11,758 research outputs found
Universal neural field computation
Turing machines and G\"odel numbers are important pillars of the theory of
computation. Thus, any computational architecture needs to show how it could
relate to Turing machines and how stable implementations of Turing computation
are possible. In this chapter, we implement universal Turing computation in a
neural field environment. To this end, we employ the canonical symbologram
representation of a Turing machine obtained from a G\"odel encoding of its
symbolic repertoire and generalized shifts. The resulting nonlinear dynamical
automaton (NDA) is a piecewise affine-linear map acting on the unit square that
is partitioned into rectangular domains. Instead of looking at point dynamics
in phase space, we then consider functional dynamics of probability
distributions functions (p.d.f.s) over phase space. This is generally described
by a Frobenius-Perron integral transformation that can be regarded as a neural
field equation over the unit square as feature space of a dynamic field theory
(DFT). Solving the Frobenius-Perron equation yields that uniform p.d.f.s with
rectangular support are mapped onto uniform p.d.f.s with rectangular support,
again. We call the resulting representation \emph{dynamic field automaton}.Comment: 21 pages; 6 figures. arXiv admin note: text overlap with
arXiv:1204.546
Real-time phase correlation based integrated system for seizure detection
This paper reports a low area, low power, integer-based digital processor for the calculation of phase synchronization between two neural signals. The processor calculates the phase-frequency content of a signal by identifying the specific time periods associated with two consecutive minima. The simplicity of this phase-frequency content identifier allows for the digital processor to utilize only basic digital blocks, such as registers, counters, adders and subtractors, without incorporating any complex multiplication and or division algorithms. In fact, the processor, fabricated in a 0.18μm CMOS process, only occupies an area of 0.0625μm2 and consumes 12.5nW from a 1.2V supply voltage when operated at 128kHz. These low-area, low-power features make the proposed processor a valuable computing element in closed loop neural prosthesis for the treatment of neural diseases, such as epilepsy, or for extracting functional connectivity maps between different recording sites in the brain.Ministerio de Economía y Competitividad TEC2016- 80923-
Adaptive cancelation of self-generated sensory signals in a whisking robot
Sensory signals are often caused by one's own active movements. This raises a problem of discriminating between self-generated sensory signals and signals generated by the external world. Such discrimination is of general importance for robotic systems, where operational robustness is dependent on the correct interpretation of sensory signals. Here, we investigate this problem in the context of a whiskered robot. The whisker sensory signal comprises two components: one due to contact with an object (externally generated) and another due to active movement of the whisker (self-generated). We propose a solution to this discrimination problem based on adaptive noise cancelation, where the robot learns to predict the sensory consequences of its own movements using an adaptive filter. The filter inputs (copy of motor commands) are transformed by Laguerre functions instead of the often-used tapped-delay line, which reduces model order and, therefore, computational complexity. Results from a contact-detection task demonstrate that false positives are significantly reduced using the proposed scheme
Data-guide for brain deformation in surgery: comparison of linear and nonlinear models
<p>Abstract</p> <p>Background</p> <p>Pre-operative imaging devices generate high-resolution images but intra-operative imaging devices generate low-resolution images. To use high-resolution pre-operative images during surgery, they must be deformed to reflect intra-operative geometry of brain.</p> <p>Methods</p> <p>We employ biomechanical models, guided by low resolution intra-operative images, to determine location of normal and abnormal regions of brain after craniotomy. We also employ finite element methods to discretize and solve the related differential equations. In the process, pre- and intra-operative images are utilized and corresponding points are determined and used to optimize parameters of the models. This paper develops a nonlinear model and compares it with linear models while our previous work developed and compared linear models (mechanical and elastic).</p> <p>Results</p> <p>Nonlinear model is evaluated and compared with linear models using simulated and real data. Partial validation using intra-operative images indicates that the proposed models reduce the localization error caused by brain deformation after craniotomy.</p> <p>Conclusions</p> <p>The proposed nonlinear model generates more accurate results than the linear models. When guided by limited intra-operative surface data, it predicts deformation of entire brain. Its execution time is however considerably more than those of linear models.</p
Generation of Explicit Knowledge from Empirical Data through Pruning of Trainable Neural Networks
This paper presents a generalized technology of extraction of explicit
knowledge from data. The main ideas are 1) maximal reduction of network
complexity (not only removal of neurons or synapses, but removal all the
unnecessary elements and signals and reduction of the complexity of elements),
2) using of adjustable and flexible pruning process (the pruning sequence
shouldn't be predetermined - the user should have a possibility to prune
network on his own way in order to achieve a desired network structure for the
purpose of extraction of rules of desired type and form), and 3) extraction of
rules not in predetermined but any desired form. Some considerations and notes
about network architecture and training process and applicability of currently
developed pruning techniques and rule extraction algorithms are discussed. This
technology, being developed by us for more than 10 years, allowed us to create
dozens of knowledge-based expert systems. In this paper we present a
generalized three-step technology of extraction of explicit knowledge from
empirical data.Comment: 9 pages, The talk was given at the IJCNN '99 (Washington DC, July
1999
Suite of Meshless Algorithms for Accurate Computation of Soft Tissue Deformation for Surgical Simulation
The ability to predict patient-specific soft tissue deformations is key for
computer-integrated surgery systems and the core enabling technology for a new
era of personalized medicine. Element-Free Galerkin (EFG) methods are better
suited for solving soft tissue deformation problems than the finite element
method (FEM) due to their capability of handling large deformation while also
eliminating the necessity of creating a complex predefined mesh. Nevertheless,
meshless methods based on EFG formulation, exhibit three major limitations: i)
meshless shape functions using higher order basis cannot always be computed for
arbitrarily distributed nodes (irregular node placement is crucial for
facilitating automated discretization of complex geometries); ii) imposition of
the Essential Boundary Conditions (EBC) is not straightforward; and, iii)
numerical (Gauss) integration in space is not exact as meshless shape functions
are not polynomial. This paper presents a suite of Meshless Total Lagrangian
Explicit Dynamics (MTLED) algorithms incorporating a Modified Moving Least
Squares (MMLS) method for interpolating scattered data both for visualization
and for numerical computations of soft tissue deformation, a novel way of
imposing EBC for explicit time integration, and an adaptive numerical
integration procedure within the Meshless Total Lagrangian Explicit Dynamics
algorithm. The appropriateness and effectiveness of the proposed methods is
demonstrated using comparisons with the established non-linear procedures from
commercial finite element software ABAQUS and experiments with very large
deformations. To demonstrate the translational benefits of MTLED we also
present a realistic brain-shift computation.Comment: Accepted for publication in Medical Image Analysi
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