90,346 research outputs found
Brain-inspired conscious computing architecture
What type of artificial systems will claim to be conscious and will claim to experience qualia? The ability to comment upon physical states of a brain-like dynamical system coupled with its environment seems to be sufficient to make claims. The flow of internal states in such system, guided and limited by associative memory, is similar to the stream of consciousness. Minimal requirements for an artificial system that will claim to be conscious were given in form of specific architecture named articon. Nonverbal discrimination of the working memory states of the articon gives it the ability to experience different qualities of internal states. Analysis of the inner state flows of such a system during typical behavioral process shows that qualia are inseparable from perception and action. The role of consciousness in learning of skills, when conscious information processing is replaced by subconscious, is elucidated. Arguments confirming that phenomenal experience is a result of cognitive processes are presented. Possible philosophical objections based on the Chinese room and other arguments are discussed, but they are insufficient to refute claims articon’s claims. Conditions for genuine understanding that go beyond the Turing test are presented. Articons may fulfill such conditions and in principle the structure of their experiences may be arbitrarily close to human
Brain-Inspired Computing
This open access book constitutes revised selected papers from the 4th International Workshop on Brain-Inspired Computing, BrainComp 2019, held in Cetraro, Italy, in July 2019. The 11 papers presented in this volume were carefully reviewed and selected for inclusion in this book. They deal with research on brain atlasing, multi-scale models and simulation, HPC and data infra-structures for neuroscience as well as artificial and natural neural architectures
Towards brain-inspired computing
We present introductory considerations and analysis toward computing
applications based on the recently introduced deterministic logic scheme with
random spike (pulse) trains [Phys. Lett. A 373 (2009) 2338-2342]. Also, in
considering the questions, "Why random?" and "Why pulses?", we show that the
random pulse based scheme provides the advantages of realizing multivalued
deterministic logic. Pulse trains are realized by an element called
orthogonator. We discuss two different types of orthogonators, parallel
(intersection-based) and serial (demultiplexer-based) orthogonators. The last
one can be slower but it makes sequential logic design straightforward. We
propose generating a multidimensional logic hyperspace [Physics Letters A 373
(2009) 1928-1934] by using the zero-crossing events of uncorrelated Gaussian
electrical noises available in the chips. The spike trains in the hyperspace
are non-overlapping, and are referred to as neuro-bits. To demonstrate this
idea, we generate 3-dimensional hyperspace bases using 2 Gaussian noises as
sources for neuro-bits, respectively. In such a scenario, the detection of
different hyperspace basis elements may have vastly differing delays. We show
that it is possible to provide an identical speed for all the hyperspace bases
elements using correlated noise sources, and demonstrate this for the 2
neuro-bits situations. The key impact of this paper is to demonstrate that a
logic design approach using such neuro-bits can yield a fast, low power
processing and environmental variation tolerant means of designing computer
circuitry. It also enables the realization of multi-valued logic, significantly
increasing the complexity of computer circuits by allowing several neuro-bits
to be transmitted on a single wire.Comment: 10 page
Brain-Inspired Conscious Computing Architecture
What type of artificial systems will claim to be conscious and will claim to experience qualia? The ability to comment upon physical states of a brain-like dynamical system coupled with its environment seems to be sufficient to make claims. The flow of internal states in such systems, guided and limited by associative memory, is similar to the stream of consciousness. A specific architecture of an artificial system, termed articon, is introduced that by its very design has to claim being conscious. Non-verbal discrimination of the working memory states of the articon gives it the ability to experience different qualities of internal states. Analysis of the flow of inner states of such a system during typical behavioral process shows that qualia are inseparable from perception and action. The role of consciousness in learning of skills — when conscious information processing is replaced by subconscious — is elucidated. Arguments confirming that phenomenal experience is a result of cognitive processes are presented. Possible philosophical objections based on the Chinese room and other arguments are discussed, but they are insufficient to refute articon’s claims that it is conscious. Conditions for genuine understanding that go beyond the Turing test are presented. Articons may fulfill such conditions and in principle the structure of their experiences may be arbitrarily close to huma
Sequence learning in Associative Neuronal-Astrocytic Network
The neuronal paradigm of studying the brain has left us with limitations in
both our understanding of how neurons process information to achieve biological
intelligence and how such knowledge may be translated into artificial
intelligence and its most brain-derived branch, neuromorphic computing.
Overturning our fundamental assumptions of how the brain works, the recent
exploration of astrocytes is revealing that these long-neglected brain cells
dynamically regulate learning by interacting with neuronal activity at the
synaptic level. Following recent experimental evidence, we designed an
associative, Hopfield-type, neuronal-astrocytic network and analyzed the
dynamics of the interaction between neurons and astrocytes. We show that
astrocytes were sufficient to trigger transitions between learned memories in
the neuronal component of the network. Further, we mathematically derived the
timing of the transitions that was governed by the dynamics of the
calcium-dependent slow-currents in the astrocytic processes. Overall, we
provide a brain-morphic mechanism for sequence learning that is inspired by,
and aligns with, recent experimental findings. To evaluate our model, we
emulated astrocytic atrophy and showed that memory recall becomes significantly
impaired after a critical point of affected astrocytes was reached. This
brain-inspired and brain-validated approach supports our ongoing efforts to
incorporate non-neuronal computing elements in neuromorphic information
processing.Comment: 8 pages, 5 figure
Leveraging Probabilistic Switching in Superparamagnets for Temporal Information Encoding in Neuromorphic Systems
Brain-inspired computing - leveraging neuroscientific principles underpinning
the unparalleled efficiency of the brain in solving cognitive tasks - is
emerging to be a promising pathway to solve several algorithmic and
computational challenges faced by deep learning today. Nonetheless, current
research in neuromorphic computing is driven by our well-developed notions of
running deep learning algorithms on computing platforms that perform
deterministic operations. In this article, we argue that taking a different
route of performing temporal information encoding in probabilistic neuromorphic
systems may help solve some of the current challenges in the field. The article
considers superparamagnetic tunnel junctions as a potential pathway to enable a
new generation of brain-inspired computing that combines the facets and
associated advantages of two complementary insights from computational
neuroscience -- how information is encoded and how computing occurs in the
brain. Hardware-algorithm co-design analysis demonstrates accuracy of
a state-compressed 3-layer spintronics enabled stochastic spiking network on
the MNIST dataset with high spiking sparsity due to temporal information
encoding
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