517 research outputs found
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
Digital twin brain: a bridge between biological intelligence and artificial intelligence
In recent years, advances in neuroscience and artificial intelligence have
paved the way for unprecedented opportunities for understanding the complexity
of the brain and its emulation by computational systems. Cutting-edge
advancements in neuroscience research have revealed the intricate relationship
between brain structure and function, while the success of artificial neural
networks highlights the importance of network architecture. Now is the time to
bring them together to better unravel how intelligence emerges from the brain's
multiscale repositories. In this review, we propose the Digital Twin Brain
(DTB) as a transformative platform that bridges the gap between biological and
artificial intelligence. It consists of three core elements: the brain
structure that is fundamental to the twinning process, bottom-layer models to
generate brain functions, and its wide spectrum of applications. Crucially,
brain atlases provide a vital constraint, preserving the brain's network
organization within the DTB. Furthermore, we highlight open questions that
invite joint efforts from interdisciplinary fields and emphasize the
far-reaching implications of the DTB. The DTB can offer unprecedented insights
into the emergence of intelligence and neurological disorders, which holds
tremendous promise for advancing our understanding of both biological and
artificial intelligence, and ultimately propelling the development of
artificial general intelligence and facilitating precision mental healthcare
Layer-Dependent Attentional Processing by Top-down Signals in a Visual Cortical Microcircuit Model
A vast amount of information about the external world continuously flows into the brain, whereas its capacity to process such information is limited. Attention enables the brain to allocate its resources of information processing to selected sensory inputs for reducing its computational load, and effects of attention have been extensively studied in visual information processing. However, how the microcircuit of the visual cortex processes attentional information from higher areas remains largely unknown. Here, we explore the complex interactions between visual inputs and an attentional signal in a computational model of the visual cortical microcircuit. Our model not only successfully accounts for previous experimental observations of attentional effects on visual neuronal responses, but also predicts contrasting differences in the attentional effects of top-down signals between cortical layers: attention to a preferred stimulus of a column enhances neuronal responses of layers 2/3 and 5, the output stations of cortical microcircuits, whereas attention suppresses neuronal responses of layer 4, the input station of cortical microcircuits. We demonstrate that the specific modulation pattern of layer-4 activity, which emerges from inter-laminar synaptic connections, is crucial for a rapid shift of attention to a currently unattended stimulus. Our results suggest that top-down signals act differently on different layers of the cortical microcircuit
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