaches being pursued and present the challenges that need to be addressed for building artificial neural processing systems that can display the richness of behaviors seen in biological systems.Comment: Submitted to Proceedings of IEEE, review of recently proposed neuromorphic computing platforms and system

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

American Geriatrics Society and National Institute on Aging Bench-to-Bedside conference: sensory impairment and cognitive decline in older adults

This article summarizes the presentations and recommendations of the tenth annual American Geriatrics Society and National Institute on Aging Bench‐to‐Bedside research conference, “Sensory Impairment and Cognitive Decline,” on October 2–3, 2017, in Bethesda, Maryland. The risk of impairment in hearing, vision, and other senses increases with age, and almost 15% of individuals aged 70 and older have dementia. As the number of older adults increases, sensory and cognitive impairments will affect a growing proportion of the population. To limit its scope, this conference focused on sensory impairments affecting vision and hearing. Comorbid vision, hearing, and cognitive impairments in older adults are more common than would be expected by chance alone, suggesting that some common mechanisms might affect these neurological systems. This workshop explored the mechanisms and consequences of comorbid vision, hearing, and cognitive impairment in older adults; effects of sensory loss on the aging brain; and bench‐to‐bedside innovations and research opportunities. Presenters and participants identified many research gaps and questions; the top priorities fell into 3 themes: mechanisms, measurement, and interventions. The workshop delineated specific research questions that provide opportunities to improve outcomes in this growing population.Funding was provided by National Institutes of Health (NIH) Grant U13 AG054139-01. Dr. Whitson's efforts and contributions were supported by R01AG043438, R24AG045050, UH2AG056925, and 5P30AG028716. Dr. Lin's effort and contributions were also supported by R01AG055426, R01HL096812, and R33DC015062. (U13 AG054139-01 - National Institutes of Health (NIH); R01AG043438; R24AG045050; UH2AG056925; 5P30AG028716; R01AG055426; R01HL096812; R33DC015062)Accepted manuscrip

Backwards is the way forward: feedback in the cortical hierarchy predicts the expected future

Clark offers a powerful description of the brain as a prediction machine, which offers progress on two distinct levels. First, on an abstract conceptual level, it provides a unifying framework for perception, action, and cognition (including subdivisions such as attention, expectation, and imagination). Second, hierarchical prediction offers progress on a concrete descriptive level for testing and constraining conceptual elements and mechanisms of predictive coding models (estimation of predictions, prediction errors, and internal models)