CiteULike: yama_tah's library [320 articles]

Healthwear: medical technology becomes wearable

A Pentland — 2008-06-11T12:17:38-00:00

Computer, Vol. 37, No. 5. (2004), pp. 42-49.

Widespread adoption of sensors that monitor the wearer's vital signs and other indicators promises to improve care for the aged and chronically ill while amassing a database that can enhance treatment and reduce medical costs.

Odor concentration invariance by chemical ratio coding

Naoshige Uchida — 2008-06-10T13:58:59-00:00

Frontiers in Systems Neuroscience, Vol. 1 (2008), pp. 1-6.

Cortical control of a prosthetic arm for self-feeding

Meel Velliste — 2008-05-29T14:02:30-00:00

Nature (28 May 2008)

Perceptual and Neural Plasticity of Odor Quality Coding in the Human Brain

Jay Gottfried — 2008-05-29T06:47:50-00:00

Chemosensory Perception

Abstract Current neurobiological models of odor perception tend to emphasize the “bottom-up” contributions of odorant chemistry in determining the perceptual features of an odor. However, increasing research suggests that “top-down” effects related to learning and experience play equally important roles in human olfactory perception, implying that a given set of olfactory receptors activated by an odorant does not neatly map onto a given odor percept. Rather, odor perception may rely on more synthetic mechanisms subserved by higher order brain regions. This review article focuses on the modulatory effects of learning, context, and experience on human odor perception. Recent psychophysical and neuroimaging work from our laboratory indicates that sensory-specific information about odor quality is not static within human piriform and orbitofrontal cortices but can be rapidly updated by mere sensory exposure. This experience-dependent neural plasticity parallels behavioral improvements in odor perception, providing direct evidence for the role of learning in shaping neural representations of odor quality in the human brain.

Food and beverage quality assurance

Corrado Di Natale — 2008-05-28T10:08:57-00:00

Handbook of Machine Olfaction (2003), pp. 505-524.

Commercial electronic nose instruments

E Vanneste — 2008-05-23T12:45:00-00:00

Handbook of Machine Olfaction (2003), pp. 161-179.

Odor handling and delivery systems

Takamichi Nakamoto — 2008-05-23T12:35:35-00:00

Handbook of Machine Olfaction (2003), pp. 55-78.

Mixture segmentation and background suppression in chemosensor arrays with a model of olfactory bulb-cortex interaction

B Raman — 2008-05-12T14:14:28-00:00

Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005. IJCNN '05, Vol. 1 (2005), pp. 131-136.

We present a model of olfactory bulb-cortex interaction for the purpose of mixture processing with gas sensor arrays. The olfactory bulb is modeled with a neurodynamic model whose lateral inhibitory connections are learned through a modified Hebbian-anti-Hebbian rule. Bulbar outputs are then projected in a non-topographic fashion onto the olfactory cortex. Associational connections within cortex using Hebbian learning form a content addressable memory. Finally, inhibitory feedback from cortex is used to modulate bulbar activity. Depending on the form of feedback, Hebbian or anti-Hebbian, the model is able to perform background suppression or mixture segmentation. The model is validated on experimental data from a gas sensor array.

Integrating a projection-based olfactory display with interactive audio-visual contents

Yasuyuki Yanagida — 2008-05-10T04:43:08-00:00

HCI International 2005 (July 2005)

Khronos projector

Alvaro Cassinelli — 2007-09-26T14:11:54-00:00

SIGGRAPH '05: ACM SIGGRAPH 2005 Emerging technologies (2005)

Opportunities and obligations for physical computing systems

JA Stankovic — 2007-09-17T11:57:20-00:00

Computer, Vol. 38, No. 11. (2005), pp. 23-31.

The recent confluence of embedded and real-time systems with wireless, sensor, and networking technologies is creating a nascent infrastructure for a technical, economic, and social revolution. Based on the seamless integration of computing with the physical world via sensors and actuators, this revolution will accrue many benefits. Potentially, its impact could be similar to that of the current Internet. We believe developers must focus on the physical, real-time, and embedded aspects of pervasive computing. We refer to this domain as physical computing systems. For pervasive computing to achieve its promise, developers must create not only high-level system software and application solutions, but also low-level embedded systems solutions. To better understand physical computing's advantages, we consider three application areas: assisted living, emergency response systems for natural or man-made disasters, and protecting critical infrastructures at the national level.

PageRank for Product Image Search

Yushi Jing — 2008-05-03T03:07:19-00:00

WWW 2008 (21 April 2008)

Towards system software for physical space applications

Kaori Fujinami — 2008-05-01T10:02:47-00:00

SAC '05: Proceedings of the 2005 ACM symposium on Applied computing (2005), pp. 1613-1620.

Behavioral analysis of olfactory coding and computation in rodents.

ZF Mainen — 2008-04-27T02:14:39-00:00

Current opinion in neurobiology, Vol. 16, No. 4. (August 2006), pp. 429-434.

Behavioral analysis is essential to understand how the olfactory system transforms chemosensory signals into information that can be used to guide actions. Recent studies in rodents have begun to address the behavioral relevance of putative olfactory codes and computations including spatial maps, oscillatory synchrony, and evolving temporal codes. To date, these studies have failed to find support for a role of any of these mechanisms in odor discrimination. Progress calls for experiments using precise psychophysical methods in conjunction with neural recording or perturbation, in addition to ethologically minded exploration of more complex forms of odor-guided behavior.

Refinement of odor molecule tuning by dendrodendritic synaptic inhibition in the olfactory bulb.

M Yokoi — 2008-04-27T02:09:09-00:00

Proceedings of the National Academy of Sciences of the United States of America, Vol. 92, No. 8. (11 April 1995), pp. 3371-3375.

Mitral/tufted cells (M/T cells) and granule cells form reciprocal dendrodendritic synapses in the main olfactory bulb; the granule cell is excited by glutamate from the M/T cell and in turn inhibits M/T cells by gamma-aminobutyrate. The trans-synaptically excited granule cell is thought to induce lateral inhibition in neighboring M/T cells and to refine olfactory information. It remains, however, elusive how significantly and specifically this synaptic regulation contributes to the discrimination of different olfactory stimuli. This investigation concerns the mechanism of olfactory discrimination by single unit recordings of responses to a series of normal aliphatic aldehydes from individual rabbit M/T cells. This analysis revealed that inhibitory responses are evoked in a M/T cell by a defined subset of odor molecules with structures closely related to the excitatory odor molecules. Furthermore, blockade of the reciprocal synaptic transmission by the glutamate receptor antagonist or the gamma-aminobutyrate receptor antagonist markedly suppressed the odor-evoked inhibition, indicating that the inhibitory responses are evoked by lateral inhibition via the reciprocal synaptic transmission. The synaptic regulation in the olfactory bulb thus greatly enhances the tuning specificity of odor responses and would contribute to discrimination of olfactory information.

A Software Infrastructure for Wearable Sensor Networks

K Hanaoka — 2008-04-27T02:03:31-00:00

Embedded and Real-Time Computing Systems and Applications, 2006. Proceedings. 12th IEEE International Conference on (2006), pp. 27-35.

In ubiquitous computing environments, context-awareness is one of the most important research topics. Computers embedded in our surrounding can extract information about a user, and the information makes it possible to offer personalized services according to the user's preference. To extract a large amount of context information, wearable sensor devices become more important in the near future. However, it is not easy to develop context-aware services that use context information from wearable sensor devices because of the gap between low-level sensor information and high-level context that the services require. In this paper, we propose a software infrastructure for wearable sensor networks. We first discuss the requirements to retrieve context information from wearable sensor networks. Then, we introduce our software infrastructure named Cinnamon that extracts high-level context from low-level context information retrieved from wearable sensor networks. The software infrastructure makes it dramatically easy to develop context-aware services for wearable sensor networks. We present the design and implementation of Cinnamon, and discuss our current prototype implementation

Large-scale model of mammalian thalamocortical systems

Eugene Izhikevich — 2008-02-28T20:15:52-00:00

Proceedings of the National Academy of Sciences (21 February 2008), 0712231105.

The understanding of the structural and dynamic complexity of mammalian brains is greatly facilitated by computer simulations. We present here a detailed large-scale thalamocortical model based on experimental measures in several mammalian species. The model spans three anatomical scales. (i) It is based on global (white-matter) thalamocortical anatomy obtained by means of diffusion tensor imaging (DTI) of a human brain. (ii) It includes multiple thalamic nuclei and six-layered cortical microcircuitry based on in vitro labeling and three-dimensional reconstruction of single neurons of cat visual cortex. (iii) It has 22 basic types of neurons with appropriate laminar distribution of their branching dendritic trees. The model simulates one million multicompartmental spiking neurons calibrated to reproduce known types of responses recorded in vitro in rats. It has almost half a billion synapses with appropriate receptor kinetics, short-term plasticity, and long-term dendritic spike-timing-dependent synaptic plasticity (dendritic STDP). The model exhibits behavioral regimes of normal brain activity that were not explicitly built-in but emerged spontaneously as the result of interactions among anatomical and dynamic processes. We describe spontaneous activity, sensitivity to changes in individual neurons, emergence of waves and rhythms, and functional connectivity on different scales. 10.1073/pnas.0712231105

Neurodevelopmental involvement in schizophrenia: the olfactory epithelium as an alternative model for research

Cascella — 2007-07-14T03:03:17-00:00

Journal of Neurochemistry, Vol. 102, No. 3. (August 2007), pp. 587-594.

Development of an Infrared Absorption Spectroscope Based on Linear Variable Filters

FG Nogueira — 2007-07-16T05:30:17-00:00

Sensors Journal, IEEE, Vol. 7, No. 8. (2007), pp. 1183-1190.

<para> The objective of this research is to develop a low-cost infrared absorption spectroscope based on linear variable filter technology for the automated detection of concentrated gases and vapors, and the semiautomated detection of liquids. This instrument represents an alternative to electronic-nose devices based on cross-selective gas sensor arrays. Instead, the proposed instrument uses the concept of computational “pseudosensors,” whereby spectral lines in an analytical instrument are clustered into groups and used as independent variables. We characterize the system on a database of chemical mixtures, and evaluate it on two real-world applications in the foodstuffs domain: oil adulteration and trans-fatty acid detection. Our results show that the proposed system is a viable low-resolution, low-cost analytical technique for niche applications. </para>

Processing of chemical sensor arrays with a biologically inspired model of olfactory coding

B Raman — 2007-06-12T23:57:24-00:00

IEEE Transactions on Neural Networks, Vol. 17, No. 4. (2006), pp. 1015-1024.

This paper presents a computational model for chemical sensor arrays inspired by the first two stages in the olfactory pathway: distributed coding with olfactory receptor neurons and chemotopic convergence onto glomerular units. We propose a monotonic concentration-response model that maps conventional sensor-array inputs into a distributed activation pattern across a large population of neuroreceptors. Projection onto glomerular units in the olfactory bulb is then simulated with a self-organizing model of chemotopic convergence. The pattern recognition performance of the model is characterized using a database of odor patterns from an array of temperature modulated chemical sensors. The chemotopic code achieved by the proposed model is shown to improve the signal-to-noise ratio available at the sensor inputs while being consistent with results from neurobiology.

Increasing the separability of chemosensor array patterns with Hebbian/anti-Hebbian learning

A Gutierrez-Galvez — 2007-06-11T23:37:56-00:00

Sensors and Actuators B: Chemical, Vol. 116, No. 1-2. (28 July 2006), pp. 29-35.

The olfactory bulb is able to enhance the contrast between odor representations through a combination of excitatory and inhibitory circuits. Inspired by this mechanism, we propose a new Hebbian/anti-Hebbian learning rule to increase the separability of sensor-array patterns in a neurodynamics model of the olfactory system: the KIII. In the proposed learning rule, a Hebbian term is used to build associations within odors and an anti-Hebbian term is used to reduce correlated activity across odors. The KIII model with the new learning rule is characterized on synthetic data and validated on experimental data from an array of temperature-modulated metal-oxide sensors. Our results show that the performance of the model is comparable to that obtained with Linear Discriminant Analysis (LDA). Furthermore, the model is able to increase pattern separability for different concentrations of three odorants: allyl-alcohol, tert-butanol, and benzene, even though it is only trained with the gas sensor response to the highest concentration.

Contrast enhancement and background suppression of chemosensor array patterns with the KIII model: Research Articles

Agustin Gutierrez-Galvez — 2007-06-09T11:25:27-00:00

International Journal of Intelligent Systems, Vol. 21, No. 9. (September 2006), pp. 937-953.

Inspired by the ability of the olfactory bulb to enhance the contrast between odor representations, we propose a new hebbian learning rule that is able to increase the separability of odor patterns from gas sensor arrays. The proposed learning rule employs a hebbian term to build associations within odors and an anti-hebbian term to reduce correlated activity across odors. In addition to increasing the separability of patterns, the new learning rule can also achieve odor background suppression when combined with a habituation term. These two functions are demonstrated on Freeman's KIII, a neurodynamics model of the olfactory system. The system is first characterized on synthetic data, and also validated on experimental data from an array of chemical sensors exposed to organic solvents.

Concentration normalization with a model of gain control in the olfactory bulb

B Raman — 2007-06-07T23:33:48-00:00

Sensors and Actuators B: Chemical, Vol. 116, No. 1-2. (28 July 2006), pp. 36-42.

This article presents a biologically inspired model capable of removing concentration effects from the multivariate response of a gas sensor array. The model is based on the first stage of lateral inhibition in the olfactory bulb, which is mediated by periglomerular interneurons. To simulate inputs to the olfactory bulb, signals from a chemosensor array are first processed with a self-organizing model of chemotopic convergence proposed earlier, which leads to odor-specific spatial patterning. Subsequently, a shunting lateral inhibitory network, modeled after the role of periglomerular cells in the olfactory bulb, is used to compress concentration information. The model is validated using experimental data from an array of temperature-modulated metal-oxide chemoresistors.

Quantized Spiking Neuron With A/D Conversion Functions

H Hamanaka — 2007-06-06T08:33:17-00:00

IEEE Transactions on Circuits and Systems II: Express Briefs, Vol. 53, No. 10. (October 2006), pp. 1049-1053.

This brief presents a simple artificial spiking neuron and proposes its application to an A/D converter. Depending on the initial state, which is an analog input, the neuron can generate spike trains having various spike position patterns. Based on spike position modulation, the spike train can be symbolized by a digital output. As a result, the analog input can be encoded into the digital output. Adjusting a reconfigurable parameter, the neuron can realize various encodings such as binary and Gray encodings. This brief also proposes a simple reconfigurable implementation circuit and experimentally confirms typical A/D conversion functions

Late integration in audio-visual continuous speech recognition

A Verma — 2007-05-21T01:11:17-00:00

Automatic Speech Recognition and Understanding (1999)

Using visual information in speech recognition has been an area of interest because it can signi#cantly improve the speech recognition e#ciency in the conditions where audio only recognition su#ers due to noisy environment. In this paper, we present a new approachtocombine audio and video to improve the robustness of the speech recognition system in the noisy environments. We also compare the results of the new approach with the corresponding results of the approaches proposed earlier in the literature. Keywords: Late Integration, Viseme, PCA, LDA, etc.

Visual speech speeds up the neural processing of auditory speech.

V van Wassenhove — 2005-01-27T03:55:27-00:00

Proc Natl Acad Sci U S A, Vol. 102, No. 4. (25 January 2005), pp. 1181-1186.

Synchronous presentation of stimuli to the auditory and visual systems can modify the formation of a percept in either modality. For example, perception of auditory speech is improved when the speaker's facial articulatory movements are visible. Neural convergence onto multisensory sites exhibiting supra-additivity has been proposed as the principal mechanism for integration. Recent findings, however, have suggested that putative sensory-specific cortices are responsive to inputs presented through a different modality. Consequently, when and where audiovisual representations emerge remain unsettled. In combined psychophysical and electroencephalography experiments we show that visual speech speeds up the cortical processing of auditory signals early (within 100 ms of signal onset). The auditory-visual interaction is reflected as an articulator-specific temporal facilitation (as well as a nonspecific amplitude reduction). The latency facilitation systematically depends on the degree to which the visual signal predicts possible auditory targets. The observed auditory-visual data support the view that there exist abstract internal representations that constrain the analysis of subsequent speech inputs. This is evidence for the existence of an "analysis-by-synthesis" mechanism in auditory-visual speech perception.

Audio-Visual Speech Recognition Using Lip Information Extracted from Side-Face Images

Koji Iwano — 2007-05-04T05:03:14-00:00

EURASIP Journal on Audio, Speech, and Music Processing, Vol. 2007 (2007), 64506.

This paper proposes an audio-visual speech recognition method using lip information extracted from side-face images as an attempt to increase noise robustness in mobile environments. Our proposed method assumes that lip images can be captured using a small camera installed in a handset. Two different kinds of lip features, lip-contour geometric features and lip-motion velocity features, are used individually or jointly, in combination with audio features. Phoneme HMMs modeling the audio and visual features are built based on the multistream HMM technique. Experiments conducted using Japanese connected digit speech contaminated with white noise in various SNR conditions show effectiveness of the proposed method. Recognition accuracy is improved by using the visual information in all SNR conditions. These visual features were confirmed to be effective even when the audio HMM was adapted to noise by the MLLR method.

Quantifying social group evolution

Gergely Palla — 2007-04-04T18:39:56-00:00

Nature, Vol. 446, No. 7136. (5 April 2007), pp. 664-667.

The rich set of interactions between individuals in society results in complex community structure, capturing highly connected circles of friends, families or professional cliques in a social network. Thanks to frequent changes in the activity and communication patterns of individuals, the associated social and communication network is subject to constant evolution. Our knowledge of the mechanisms governing the underlying community dynamics is limited, but is essential for a deeper understanding of the development and self-optimization of society as a whole. We have developed an algorithm based on clique percolation that allows us to investigate the time dependence of overlapping communities on a large scale, and thus uncover basic relationships characterizing community evolution. Our focus is on networks capturing the collaboration between scientists and the calls between mobile phone users. We find that large groups persist for longer if they are capable of dynamically altering their membership, suggesting that an ability to change the group composition results in better adaptability. The behaviour of small groups displays the opposite tendency-the condition for stability is that their composition remains unchanged. We also show that knowledge of the time commitment of members to a given community can be used for estimating the community's lifetime. These findings offer insight into the fundamental differences between the dynamics of small groups and large institutions.

Evidence that cochlear-implanted deaf patients are better multisensory integrators.

J Rouger — 2007-04-04T15:24:33-00:00

Proc Natl Acad Sci U S A (2 April 2007)

The cochlear implant (CI) is a neuroprosthesis that allows profoundly deaf patients to recover speech intelligibility. This recovery goes through long-term adaptative processes to build coherent percepts from the coarse information delivered by the implant. Here we analyzed the longitudinal postimplantation evolution of word recognition in a large sample of CI users in unisensory (visual or auditory) and bisensory (visuoauditory) conditions. We found that, despite considerable recovery of auditory performance during the first year postimplantation, CI patients maintain a much higher level of word recognition in speechreading conditions compared with normally hearing subjects, even several years after implantation. Consequently, we show that CI users present higher visuoauditory performance when compared with normally hearing subjects with similar auditory stimuli. This better performance is not only due to greater speechreading performance, but, most importantly, also due to a greater capacity to integrate visual input with the distorted speech signal. Our results suggest that these behavioral changes in CI users might be mediated by a reorganization of the cortical network involved in speech recognition that favors a more specific involvement of visual areas. Furthermore, they provide crucial indications to guide the rehabilitation of CI patients by using visually oriented therapeutic strategies.

Lipreading Using Profile Versus Frontal Views

P Lucey — 2007-05-01T00:03:50-00:00

Multimedia Signal Processing, 2006 IEEE 8th Workshop on (2006), pp. 24-28.

Visual information from a speaker's mouth region is known to improve automatic speech recognition robustness. However, the vast majority of audio-visual automatic speech recognition (AVASR) studies assume frontal images of the speaker's face. In contrast, this paper investigates extracting visual speech information from the speaker's profile view, and, to our knowledge, constitutes the first real attempt to attack this problem. As with any AVASR system, the overall recognition performance depends heavily on the visual front end. This is especially the case with profile-view data, as the facial features are heavily compacted compared to the frontal scenario. In this paper, we particularly describe our visual front end approach, and report experiments on a multi-subject, small-vocabulary, bimodal, multisensory database that contains synchronously captured audio with frontal and profile face video. Our experiments show that AVASR is possible from profile views with moderate performance degradation compared to frontal video data.

Reliability, discriminability and stochastic synchronization of olfactory neurons

Roberto Galan — 2007-04-04T11:20:22-00:00

Sensors and Actuators B: Chemical, Vol. 116, No. 1-2. (28 July 2006), pp. 168-173.

Combining computer simulations and electrophysiological experiments we have studied the effect of noise on the responses of olfactory neurons. In particular, we first investigated the reliability of mitral cell responses and found, as previously observed in other neural systems, that, in the presence of background noise, mitral cells reliably respond to fast fluctuating inputs but not to constant stimuli. We then investigated a related property, input discriminability and a closely related phenomenon, stochastic synchronization that may account for the synchronous firing of mitral cells leading to network oscillations in the beta/gamma frequency range. We argue that these phenomena: reliability, discriminability and stochastic synchronization are not exclusive to neurons but are rather common to all devices with a resetting threshold. Therefore we suggest that an artificial nose with a hardware implementation of such devices may optimally operate with low signal-to-noise ratios.

Audiovisual speech processing

Tsuhan Chen — 2007-04-02T00:01:15-00:00

Signal Processing Magazine, IEEE, Vol. 18, No. 1. (2001), pp. 9-21.

We have reported activities in audiovisual speech processing, with emphasis on lip reading and lip synchronization. These research results have shown that, with lip reading, it is possible to enhance the reliability of audio speech recognition, which may result in a computer that can truly understand the user via hand-free natural spoken language even in a very noisy environments. Similarly, with lip synchronization, it is possible to render realistic talking heads with lip movements synchronized with the voice, which is very useful for human-computer interactions. We envision that in the near future, advancement in audiovisual speech processing will greatly increase the usability of computers. Once that happens, the cameras and the microphone may replace the keyboard and the mouse as better mechanisms for human-computer interaction

Statistical multimodal integration for audio-visual speech processing

S Nakamura — 2007-03-24T06:12:16-00:00

Neural Networks, IEEE Transactions on, Vol. 13, No. 4. (2002), pp. 854-866.

Sensory information is indispensable for living things. It is also important for living things to integrate multiple types of senses to understand their surroundings. In human communications, human beings must further integrate the multimodal senses of audition and vision to understand intention. In this paper, we describe speech related modalities since speech is the most important media to transmit human intention. To date, there have been a lot of studies concerning technologies in speech communications, but performance levels still have room for improvement. For instance, although speech recognition has achieved remarkable progress, the speech recognition performance still seriously degrades in acoustically adverse environments. On the other hand, perceptual research has proved the existence of the complementary integration of audio speech and visual face movements in human perception mechanisms. Such research has stimulated attempts to apply visual face information to speech recognition and synthesis. This paper introduces works on audio-visual speech recognition, speech to lip movement mapping for audio-visual speech synthesis, and audio-visual speech translation.

Neuromorphic Processing for Optical Microbead Arrays: Dimensionality Reduction and Contrast Enhancement

B Raman — 2007-03-18T02:49:04-00:00

Sensors Journal, IEEE, Vol. 7, No. 4. (2007), pp. 506-514.

<para> This paper presents a neuromorphic approach for sensor-based machine olfaction that combines a portable chemical detection system based on microbead array technology with a biologically inspired model of signal processing in the olfactory bulb. The sensor array contains hundreds of microbeads coated with solvatochromic dyes adsorbed in, or covalently attached on, the matrix of various microspheres. When exposed to odors, each bead sensor responds with corresponding intensity changes, spectral shifts, and time-dependent variations associated with the fluorescent sensors. The bead array responses are subsequently processed using a model of olfactory circuits that capture the following two functions: chemotopic convergence of receptor neurons and center on–off surround lateral interactions. The first circuit performs dimensionality reduction, transforming the high-dimensional microbead array response into an organized spatial pattern (i.e., an odor image). The second circuit enhances the contrast of these spatial patterns, improving the separability of odors. The model is validated on an experimental dataset containing the responses of a large array of microbead sensors to five different analytes. Our results indicate that the model is able to significantly improve the separability between odor patterns, compared to that available from the raw sensor response. </para>

Interglomerular Center-Surround Inhibition Shapes Odorant-Evoked Input to the Mouse Olfactory Bulb In Vivo

Dejan Vucinic — 2007-03-13T04:53:15-00:00

J Neurophysiol, Vol. 95, No. 3. (1 March 2006), pp. 1881-1887.

Mouse olfactory receptor proteins have relatively broad odorant tuning profiles, so single odorants typically activate a substantial subset of glomeruli in the main olfactory bulb, resulting in stereotyped odorant- and concentration-dependent glomerular input maps. One of the functions of the olfactory bulb may be to reduce the extent of this rather widespread activation before transmitting the information to higher olfactory centers. Two circuits have been studied in vitro that could perform center-surround inhibition in the olfactory bulb, one circuit acting between glomeruli, the other through the classical reciprocal synapses between the lateral dendrites of mitral cells and the dendrites of granule cells. One unanswered question from these in vitro measurements was how these circuits would affect the response to odorants in vivo. We made measurements of the odorant-evoked increase in calcium concentration in the olfactory receptor neuron terminals in the anesthetized mouse to evaluate the role of presynaptic inhibition in reshaping the input to the olfactory bulb. We compared the glomerular responses in 2- to 4-wk-old mice before and after suppressing presynaptic inhibition onto the receptor neuron terminals with the GABAB antagonist, CGP46381. We find that the input maps are modified by an apparent center-surround inhibition: strongly activated glomeruli appear to suppress the release from receptor neurons terminating in surrounding glomeruli. This form of lateral inhibition has the effect of increasing the contrast of the sensory input map. 10.1152/jn.00918.2005

Mining information from event-related recordings

NA Laskaris — 2007-03-13T02:13:24-00:00

Signal Processing Magazine, IEEE, Vol. 21, No. 3. (2004), pp. 66-77.

In this article we describe a signal-processing framework for mining information from event-related recordings. Pattern-analytic tools are combined with graph-theoretic techniques and signal understanding methodologies in a user-friendly environment with the scope of learning, parameterization, and representation of the ST data manifold. Through the first part, we provide a general outline of our methodological approach while trying to demonstrate all the different stages, where DM tools can be applied. In the second part, we provide a more detailed demonstration, give a synopsis of the obtained results and take the opportunity to underline the merits of the adopted algorithmic procedures. To enable the full justification of our framework, instead of just including a technical demonstration of some of the incorporated DM and KDD tools, we address the problem of response variability: an issue of great neuroscientific importance and the subject of continuous debate. The major question in all the previous studies was the validity of "signal plus noise" model, i.e., whether a stereotyped evoked response is linearly superimposed on the ongoing brain activity after every stimulus presentation, a prerequisite for the validity of ensemble-averaging. Using data from a simple visual experiment targeting at the early neuromagnetic response known as N70m, we try to bridge the gap between the "conservative-party" that suggests heavy averaging as the only way to study the brain's response and the "neurodynamics-party" that claims the averaged-signal has very little to say about how the real-time processing of an input from a sensory pathway is actually performed in the cortex.

Cultural differences reduce Japanese researchers' visibility on the Web

Masao Ito — 2006-12-14T05:18:50-00:00

Nature, Vol. 444, No. 7121. (13 December 2006), pp. 817-817.

Audio-visual speech recognition (AVSR Workshop 2000 Final Report)

Chalapathy Neti — 2006-11-05T03:01:47-00:00

(12 October 2000)

We have made signicant progress in automatic speech recognition (ASR) for well-dened applications like dictation and medium vocabulary transaction processing tasks in relatively controlled environments. However, for ASR to approach human levels of performance and for speech to become a truly pervasive user interface, we need novel, nontraditional approaches that have the potential of yielding dramatic ASR improvements. Visual speech is one such source for making large improvements in high noise environments with the potential of channel and task independence. It is not eected by the acoustic environment and noise, and it possibly contains the greatest amount of complementary information to the acoustic signal. In this workshop, our goal was to advance the state-of-the-art in ASR by demonstrating the use of visual information in addition to the traditional audio for large vocabulary continuous speech recognition (LVCSR). Starting with an appropriate audio-visual database, collected and provided by IBM, we demonstrated for the rst time that LVCSR performance can be improved by the use of visual information in the clean audio case. Specically, by conducting audio lattice rescoring experiments, we showed a 7% relative word error rate (WER) reduction in that condition. Furthermore, for the harder problem of speech contaminated by speech \babble" noise at 10 dB SNR, we demonstrated that recognition performance can be improved by 27% in relative WER reduction, compared to an equivalent audio-only recognizer matched to the noise environment. We believe that this paves the way to seriously address the challenge of speech recognition in high noise environments and to potentially achieve human levels of performance. In this report, we detail a number of approaches and experiments conducted during the summer workshop in the areas of visual feature extraction, hidden Markov model based visual-only recognition, and audio-visual information fusion. The later was our main concentration: In the workshop, a number of feature fusion as well as decision fusion techniques for audio-visual ASR were explored and compared.

Human-Inspired Robots

Silvia Coradeschi — 2006-09-24T23:41:40-00:00

IEEE Intelligent Systems, Vol. 21, No. 4. (July 2006), pp. 74-85.

A high-performance brainâcomputer interface

Gopal Santhanam — 2006-07-14T09:50:28-00:00

Nature, Vol. 442, No. 7099., pp. 195-198.

Bacteria classification using Cyranose 320 electronic nose.

R Dutta — 2006-09-11T00:21:54-00:00

Biomed Eng Online, Vol. 1 (16 October 2002)

BACKGROUND: An electronic nose (e-nose), the Cyrano Sciences' Cyranose 320, comprising an array of thirty-two polymer carbon black composite sensors has been used to identify six species of bacteria responsible for eye infections when present at a range of concentrations in saline solutions. Readings were taken from the headspace of the samples by manually introducing the portable e-nose system into a sterile glass containing a fixed volume of bacteria in suspension. Gathered data were a very complex mixture of different chemical compounds. METHOD: Linear Principal Component Analysis (PCA) method was able to classify four classes of bacteria out of six classes though in reality other two classes were not better evident from PCA analysis and we got 74% classification accuracy from PCA. An innovative data clustering approach was investigated for these bacteria data by combining the 3-dimensional scatter plot, Fuzzy C Means (FCM) and Self Organizing Map (SOM) network. Using these three data clustering algorithms simultaneously better 'classification' of six eye bacteria classes were represented. Then three supervised classifiers, namely Multi Layer Perceptron (MLP), Probabilistic Neural network (PNN) and Radial basis function network (RBF), were used to classify the six bacteria classes. RESULTS: A [6 x 1] SOM network gave 96% accuracy for bacteria classification which was best accuracy. A comparative evaluation of the classifiers was conducted for this application. The best results suggest that we are able to predict six classes of bacteria with up to 98% accuracy with the application of the RBF network. CONCLUSION: This type of bacteria data analysis and feature extraction is very difficult. But we can conclude that this combined use of three nonlinear methods can solve the feature extraction problem with very complex data and enhance the performance of Cyranose 320.

Twenty problems frequently found in English research papers authored by Japanese researchers

T Orr — 2006-09-07T12:36:37-00:00

Professional Communication Conference, 2004. IPCC 2004. Proceedings. International (2004), pp. 23-35.

This work describes the research and results of a project designed to identify significant problems in English research papers authored by Japanese researchers, for whom English is a foreign language. Research text written by Japanese students, faculty, and corporate researchers in a wide range of scientific and technical fields was obtained in draft or final form for analysis, along with input from research writing instructors and editors working in Japan, to discover persistent language problems and describe them for educational purposes. The problems identified were also compared with those commonly covered in writing handbook designed for native or normative speakers to evaluate the suitability of popular texts to address these problems. Results from the study provide useful and reliable information, for Japanese writers as well as for teachers and editors of Japanese writers, which greatly clarifies significant gaps in writer knowledge that will likely need to be addressed at advanced levels of English research writing instruction specifically tailored for Japanese researchers.

Adaptive Neural Coding Dependent on the Time-Varying Statistics of the Somatic Input Current

Jonghan Shin — 2006-08-25T02:33:30-00:00

Neural Comp., Vol. 11, No. 8. (15 November 1999), pp. 1893-1913.

It is generally assumed that nerve cells optimize their performance to reflect the statistics of their input. Electronic circuit analogs of neurons require similar methods of self-optimization for stable and autonomous operation. We here describe and demonstrate a biologically plausible adaptive algorithm that enables a neuron to adapt the current threshold and the slope (or gain) of its current-frequency relationship to match the mean (or dc offset) and variance (or dynamic range or contrast) of the time-varying somatic input current. The adaptation algorithm estimates the somatic current signal from the spike train by way of the intracellular somatic calcium concentration, thereby continuously adjusting the neurons' firing dynamics. This principle is shown to work in ananalog VLSI-designed silicon neuron.

Novel neural circuits based on stochastic pulse coding and noise feedback pulse coding

Jong Shin — 2006-08-25T00:24:30-00:00

International Journal of Electronics, Vol. 74, No. 3. (1993), pp. 359-368.

Two novel neural circuits based on stochastic pulse coding (SPC) and noise feedback pulse coding (NFPC) are presented. Artificial axon hillock circuits to realize these codings are compared with respect to coding efficiency. The axon hillock circuit using NFPC needs far fewer pulses than that using SPC to convert the analogue neuron body voltage into a pulse sequence under the same signal-to-noise ratio. This result suggests that the NFPC neural circuit can improve the speed of computation and communication compared with the SPC neural circuit. Simulation and experimental results are included.

Psychophysical and behavioral characteristics of olfactory adaptation.

P Dalton — 2006-08-08T02:46:24-00:00

Chem Senses, Vol. 25, No. 4. (August 2000), pp. 487-492.

Sensory adaptation allows organisms to reach behavioral equilibrium with the ambient environment and respond primarily to changes in stimulation. Given its functional significance, it is not surprising that adaptation in the olfactory system exhibits many of the same characteristics as adaptation in other sensory systems, including vision. Repeated or prolonged exposure to an odorant typically leads to stimulus-specific decreases in olfactory sensitivity to that odorant, but sensitivity recovers over time in the absence of further exposure. Psychophysical analysis shows that olfactory adaptation results in elevations in odor thresholds and in reduced responsiveness to suprathreshold stimulation. Further, the magnitude of the decrease and the time course of adaptation and recovery are dependent on the concentration of the odor and on the duration of exposure. It is generally agreed that olfactory adaptation can occur at multiple levels in the olfactory system and can involve both peripheral (receptor level) and more central (post-receptor) components. Evidence for peripheral and central involvement comes from studies showing that monorhinal stimulation results in adaptation in both the ipsilateral and contralateral nostril, although the degree of adaptation in the ipsilateral nostril is more profound and recovery is slower. Additional evidence for central involvement comes from studies that have found relatively small decreases in peripheral response following repeated stimulation despite substantial reductions in perceived intensity. Most psychophysical studies of adaptation, however, have not differentiated the peripheral and central processes. Although relatively few in number, studies of the parametric features of olfactory adaptation in both vertebrate (e.g. rat) and invertebrate (e.g. Drosophila, Caenorhabditis elegans) animal models appear to replicate the findings in psychophysical studies of adult humans. Despite the broad overall similarity of olfactory adaptation to adaptation in other sensory systems, olfactory adaptation exhibits some unique features. Adaptation in olfaction has been shown to be very long-lasting in some cases and may be modulated by the contribution of pre-neural events and physico-chemical properties of the odorant molecules that govern diffusion to receptor sites and post-receptor clearance.

Reducing the Dimensionality of Data with Neural Networks

GE Hinton — 2006-07-28T15:16:42-00:00

Science, Vol. 313, No. 5786. (28 July 2006), pp. 504-507.

High-dimensional data can be converted to low-dimensional codes by training a multilayer neural network with a small central layer to reconstruct high-dimensional input vectors. Gradient descent can be used for fine-tuning the weights in such "autoencoder" networks, but this works well only if the initial weights are close to a good solution. We describe an effective way of initializing the weights that allows deep autoencoder networks to learn low-dimensional codes that work much better than principal components analysis as a tool to reduce the dimensionality of data. 10.1126/science.1127647

Bulbocortical interplay in olfactory information processing via synchronous oscillations.

T Fukai — 2006-08-03T23:50:10-00:00

Biol Cybern, Vol. 74, No. 4. (April 1996), pp. 309-317.

Emergence of synchronous oscillatory activity is an inherent feature of the olfactory systems of insects, mollusks and mammals. A class of simple computational models of the mammalian olfactory system consisting of olfactory bulb and olfactory cortex is constructed to explore possible roles of the related neural circuitry in olfactory information processing via synchronous oscillations. In the models, the bulbar neural circuitry is represented by a chain of oscillators and that of cortex is analogous to an associative memory network with horizontal synaptic connections. The models incorporate the backprojection from cortical units to the bulbar oscillators in particular ways. They exhibit rapid and robust synchronous oscillations in the presence of odorant stimuli, while they show either nonoscillatory states or propagating waves in the absence of stimuli, depending on the values of model parameters. In both models, the backprojection is shown to enhance the establishment of large-scale synchrony. The results suggest that the modulation of neural activity through centrifugal inputs may play an important role at the early stage of cortical information processing.

Proceedings of the Symposium on Adaptation in Vision and Olfaction Held at the XXI Meeting of AChemS on April 15, 1999

Diego Restrepo — 2006-08-03T03:04:02-00:00

Chem. Senses, Vol. 25, No. 4. (1 August 2000), 471.

10.1093/chemse/25.4.471

Odour recognition and segmentation by a model olfactory bulb and cortex.

Z Li — 2006-07-26T08:11:31-00:00

Network, Vol. 11, No. 1. (February 2000), pp. 83-102.

We present a model of an olfactory system that performs odour segmentation. Based on the anatomy and physiology of natural olfactory systems, it consists of a pair of coupled modules, bulb and cortex. The bulb encodes the odour inputs as oscillating patterns. The cortex functions as an associative memory: when the input from the bulb matches a pattern stored in the connections between its units, the cortical units resonate in an oscillatory pattern characteristic of that odour. Further circuitry transforms this oscillatory signal to a slowly varying feedback to the bulb. This feedback implements olfactory segmentation by suppressing the bulbar response to the pre-existing odour, thereby allowing subsequent odours to be singled out for recognition.

Noise shaping in populations of coupled model neurons.

DJ Mar — 2006-07-21T01:49:07-00:00

Proc Natl Acad Sci U S A, Vol. 96, No. 18. (31 August 1999), pp. 10450-10455.

Biological information-processing systems, such as populations of sensory and motor neurons, may use correlations between the firings of individual elements to obtain lower noise levels and a systemwide performance improvement in the dynamic range or the signal-to-noise ratio. Here, we implement such correlations in networks of coupled integrate-and-fire neurons using inhibitory coupling and demonstrate that this can improve the system dynamic range and the signal-to-noise ratio in a population rate code. The improvement can surpass that expected for simple averaging of uncorrelated elements. A theory that predicts the resulting power spectrum is developed in terms of a stochastic point-process model in which the instantaneous population firing rate is modulated by the coupling between elements.