29,022 research outputs found
Emergence and reconfiguration of modular structure for synaptic neural networks during continual familiarity detection
While advances in artificial intelligence and neuroscience have enabled the
emergence of neural networks capable of learning a wide variety of tasks, our
understanding of the temporal dynamics of these networks remains limited. Here,
we study the temporal dynamics during learning of Hebbian Feedforward (HebbFF)
neural networks in tasks of continual familiarity detection. Drawing
inspiration from the field of network neuroscience, we examine the network's
dynamic reconfiguration, focusing on how network modules evolve throughout
learning. Through a comprehensive assessment involving metrics like network
accuracy, modular flexibility, and distribution entropy across diverse learning
modes, our approach reveals various previously unknown patterns of network
reconfiguration. In particular, we find that the emergence of network
modularity is a salient predictor of performance, and that modularization
strengthens with increasing flexibility throughout learning. These insights not
only elucidate the nuanced interplay of network modularity, accuracy, and
learning dynamics but also bridge our understanding of learning in artificial
and biological realms
A superconducting nanowire spiking element for neural networks
As the limits of traditional von Neumann computing come into view, the
brain's ability to communicate vast quantities of information using low-power
spikes has become an increasing source of inspiration for alternative
architectures. Key to the success of these largescale neural networks is a
power-efficient spiking element that is scalable and easily interfaced with
traditional control electronics. In this work, we present a spiking element
fabricated from superconducting nanowires that has pulse energies on the order
of ~10 aJ. We demonstrate that the device reproduces essential characteristics
of biological neurons, such as a refractory period and a firing threshold.
Through simulations using experimentally measured device parameters, we show
how nanowire-based networks may be used for inference in image recognition, and
that the probabilistic nature of nanowire switching may be exploited for
modeling biological processes and for applications that rely on stochasticity.Comment: 5 main figures; 7 supplemental figure
Neurocognitive Informatics Manifesto.
Informatics studies all aspects of the structure of natural and artificial information systems. Theoretical and abstract approaches to information have made great advances, but human information processing is still unmatched in many areas, including information management, representation and understanding. Neurocognitive informatics is a new, emerging field that should help to improve the matching of artificial and natural systems, and inspire better computational algorithms to solve problems that are still beyond the reach of machines. In this position paper examples of neurocognitive inspirations and promising directions in this area are given
Flexible couplings: diffusing neuromodulators and adaptive robotics
Recent years have seen the discovery of freely diffusing gaseous neurotransmitters, such as nitric oxide (NO), in biological nervous systems. A type of artificial neural network (ANN) inspired by such gaseous signaling, the GasNet, has previously been shown to be more evolvable than traditional ANNs when used as an artificial nervous system in an evolutionary robotics setting, where evolvability means consistent speed to very good solutions¿here, appropriate sensorimotor behavior-generating systems. We present two new versions of the GasNet, which take further inspiration from the properties of neuronal gaseous signaling. The plexus model is inspired by the extraordinary NO-producing cortical plexus structure of neural fibers and the properties of the diffusing NO signal it generates. The receptor model is inspired by the mediating action of neurotransmitter receptors. Both models are shown to significantly further improve evolvability. We describe a series of analyses suggesting that the reasons for the increase in evolvability are related to the flexible loose coupling of distinct signaling mechanisms, one ¿chemical¿ and one ¿electrical.
Evolution and development of complex computational systems using the paradigm of metabolic computing in Epigenetic Tracking
Epigenetic Tracking (ET) is an Artificial Embryology system which allows for
the evolution and development of large complex structures built from artificial
cells. In terms of the number of cells, the complexity of the bodies generated
with ET is comparable with the complexity of biological organisms. We have
previously used ET to simulate the growth of multicellular bodies with
arbitrary 3-dimensional shapes which perform computation using the paradigm of
"metabolic computing". In this paper we investigate the memory capacity of such
computational structures and analyse the trade-off between shape and
computation. We now plan to build on these foundations to create a
biologically-inspired model in which the encoding of the phenotype is efficient
(in terms of the compactness of the genome) and evolvable in tasks involving
non-trivial computation, robust to damage and capable of self-maintenance and
self-repair.Comment: In Proceedings Wivace 2013, arXiv:1309.712
Born to learn: The inspiration, progress, and future of evolved plastic artificial neural networks
Biological plastic neural networks are systems of extraordinary computational
capabilities shaped by evolution, development, and lifetime learning. The
interplay of these elements leads to the emergence of adaptive behavior and
intelligence. Inspired by such intricate natural phenomena, Evolved Plastic
Artificial Neural Networks (EPANNs) use simulated evolution in-silico to breed
plastic neural networks with a large variety of dynamics, architectures, and
plasticity rules: these artificial systems are composed of inputs, outputs, and
plastic components that change in response to experiences in an environment.
These systems may autonomously discover novel adaptive algorithms, and lead to
hypotheses on the emergence of biological adaptation. EPANNs have seen
considerable progress over the last two decades. Current scientific and
technological advances in artificial neural networks are now setting the
conditions for radically new approaches and results. In particular, the
limitations of hand-designed networks could be overcome by more flexible and
innovative solutions. This paper brings together a variety of inspiring ideas
that define the field of EPANNs. The main methods and results are reviewed.
Finally, new opportunities and developments are presented
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