22 research outputs found
Universal discrete-time reservoir computers with stochastic inputs and linear readouts using non-homogeneous state-affine systems
A new class of non-homogeneous state-affine systems is introduced for use in
reservoir computing. Sufficient conditions are identified that guarantee first,
that the associated reservoir computers with linear readouts are causal,
time-invariant, and satisfy the fading memory property and second, that a
subset of this class is universal in the category of fading memory filters with
stochastic almost surely uniformly bounded inputs. This means that any
discrete-time filter that satisfies the fading memory property with random
inputs of that type can be uniformly approximated by elements in the
non-homogeneous state-affine family.Comment: 41 page
An electronic neuromorphic system for real-time detection of High Frequency Oscillations (HFOs) in intracranial EEG
In this work, we present a neuromorphic system that combines for the first
time a neural recording headstage with a signal-to-spike conversion circuit and
a multi-core spiking neural network (SNN) architecture on the same die for
recording, processing, and detecting High Frequency Oscillations (HFO), which
are biomarkers for the epileptogenic zone. The device was fabricated using a
standard 0.18m CMOS technology node and has a total area of 99mm. We
demonstrate its application to HFO detection in the iEEG recorded from 9
patients with temporal lobe epilepsy who subsequently underwent epilepsy
surgery. The total average power consumption of the chip during the detection
task was 614.3W. We show how the neuromorphic system can reliably detect
HFOs: the system predicts postsurgical seizure outcome with state-of-the-art
accuracy, specificity and sensitivity (78%, 100%, and 33% respectively). This
is the first feasibility study towards identifying relevant features in
intracranial human data in real-time, on-chip, using event-based processors and
spiking neural networks. By providing "neuromorphic intelligence" to neural
recording circuits the approach proposed will pave the way for the development
of systems that can detect HFO areas directly in the operation room and improve
the seizure outcome of epilepsy surgery.Comment: 16 pages. A short video describing the rationale underlying the study
can be viewed on https://youtu.be/NuAA91fdma
Towards a theoretical foundation for morphological computation with compliant bodies
Erworben im Rahmen der Schweizer Nationallizenzen (http://www.nationallizenzen.ch)The control of compliant robots is, due to their often nonlinear and complex dynamics, inherently difficult. The vision of morphological computation proposes to view these aspects not only as problems, but rather also as parts of the solution. Non-rigid body parts are not seen anymore as imperfect realizations of rigid body parts, but rather as potential computational resources. The applicability of this vision has already been demonstrated for a variety of complex robot control problems. Nevertheless, a theoretical basis for understanding the capabilities and limitations of morphological computation has been missing so far. We present a model for morphological computation with compliant bodies, where a precise mathematical characterization of the potential computational contribution of a complex physical body is feasible. The theory suggests that complexity and nonlinearity, typically unwanted properties of robots, are desired features in order to provide computational power. We demonstrate that simple generic models of physical bodies, based on mass-spring systems, can be used to implement complex nonlinear operators. By adding a simple readout (which is static and linear) to the morphology such devices are able to emulate complex mappings of input to output streams in continuous time. Hence, by outsourcing parts of the computation to the physical body, the difficult problem of learning to control a complex body, could be reduced to a simple and perspicuous learning task, which can not get stuck in local minima of an error function
Towards a theoretical foundation for morphological computation with compliant bodies
The control of compliant robots is, due to their often nonlinear and complex dynamics, inherently difficult. The vision of morphological computation proposes to view these aspects not only as problems, but rather also as parts of the solution. Non-rigid body parts are not seen anymore as imperfect realizations of rigid body parts, but rather as potential computational resources. The applicability of this vision has already been demonstrated for a variety of complex robot control problems. Nevertheless, a theoretical basis for understanding the capabilities and limitations of morphological computation has been missing so far. We present a model for morphological computation with compliant bodies, where a precise mathematical characterization of the potential computational contribution of a complex physical body is feasible. The theory suggests that complexity and nonlinearity, typically unwanted properties of robots, are desired features in order to provide computational power. We demonstrate that simple generic models of physical bodies, based on mass-spring systems, can be used to implement complex nonlinear operators. By adding a simple readout (which is static and linear) to the morphology such devices are able to emulate complex mappings of input to output streams in continuous time. Hence, by outsourcing parts of the computation to the physical body, the difficult problem of learning to control a complex body, could be reduced to a simple and perspicuous learning task, which can not get stuck in local minima of an error functio
Learning strange attractors with reservoir systems
This paper shows that the celebrated embedding theorem of Takens is a particular case of a much more general statement according to which, randomly generated linear state-space representations of generic observations of an invertible dynamical system carry in their wake an embedding of the phase space dynamics into the chosen Euclidean state space. This embedding coincides with a natural generalized synchronization that arises in this setup and that yields a topological conjugacy between the state-space dynamics driven by the generic observations of the dynamical system and the dynamical system itself. This result provides additional tools for the representation, learning, and analysis of chaotic attractors and sheds additional light on the reservoir computing phenomenon that appears in the context of recurrent neural networks.</p
Learning strange attractors with reservoir systems
This paper shows that the celebrated embedding theorem of Takens is a particular case of a much more general statement according to which, randomly generated linear state-space representations of generic observations of an invertible dynamical system carry in their wake an embedding of the phase space dynamics into the chosen Euclidean state space. This embedding coincides with a natural generalized synchronization that arises in this setup and that yields a topological conjugacy between the state-space dynamics driven by the generic observations of the dynamical system and the dynamical system itself. This result provides additional tools for the representation, learning, and analysis of chaotic attractors and sheds additional light on the reservoir computing phenomenon that appears in the context of recurrent neural networks.</p
Learning strange attractors with reservoir systems
This paper shows that the celebrated Embedding Theorem of Takens is a
particular case of a much more general statement according to which, randomly
generated linear state-space representations of generic observations of an
invertible dynamical system carry in their wake an embedding of the phase space
dynamics into the chosen Euclidean state space. This embedding coincides with a
natural generalized synchronization that arises in this setup and that yields a
topological conjugacy between the state-space dynamics driven by the generic
observations of the dynamical system and the dynamical system itself. This
result provides additional tools for the representation, learning, and analysis
of chaotic attractors and sheds additional light on the reservoir computing
phenomenon that appears in the context of recurrent neural networks.Comment: 36 pages, 11 figure