2,102 research outputs found
Capacity, Fidelity, and Noise Tolerance of Associative Spatial-Temporal Memories Based on Memristive Neuromorphic Network
We have calculated the key characteristics of associative
(content-addressable) spatial-temporal memories based on neuromorphic networks
with restricted connectivity - "CrossNets". Such networks may be naturally
implemented in nanoelectronic hardware using hybrid CMOS/memristor circuits,
which may feature extremely high energy efficiency, approaching that of
biological cortical circuits, at much higher operation speed. Our numerical
simulations, in some cases confirmed by analytical calculations, have shown
that the characteristics depend substantially on the method of information
recording into the memory. Of the four methods we have explored, two look
especially promising - one based on the quadratic programming, and the other
one being a specific discrete version of the gradient descent. The latter
method provides a slightly lower memory capacity (at the same fidelity) then
the former one, but it allows local recording, which may be more readily
implemented in nanoelectronic hardware. Most importantly, at the synchronous
retrieval, both methods provide a capacity higher than that of the well-known
Ternary Content-Addressable Memories with the same number of nonvolatile memory
cells (e.g., memristors), though the input noise immunity of the CrossNet
memories is somewhat lower
Bio-inspired computational memory model of the Hippocampus: an approach to a neuromorphic spike-based Content-Addressable Memory
The brain has computational capabilities that surpass those of modern
systems, being able to solve complex problems efficiently in a simple way.
Neuromorphic engineering aims to mimic biology in order to develop new systems
capable of incorporating such capabilities. Bio-inspired learning systems
continue to be a challenge that must be solved, and much work needs to be done
in this regard. Among all brain regions, the hippocampus stands out as an
autoassociative short-term memory with the capacity to learn and recall
memories from any fragment of them. These characteristics make the hippocampus
an ideal candidate for developing bio-inspired learning systems that, in
addition, resemble content-addressable memories. Therefore, in this work we
propose a bio-inspired spiking content-addressable memory model based on the
CA3 region of the hippocampus with the ability to learn, forget and recall
memories, both orthogonal and non-orthogonal, from any fragment of them. The
model was implemented on the SpiNNaker hardware platform using Spiking Neural
Networks. A set of experiments based on functional, stress and applicability
tests were performed to demonstrate its correct functioning. This work presents
the first hardware implementation of a fully-functional bio-inspired spiking
hippocampal content-addressable memory model, paving the way for the
development of future more complex neuromorphic systems.Comment: 15 pages, 5 figures, journal, Spiking Neural Networ
Optical implementation of the Hopfield model
Optical implementation of content addressable associative memory based on the Hopfield model for neural networks and on the addition of nonlinear iterative feedback to a vector-matrix multiplier is described. Numerical and experimental results presented show that the approach is capable of introducing accuracy and robustness to optical processing while maintaining the traditional advantages of optics, namely, parallelism and massive interconnection capability. Moreover a potentially useful link between neural processing and optics that can be of interest in pattern recognition and machine vision is established
Embodied Artificial Intelligence through Distributed Adaptive Control: An Integrated Framework
In this paper, we argue that the future of Artificial Intelligence research
resides in two keywords: integration and embodiment. We support this claim by
analyzing the recent advances of the field. Regarding integration, we note that
the most impactful recent contributions have been made possible through the
integration of recent Machine Learning methods (based in particular on Deep
Learning and Recurrent Neural Networks) with more traditional ones (e.g.
Monte-Carlo tree search, goal babbling exploration or addressable memory
systems). Regarding embodiment, we note that the traditional benchmark tasks
(e.g. visual classification or board games) are becoming obsolete as
state-of-the-art learning algorithms approach or even surpass human performance
in most of them, having recently encouraged the development of first-person 3D
game platforms embedding realistic physics. Building upon this analysis, we
first propose an embodied cognitive architecture integrating heterogenous
sub-fields of Artificial Intelligence into a unified framework. We demonstrate
the utility of our approach by showing how major contributions of the field can
be expressed within the proposed framework. We then claim that benchmarking
environments need to reproduce ecologically-valid conditions for bootstrapping
the acquisition of increasingly complex cognitive skills through the concept of
a cognitive arms race between embodied agents.Comment: Updated version of the paper accepted to the ICDL-Epirob 2017
conference (Lisbon, Portugal
NASA JSC neural network survey results
A survey of Artificial Neural Systems in support of NASA's (Johnson Space Center) Automatic Perception for Mission Planning and Flight Control Research Program was conducted. Several of the world's leading researchers contributed papers containing their most recent results on artificial neural systems. These papers were broken into categories and descriptive accounts of the results make up a large part of this report. Also included is material on sources of information on artificial neural systems such as books, technical reports, software tools, etc
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