82 research outputs found
Functioning of Declarative Memory: Intersection between Neuropsychology and Mathematics
The understanding of memory has been a constant challenge for scientific research for centuries. The mnemonic processes, which determine the identity of the human being, have been investigated through multiple points of view, such as the psychological, neurophysiological and physical ones. The result is complex and multifaceted visions that should be integrated to provide a unitary and complete interpretation. A survey of the most recent scientific literature is carried out on the functioning of declarative memory, to analyse the relationship between real information coming from the outside world, the encoded event and the recovered memory. The aim of the essay is to investigate the neural correlates, which regulate the cognitive system in question, through a dual neuropsychological-mathematical interpretation. Neuropsychology sheds light on the anatomical, physiological and psychic mechanisms of memory while Mathematics associates the corresponding mathematical configurations to neural networks. The reunification process between the two disciplines is achieved through neuromorphic computational simulation that emulates mind uploading. The assembly of artificial neurons has the potential to clarify in detail the memory processes, the functioning of neural correlates and to carry out the mapping of the biological brain. We hope that the results obtained will provide new knowledge on mnestic mechanisms to contribute to the evolution of disciplines such as General Psychology, Forensic Neuroscience, Cognitive Rehabilitation and Awake Surgery
Intelligent Computing: The Latest Advances, Challenges and Future
Computing is a critical driving force in the development of human
civilization. In recent years, we have witnessed the emergence of intelligent
computing, a new computing paradigm that is reshaping traditional computing and
promoting digital revolution in the era of big data, artificial intelligence
and internet-of-things with new computing theories, architectures, methods,
systems, and applications. Intelligent computing has greatly broadened the
scope of computing, extending it from traditional computing on data to
increasingly diverse computing paradigms such as perceptual intelligence,
cognitive intelligence, autonomous intelligence, and human-computer fusion
intelligence. Intelligence and computing have undergone paths of different
evolution and development for a long time but have become increasingly
intertwined in recent years: intelligent computing is not only
intelligence-oriented but also intelligence-driven. Such cross-fertilization
has prompted the emergence and rapid advancement of intelligent computing.
Intelligent computing is still in its infancy and an abundance of innovations
in the theories, systems, and applications of intelligent computing are
expected to occur soon. We present the first comprehensive survey of literature
on intelligent computing, covering its theory fundamentals, the technological
fusion of intelligence and computing, important applications, challenges, and
future perspectives. We believe that this survey is highly timely and will
provide a comprehensive reference and cast valuable insights into intelligent
computing for academic and industrial researchers and practitioners
X-SRAM: Enabling In-Memory Boolean Computations in CMOS Static Random Access Memories
Silicon-based Static Random Access Memories (SRAM) and digital Boolean logic
have been the workhorse of the state-of-art computing platforms. Despite
tremendous strides in scaling the ubiquitous metal-oxide-semiconductor
transistor, the underlying \textit{von-Neumann} computing architecture has
remained unchanged. The limited throughput and energy-efficiency of the
state-of-art computing systems, to a large extent, results from the well-known
\textit{von-Neumann bottleneck}. The energy and throughput inefficiency of the
von-Neumann machines have been accentuated in recent times due to the present
emphasis on data-intensive applications like artificial intelligence, machine
learning \textit{etc}. A possible approach towards mitigating the overhead
associated with the von-Neumann bottleneck is to enable \textit{in-memory}
Boolean computations. In this manuscript, we present an augmented version of
the conventional SRAM bit-cells, called \textit{the X-SRAM}, with the ability
to perform in-memory, vector Boolean computations, in addition to the usual
memory storage operations. We propose at least six different schemes for
enabling in-memory vector computations including NAND, NOR, IMP (implication),
XOR logic gates with respect to different bit-cell topologies the 8T cell
and the 8T Differential cell. In addition, we also present a novel
\textit{`read-compute-store'} scheme, wherein the computed Boolean function can
be directly stored in the memory without the need of latching the data and
carrying out a subsequent write operation. The feasibility of the proposed
schemes has been verified using predictive transistor models and Monte-Carlo
variation analysis.Comment: This article has been accepted in a future issue of IEEE Transactions
on Circuits and Systems-I: Regular Paper
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