Spike-Triggered Covariance Analysis Reveals Phenomenological Diversity of Contrast Adaptation in the Retina

When visual contrast changes, retinal ganglion cells adapt by adjusting their sensitivity as well as their temporal filtering characteristics. The latter has classically been described by contrast-induced gain changes that depend on temporal frequency. Here, we explored a new perspective on contrast-induced changes in temporal filtering by using spike-triggered covariance analysis to extract multiple parallel temporal filters for individual ganglion cells. Based on multielectrode-array recordings from ganglion cells in the isolated salamander retina, we found that contrast adaptation of temporal filtering can largely be captured by contrast-invariant sets of filters with contrast-dependent weights. Moreover, differences among the ganglion cells in the filter sets and their contrast-dependent contributions allowed us to phenomenologically distinguish three types of filter changes. The first type is characterized by newly emerging features at higher contrast, which can be reproduced by computational models that contain response-triggered gain-control mechanisms. The second type follows from stronger adaptation in the Off pathway as compared to the On pathway in On-Off-type ganglion cells. Finally, we found that, in a subset of neurons, contrast-induced filter changes are governed by particularly strong spike-timing dynamics, in particular by pronounced stimulus-dependent latency shifts that can be observed in these cells. Together, our results show that the contrast dependence of temporal filtering in retinal ganglion cells has a multifaceted phenomenology and that a multi-filter analysis can provide a useful basis for capturing the underlying signal-processing dynamics

A sustainable system development method with applications

Wiryana IM. A sustainable system development method with applications. Bielefeld (Germany): Bielefeld University; 2009.Sustainable development is a development that meets the needs of present generation while not compromising the ability of future generations to also meet their needs. There are 2 cases of sustainability. Case-1 is due to the discrepancy of environment and Case-2 is due to the change of environment. User, usage context and cultural aspects play important roles in the sustainability of system. Different groups of user response differently to the same system. Understanding the cultural consequence contributes to better sustainability of a system. A sustainable system always attempts to maintain the Quality of Service (QoS) delivered by system. A service - a meaningful set of capabilities provided by an existing or intended set of system to all who utilize it: subscribers, end users, network providers, and service providers - each seeing a different perspective of service. The definition, parameters and technologies of QoS are determined by level of QoS domain. The level model is defined with respect to design approach used in the level. Those levels are: QoS in infrastructure, QoS in network level, Software quality as QoS, Usability as QoS, Culturability, Acceptability and Actability. The Lightweight Why-Because Analysis (LWBA) as an extension of Why-Because Analysis (WBA) is introduced as a tool for sustainable system development. LWBA is a semi-formal analysis, that investigates constraints in a non-judgmental manner. LWBA is also used to understand the needs of new development method which focus on sustainability. The analysis covers also non technical aspects, such as human resource, regulation and organization. LWBA is also employed as an investigative tool during the ethnography studies, a development tool in the proposed development method, and also as an explanation and a communication tool. A novel development named as Bandung Bandowoso System Development Method (BBSDM) is introduced. The name is referred to a particular class of project in an extreme condition with many constraints that is culturally widely accepted in Indonesia. This system development method is formalized by actively involving in the real development projects for public. The BBSDM is commenced by requirement elicitation and analysis which is triggered by Case-1 or Case-2 sustainability problems, as well as from examples provided by stakeholders. The next phase is service specification and also the organizational and learning strategy specification. Service refinement is employed during the implementation phase. Then, the service evaluation and sustainability analysis. The LWBA with the Lightweight Why Because Graph (LWBG) as the representation tool are used in the entire life cycle of BBSDM. As proof of the proposed development method, the development of real projects which employ BBSDM are described. Those projects are: Indonesia localization in Distro WinBI, the Air Putih in Tsunami relief action activities, the development of Early Warning Information System (EWIS) in Indonesia, and development as well as operation of critical web site of President of Republic of Indonesia, Dr. Susilo Bambang Yudhoyono. The systems are developed with many constraints and limitations in a very short time. By employing the BBSDM, the systems can sustain and still provide the expected services. The participatory action research is chosen to develop the BBSDM and by utilizing Internet as research tool to perform the ethnography study. The make use of LWBA as analytical tool in this research methodology is also a contribution as a new way in performing action research

Behavior acquisition in artificial agents

Thurau C. Behavior acquisition in artificial agents. Bielefeld (Germany): Bielefeld University; 2006.Computational skill acquisition in robots and simulated agents has been a topic of increasing popularity throughout the last years. Despite impressive progress, autonomous behavior at a level of animals and humans are not yet replicated by machines. Especially when a complex environment demands versatile, goal-oriented behavior, current artificial systems show shortcomings. Consider for instance modern 3D computer games. Despite their key role for more emersive game experience, surprisingly little effort was made towards new techniques for life-like behavior creation in artificial characters. Modern interactive computer games provide the ability to objectively record complex human behavior, offering a variety of interesting challenges to the pattern recognition community. Such recordings often represent a multiplexing of long-term strategy, mid-term tactics and short-term reactions, in addition to the more low-level details of the player's movements. In this work we approach the goal of behavior acquisition in artifical game agents using methods from machine learning and pattern recognition. Recordings of human players implicitly encode the behaviors we are looking for and consequently serve as a training sample base. We assume the behavior of game characters to be a function that maps the current game state onto a reaction. Since a global approach for imitating human behavior is not feasible, various methods are applied to extract and mimic behaviors operating on different timescales (long-term, mid-term, and short-term). For example Bayesian approaches map the world-state onto a discrete set of movement prototypes to imitate situative action sequences, or a Mixture of Experts architecture is used for learning (mid-term) behavioral functions. Although the focus is on the acquisition of single behaviors, first approaches on integrating behaviors are elaborated as well. For experimental validation we use the popular computer game Quake II. The experimental results show that human behavior in simulated environments can indeed be learned from scratch using supervised learning. In final experiments we compare learned behaviors to a human player, and to the behavior of a conventional artificial game agent. A Turing-Test like survey indicates that the learned behaviors are in most cases perceived as humanlike

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