63,567 research outputs found
Learning to Represent Haptic Feedback for Partially-Observable Tasks
The sense of touch, being the earliest sensory system to develop in a human
body [1], plays a critical part of our daily interaction with the environment.
In order to successfully complete a task, many manipulation interactions
require incorporating haptic feedback. However, manually designing a feedback
mechanism can be extremely challenging. In this work, we consider manipulation
tasks that need to incorporate tactile sensor feedback in order to modify a
provided nominal plan. To incorporate partial observation, we present a new
framework that models the task as a partially observable Markov decision
process (POMDP) and learns an appropriate representation of haptic feedback
which can serve as the state for a POMDP model. The model, that is parametrized
by deep recurrent neural networks, utilizes variational Bayes methods to
optimize the approximate posterior. Finally, we build on deep Q-learning to be
able to select the optimal action in each state without access to a simulator.
We test our model on a PR2 robot for multiple tasks of turning a knob until it
clicks.Comment: IEEE International Conference on Robotics and Automation (ICRA), 201
A scalable multi-core architecture with heterogeneous memory structures for Dynamic Neuromorphic Asynchronous Processors (DYNAPs)
Neuromorphic computing systems comprise networks of neurons that use
asynchronous events for both computation and communication. This type of
representation offers several advantages in terms of bandwidth and power
consumption in neuromorphic electronic systems. However, managing the traffic
of asynchronous events in large scale systems is a daunting task, both in terms
of circuit complexity and memory requirements. Here we present a novel routing
methodology that employs both hierarchical and mesh routing strategies and
combines heterogeneous memory structures for minimizing both memory
requirements and latency, while maximizing programming flexibility to support a
wide range of event-based neural network architectures, through parameter
configuration. We validated the proposed scheme in a prototype multi-core
neuromorphic processor chip that employs hybrid analog/digital circuits for
emulating synapse and neuron dynamics together with asynchronous digital
circuits for managing the address-event traffic. We present a theoretical
analysis of the proposed connectivity scheme, describe the methods and circuits
used to implement such scheme, and characterize the prototype chip. Finally, we
demonstrate the use of the neuromorphic processor with a convolutional neural
network for the real-time classification of visual symbols being flashed to a
dynamic vision sensor (DVS) at high speed.Comment: 17 pages, 14 figure
Nanophotonic reservoir computing with photonic crystal cavities to generate periodic patterns
Reservoir computing (RC) is a technique in machine learning inspired by neural systems. RC has been used successfully to solve complex problems such as signal classification and signal generation. These systems are mainly implemented in software, and thereby they are limited in speed and power efficiency. Several optical and optoelectronic implementations have been demonstrated, in which the system has signals with an amplitude and phase. It is proven that these enrich the dynamics of the system, which is beneficial for the performance. In this paper, we introduce a novel optical architecture based on nanophotonic crystal cavities. This allows us to integrate many neurons on one chip, which, compared with other photonic solutions, closest resembles a classical neural network. Furthermore, the components are passive, which simplifies the design and reduces the power consumption. To assess the performance of this network, we train a photonic network to generate periodic patterns, using an alternative online learning rule called first-order reduced and corrected error. For this, we first train a classical hyperbolic tangent reservoir, but then we vary some of the properties to incorporate typical aspects of a photonics reservoir, such as the use of continuous-time versus discrete-time signals and the use of complex-valued versus real-valued signals. Then, the nanophotonic reservoir is simulated and we explore the role of relevant parameters such as the topology, the phases between the resonators, the number of nodes that are biased and the delay between the resonators. It is important that these parameters are chosen such that no strong self-oscillations occur. Finally, our results show that for a signal generation task a complex-valued, continuous-time nanophotonic reservoir outperforms a classical (i.e., discrete-time, real-valued) leaky hyperbolic tangent reservoir (normalized root-mean-square errors = 0.030 versus NRMSE = 0.127)
PYRO-NN: Python Reconstruction Operators in Neural Networks
Purpose: Recently, several attempts were conducted to transfer deep learning
to medical image reconstruction. An increasingly number of publications follow
the concept of embedding the CT reconstruction as a known operator into a
neural network. However, most of the approaches presented lack an efficient CT
reconstruction framework fully integrated into deep learning environments. As a
result, many approaches are forced to use workarounds for mathematically
unambiguously solvable problems. Methods: PYRO-NN is a generalized framework to
embed known operators into the prevalent deep learning framework Tensorflow.
The current status includes state-of-the-art parallel-, fan- and cone-beam
projectors and back-projectors accelerated with CUDA provided as Tensorflow
layers. On top, the framework provides a high level Python API to conduct FBP
and iterative reconstruction experiments with data from real CT systems.
Results: The framework provides all necessary algorithms and tools to design
end-to-end neural network pipelines with integrated CT reconstruction
algorithms. The high level Python API allows a simple use of the layers as
known from Tensorflow. To demonstrate the capabilities of the layers, the
framework comes with three baseline experiments showing a cone-beam short scan
FDK reconstruction, a CT reconstruction filter learning setup, and a TV
regularized iterative reconstruction. All algorithms and tools are referenced
to a scientific publication and are compared to existing non deep learning
reconstruction frameworks. The framework is available as open-source software
at \url{https://github.com/csyben/PYRO-NN}. Conclusions: PYRO-NN comes with the
prevalent deep learning framework Tensorflow and allows to setup end-to-end
trainable neural networks in the medical image reconstruction context. We
believe that the framework will be a step towards reproducible researchComment: V1: Submitted to Medical Physics, 11 pages, 7 figure
Nearly extensive sequential memory lifetime achieved by coupled nonlinear neurons
Many cognitive processes rely on the ability of the brain to hold sequences
of events in short-term memory. Recent studies have revealed that such memory
can be read out from the transient dynamics of a network of neurons. However,
the memory performance of such a network in buffering past information has only
been rigorously estimated in networks of linear neurons. When signal gain is
kept low, so that neurons operate primarily in the linear part of their
response nonlinearity, the memory lifetime is bounded by the square root of the
network size. In this work, I demonstrate that it is possible to achieve a
memory lifetime almost proportional to the network size, "an extensive memory
lifetime", when the nonlinearity of neurons is appropriately utilized. The
analysis of neural activity revealed that nonlinear dynamics prevented the
accumulation of noise by partially removing noise in each time step. With this
error-correcting mechanism, I demonstrate that a memory lifetime of order
can be achieved.Comment: 21 pages, 5 figures, the manuscript has been accepted for publication
in Neural Computatio
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