Neural network models of sensory integration for improved vowelrecognition

It is demonstrated that multiple sources of speech information can be integrated at a subsymbolic level to improve vowel recognition. Feedforward and recurrent neural networks are trained to estimate the acoustic characteristics of a vocal tract from images of the speaker's mouth. These estimates are then combined with the noise-degraded acoustic information, effectively increasing the signal-to-noise ratio and improving the recognition of these noise-degraded signals. Alternative symbolic strategies such as direct categorization of the visual signals into vowels are also presented. The performances of these neural networks compare favorably with human performance and with other pattern-matching and estimation techniques     

The once and future Hebb synapse.

Suggests that the Hebb synapse has become better known than D. Hebb himself. In this respect he has joined an exclusive club along with the Ising model in condensed matter physics and Parkinson's disease in medicine. This is not to say that Hebb has not made other important contributions, as P. Milner (see record 2003-01537-001) and B. Kolb (see record 2003-01537-003) document, but the Hebb synapse has eclipsed these other achievements. The goal of this essay is to examine how this happened. The Hebb synapse remains a vital organizing concept for both experimental studies and theoretical analysis, as G. Hinton emphasizes (see record 2003-01537-002). (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Classifying Facial Actions

The Facial Action Coding System (FACS) [23] is an objective method for quantifying facial movement in terms of component actions. This system is widely used in behavioral investigations of emotion, cognitive processes, and social interaction. The coding is presently performed by highly trained human experts. This paper explores and compares techniques for automatically recognizing facial actions in sequences of images. These techniques include analysis of facial motion through estimation of optical flow; holistic spatial analysis, such as principal component analysis, independent component analysis, local feature analysis, and linear discriminant analysis; and methods based on the outputs of local filters, such as Gabor wavelet representations and local principal components. Performance of these systems is compared to naive and expert human subjects. Best performances were obtained using the Gabor wavelet representation and the independent component representation, both of which achieved 96 percent accuracy for classifying 12 facial actions of the upper and lower face. The results provide converging evidence for the importance of using local filters, high spatial frequencies, and statistical independence for classifying facial actions.     

ICA mixture models for unsupervised classification of non-Gaussian classes and automatic context switching in blind signal separation

An unsupervised classification algorithm is derived by modeling observed data as a mixture of several mutually exclusive classes that are each described by linear combinations of independent, non-Gaussian densities. The algorithm estimates the density of each class and is able to model class distributions with non-Gaussian structure. The new algorithm can improve classification accuracy compared with standard Gaussian mixture models. When applied to blind source separation in nonstationary environments, the method can switch automatically between classes, which correspond to contexts with different mixing properties. The algorithm can learn efficient codes for images containing both natural scenes and text. This method shows promise for modeling non-Gaussian structure in high-dimensional data and has many potential applications.     

Finding the event structure of neuronal spike trains

Neurons in sensory systems convey information about physical stimuli in their spike trains. In vitro, single neurons respond precisely and reliably to the repeated injection of the same fluctuating current, producing regions of elevated firing rate, termed events. Analysis of these spike trains reveals that multiple distinct spike patterns can be identified as trial-to-trial correlations between spike times (Fellous, Tiesinga, Thomas, & Sejnowski, 2004 ). Finding events in data with realistic spiking statistics is challenging because events belonging to different spike patterns may overlap. We propose a method for finding spiking events that uses contextual information to disambiguate which pattern a trial belongs to. The procedure can be applied to spike trains of the same neuron across multiple trials to detect and separate responses obtained during different brain states. The procedure can also be applied to spike trains from multiple simultaneously recorded neurons in order to identify volleys of near-synchronous activity or to distinguish between excitatory and inhibitory neurons. The procedure was tested using artificial data as well as recordings in vitro in response to fluctuating current waveforms.     

Irregular firing of isolated cortical interneurons in vitro driven by intrinsic stochastic mechanisms

Pharmacologically isolated GABAergic irregular spiking and stuttering interneurons in the mouse visual cortex display highly irregular spike times, with high coefficients of variation approximately 0.9-3, in response to a depolarizing, constant current input. This is in marked contrast to cortical pyramidal cells, which spike quite regularly in response to the same current injection. We applied time-series analysis methods to show that the irregular behavior of the interneurons was not a consequence of low-dimensional, deterministic processes. These methods were also applied to the Hindmarsh and Rose neuronal model to confirm that the methods are adequate for the types of data under investigation. This result has important consequences for the origin of fluctuations observed in the cortex in vivo.     

A new view of hemineglect based on the response properties of parietal neurones

Lesion studies of the parietal cortex have led to a wide range of conclusions regarding the coordinate reference frame in which hemineglect is expressed. A model of spatial representation in the parietal cortex has recently been developed in which the position of an object is not encoded in a particular frame of reference, but instead involves neurones computing basis functions of sensory inputs. In this type of representation, a nonlinear sensorimotor transformation of an object is represented in a population of units having the response properties of neurones that are observed in the parietal cortex. A simulated lesion in a basis-function representation was found to replicate three of the most important aspects of hemineglect: (i) the model behaved like parietal patients in line-cancellation and line-bisection experiments; (ii) the deficit affected multiple frames of reference; and (iii) the deficit could be object-centred. These results support the basis-function hypothesis for spatial representations and provide a testable computational theory of hemineglect at the level of single cells.