11160456
Structure of Lateral Inhibition in an Olfactory Bulb Model
It has been shown that mutual lateral inhibition of the projection neurones in the olfactory bulb, mediated by interneurones, serves to tune the representation of odours in the bulb and reduce the overlap between similaz odorants. In this paper we demonstrate that the parameters of the lateral interaction, specifically the relation of synaptic strength to cell separation and the effective overall gain of the network, have a significant effect on the strength and range of lateral inhibition in a simple model of the olfactory bulb.
42776203
Fixed-Point Attractor Analysis for a Class of Neurodynamics
Nearly all models in neural networks start from the assumption that the input-output characteristic is a sigmoidal function. On parameter space, we present a systematic and feasible method for analyzing the whole spectrum of attractorsall-saturated, all-but-one-saturated, all-but-twosaturated, and so onof a neurodynamical system with a saturated sigmoidal function as its input-output characteristic. We present an argument that claims, under a mild condition, that only all-saturated or all but-one-saturated attractors are observable for the neurodynamics. For any given all-saturated configuration (all-but-one-saturated configuration ) the article shows how to construct an exact parameter region R()(()) such that if and only if the parameters fall within R()(()), then () is an attractor (a fixed point) of the dynamics. The parameter region for an all-saturated fixed-point attractor is independent of the specific choice of a saturated sigmoidal function, whereas for an all-but-one-saturated fixed point, it is sensitive to the input-output characteristic. Based on a similar idea, the role of weight normalization realized by a saturated sigmoidal function in competitive learning is discussed. A necessary and sufficient condition is provided to distinguish two kinds of competitive learning: stable competitive learning with the weight vectors representing extremes of input space and being fixed-point attractors, and unstable competitive learning. We apply our results to Linsker's model and (using extreme value theory in statistics) the Hopfield model and obtain some novel results on these two models.
1440521
Spike synchronization in a biophysically-detailed model of the olfactory bulb
Abstract Stimulus-evoked synchronization of action potentials has been demonstrated in mammalian olfactory bulb and in insect antennal lobes. Abolition of synchronization has been shown to impair the ability of honeybees to perform fine olfactory discrimination. We present a biophysically detailed computer model of the olfactory bulb which qualitatively reproduces many features seen in experimental recordings. The mitral cells of the model synchronize readily without common input due to lateral interactions with inhibitory granule cells. Weakly activated mitral cells fire more slowly than, but always synchronously with, strongly activated cells. Nearby cells synchronize more readily than widely separated ones.
9793653
Paradoxical Relationship between Output and Input Regularity for the FitzHugh-Nagumo Model
We examine the effects of changing the coefficient of variation (CV) of the inter-stimulus interval on the CV of the output interspike interval (ISI), using constant magnitude, supra-threshold point-process stimuli of the membrane potential variable in the FitzHugh-Nagumo model. The coefficient of variation of the input is changed within the context of a displaced exponential distribution. We find that for some values of mean inter-stimulus interval, CV of ISI has an inverse relationship with input coefficient of variation, whereas for other mean stimulation rates, CV of ISI increases with input coefficient of variation. Over a wide range of input regularity CV of ISI is approximately constant, and is less than 0.4.
206848726
Viewing a Class of Neurodynamics on Parameter Space
Nearly all models in neural networks start from the assumption that the input-output characteristic is a sigmoidal function. On parameter space we present a systematic and feasible method for analyzing the whole spectrum of attractors-all saturated, all-but-one saturated, all-but-two saturated, etc. — of a neurodynamical system with a saturated sigmoidal function as its input-output characteristic. We present an argument which claims, under a mild condition, that only all saturated or all-but-one saturated attractors are observable for the neurodynamics. For any given all saturated configuration ξ (all-but-one saturated configuration η) the paper shows how to construct an exact parameter region R(ξ) (¯R(η)) such that if and only if the parameters fall within R(ξ) (¯R(η)), then ξ (η) is an attractor (a fixed point) of the dynamics. The parameter region for an all saturated fixed point attractor is independent of the specific choice of a saturated sigmoidal function, whereas for an all-but-one saturated fixed point it is sensitive to the input-output characteristic.
14367385
Impact of Correlated Inputs on the Output of the Integrate-and-Fire Model
For the integrate-and-fire model with or without reversal potentials, we consider how correlated inputs affect the variability of cellular output. For both models, the variability of efferent spike trains measured by coefficient of variation (CV) of the interspike interval is a nondecreasing function of input correlation. When the correlation coefficient is greater than 0.09, the CV of the integrate-and-fire model without reversal potentials is always above 0.5, no matter how strong the inhibitory inputs. When the correlation coefficient is greater than 0.05, CV for the integrate- and-fire model with reversal potentials is always above 0.5, independent of the strength of the inhibitory inputs. Under a given condition on correlation coefficients, we find that correlated Poisson processes can be decomposed into independent Poisson processes. We also develop a novel method to estimate the distribution density of the first passage time of the integrate-and-fire model.
60537960
An orthodontic expert system
Abstract A Fuzzy Relational Inference Language, FRIL has been used to generate an expert system to help non-specialist dentists with orthodontic problems. The use of fuzzy relations, descriptors and numbers has been found useful in modelling the thought processes of orthodontic specialists.
9288911
The search for a new model structure of β-Factor XIIa
We present the search for a new model of β-factor XIIa, a blood coagulation enzyme, with an unknown experimental 3D-structure. We decided to build not one but three different models using different homologous proteins as well as different techniques and different modellers. Additional studies, including extensive molecular dynamics simulations on the solvated state, allowed us to draw several conclusions concerning homology modelling, in general, and β-factor XIIa, in particular.
37865441
Adapting robot kinematics for human-arm motion recognition
This paper presents a novel method to the analysis of human-arm motion, in particular improving the efficiency of conventional motion recognition algorithms. Contrary to the prior art methods, this research develops a framework for human-arm motion recognition where qualitative normalised templates (QNTs) is proposed to replace the conventional approaches. First of all, the conventional robotic model has been employed to build a generic vision model for a human-arm, that is we utilise the robot kinematics to construct a stick model. Secondly, the qualitative robotic model is adopted to learn and construct the QNTs where human-arm motion is termed as, whose execution is consistent and could be easily characterised by a definite space-time trajectory in configuration space. Finally, classification of the human-arm motion is achieved by comparing the QNTs to the parameters learnt with particle filter based motion tracking algorithm. Experimental evaluation has demonstrated the effectiveness of the proposed method in human-arm motion classification, and our future work is focused on extending the proposed method to recognise complex human motion, e.g. walking and running.
153803859
Digital State at the Leading Edge
The impact of information technology (IT) on government in the last five years has been profound. Using the governments of Canada and Ontario (both recognized as international leaders in the use of IT) as case studies, Digital State at the Leading Edge is the first attempt to take a comprehensive view of the influence of IT on the whole of government, including areas such as political campaigning, public consultation, service delivery, knowledge management, and procurement.Exploring the concepts of channel choice, procurement market analysis, organizational integration, and digital leadership, this study looks at the inter-relationships among various aspects of the application of IT to government and politics. The authors seek to understand how IT is transforming government and what the nature of that transformation is. In the process, they offer an explanation of Canada's relative success, and conclude with practical advice to politicians and public servants about how to manage IT in government more effectively. Based on original research undertaken over the last five years, the findings of this innovative study will be of particular interest to those studying or working in the fields of public administration, political science, and information technology.
35589713
Time Discretisation Applied to Anomaly Detection in a Marine Engine
An introduction to the problems associated with anomaly detection in a marine engine, explaining the benefits that the SAX representation brings to the field. Despite limitations in accuracy of the SAX representation in comparison with the normalised time series, we conclude that because of the reduction in data points that should be processed SAX should be considered further as a valid and efficient representation. Finally, a continuation of the work to make the approach more viable in the real world is briefly noted based upon Markov Chaining and Support Vector Machines.