26,052 research outputs found
From Parallel Sequence Representations to Calligraphic Control: A Conspiracy of Neural Circuits
Calligraphic writing presents a rich set of challenges to the human movement control system. These challenges include: initial learning, and recall from memory, of prescribed stroke sequences; critical timing of stroke onsets and durations; fine control of grip and contact forces; and letter-form invariance under voluntary size scaling, which entails fine control of stroke direction and amplitude during recruitment and derecruitment of musculoskeletal degrees of freedom. Experimental and computational studies in behavioral neuroscience have made rapid progress toward explaining the learning, planning and contTOl exercised in tasks that share features with calligraphic writing and drawing. This article summarizes computational neuroscience models and related neurobiological data that reveal critical operations spanning from parallel sequence representations to fine force control. Part one addresses stroke sequencing. It treats competitive queuing (CQ) models of sequence representation, performance, learning, and recall. Part two addresses letter size scaling and motor equivalence. It treats cursive handwriting models together with models in which sensory-motor tmnsformations are performed by circuits that learn inverse differential kinematic mappings. Part three addresses fine-grained control of timing and transient forces, by treating circuit models that learn to solve inverse dynamics problems.National Institutes of Health (R01 DC02852
Spatial Filtering Pipeline Evaluation of Cortically Coupled Computer Vision System for Rapid Serial Visual Presentation
Rapid Serial Visual Presentation (RSVP) is a paradigm that supports the
application of cortically coupled computer vision to rapid image search. In
RSVP, images are presented to participants in a rapid serial sequence which can
evoke Event-related Potentials (ERPs) detectable in their Electroencephalogram
(EEG). The contemporary approach to this problem involves supervised spatial
filtering techniques which are applied for the purposes of enhancing the
discriminative information in the EEG data. In this paper we make two primary
contributions to that field: 1) We propose a novel spatial filtering method
which we call the Multiple Time Window LDA Beamformer (MTWLB) method; 2) we
provide a comprehensive comparison of nine spatial filtering pipelines using
three spatial filtering schemes namely, MTWLB, xDAWN, Common Spatial Pattern
(CSP) and three linear classification methods Linear Discriminant Analysis
(LDA), Bayesian Linear Regression (BLR) and Logistic Regression (LR). Three
pipelines without spatial filtering are used as baseline comparison. The Area
Under Curve (AUC) is used as an evaluation metric in this paper. The results
reveal that MTWLB and xDAWN spatial filtering techniques enhance the
classification performance of the pipeline but CSP does not. The results also
support the conclusion that LR can be effective for RSVP based BCI if
discriminative features are available
A Scalable Model of Cerebellar Adaptive Timing and Sequencing: The Recurrent Slide and Latch (RSL) Model
From the dawn of modern neural network theory, the mammalian cerebellum has been a favored object of mathematical modeling studies. Early studies focused on the fan-out, convergence, thresholding, and learned weighting of perceptual-motor signals within the cerebellar cortex. This led in the proposals of Albus (1971; 1975) and Marr (1969) to the still viable idea that the granule cell stage in the cerebellar cortex performs a sparse expansive recoding of the time-varying input vector. This recoding reveals and emphasizes combinations (of input state variables) in a distributed representation that serves as a basis for the learned, state-dependent control actions engendered by cerebellar outputs to movement related centers. Although well-grounded as such, this perspective seriously underestimates the intelligence of the cerebellar cortex. Context and state information arises asynchronously due to the heterogeneity of sources that contribute signals to compose the cerebellar input vector. These sources include radically different sensory systems - vision, kinesthesia, touch, balance and audition - as well as many stages of the motor output channel. To make optimal use of available signals, the cerebellum must be able to sift the evolving state representation for the most reliable predictors of the need for control actions, and to use those predictors even if they appear only transiently and well in advance of the optimal time for initiating the control action. Such a cerebellar adaptive timing competence has recently been experimentally verified (Perrett, Ruiz, & Mauk, 1993). This paper proposes a modification to prior, population, models for cerebellar adaptive timing and sequencing. Since it replaces a population with a single clement, the proposed Recurrent Slide and Latch (RSL) model is in one sense maximally efficient, and therefore optimal from the perspective of scalability.Defense Advanced Research Projects Agency and the Office of Naval Research (N00014-92-J-1309, N00014-93-1-1364, N00014-95-1-0409)
RoboJam: A Musical Mixture Density Network for Collaborative Touchscreen Interaction
RoboJam is a machine-learning system for generating music that assists users
of a touchscreen music app by performing responses to their short
improvisations. This system uses a recurrent artificial neural network to
generate sequences of touchscreen interactions and absolute timings, rather
than high-level musical notes. To accomplish this, RoboJam's network uses a
mixture density layer to predict appropriate touch interaction locations in
space and time. In this paper, we describe the design and implementation of
RoboJam's network and how it has been integrated into a touchscreen music app.
A preliminary evaluation analyses the system in terms of training, musical
generation and user interaction
- …