Biological neural systems must grow their own connections and maintain topological relations between elements that are related to the sensory input surface. Artificial systems have traditionally prewired such maps, but the sensor arrangement is not always known and can be expensive to specify before run time. Here we present a method for learning and updating topographic maps in systems comprising modular, event-based elements. Using an unsupervised neural spike-timing-based learning rule combined with Hebbian learning, our algorithm uses the spatiotemporal coherence of the external world to train its network. It improves on existing algorithms by not assuming a known topography of the target map and includes a novel method for automatically detecting edge elements. We show how, for stimuli that are small relative to the sensor resolution, the temporal learning window parameters can be determined without using any user-specified constants. For stimuli that are larger relative to the sensor resolution, we provide a parameter extraction method that generally outperforms the small-stimulus method but requires one user-specified constant. The algorithm was tested on real data from a 64 x 64-pixel section of an event-based temporal contrast silicon retina and a 360-tile tactile luminous floor. It learned 95.8% of the correct neighborhood relations for the silicon retina within about 400 seconds of real-world input from a driving scene and 98.1% correct for the sensory floor after about 160 minutes of human pedestrian traffic. Residual errors occurred in regions receiving little or ambiguous input, and the learned topological representations were able to update automatically in response to simulated damage. Our algorithm has applications in the design of modular autonomous systems in which the interfaces between components are learned during operation rather than at design time.

We extend the study of human-robot interaction into the area of large-scale, multi-user, robotic interactive environments. Using our experimental infrastructure – the interactive space Ada, an exhibit at the Swiss national expo in 2002 that received 553,700 visitors – we show that human movement is predictive of key attitudes towards a space and other humans, and that subjects’ behavior and attitudes are influenced by subtle modifications of environmental parameters. We also found several demographic effects on visitors’ opinions of interactive spaces. These findings enhance our quantitative understanding of collective human behavior in interactive spaces and are a first step towards the construction of active environments that can automatically influence human motion and experience. This knowledge will be important in the design and construction of future interactive environments for enhancing the safety and enjoyment of shared areas for large numbers of people.

A key question in the design of intelligent environments is how a space can influence the actions of its users, and how such behavior can be learned. We present the results of experiments conducted as part of the Ada project, an interactive entertainment exhibit deployed at the Swiss national exhibition Expo.02. We used a learning model called distributed adaptive control (DAC) that is based on the animal learning paradigms of classical and operant conditioning. DAC has been developed using mobile robots in foraging tasks. Here, it was applied to the learning of effective cues for guiding visitors in a given direction. Our results show that, by using this learning mechanism, Ada was able to influence the behavior of visitors by learning to deploy particular types of cues. Many visitors could be induced to move toward a region of the space that they normally avoided visiting-an effect that can be seen as a spatial classification of visitors into interactive and noninteractive categories. In our analysis, we also introduce a measure of human activity that combines different types of data to capture key aspects of human behavior in interactive spaces.

We present a biophysically constrained cerebellar model of classical conditioning, implemented using a neuromorphic analog VLSI (aVLSI) chip. Like its biological counterpart, our cerebellar model is able to control adaptive behavior by predicting the precise timing of events. Here we describe the functionality of the chip and present its learning performance, as evaluated in simulated conditioning experiments at the circuit level and in behavioral experiments using a mobile robot. We show that this aVLSI model supports the acquisition and extinction of adaptively timed conditioned responses under real-world conditions with ultra-low power consumption.

Ada is an entertainment exhibit that is able to interact with many people simultaneously, using a language of light and sound. "She " received 553,700 visitors over 5 months during the Swiss Expo.02 in 2002. In this paper we present the broad motivations, design and technologies behind Ada, and a first overview of the outcomes of the exhibit.

Despite immense progress in neuroscience, we remain restricted in our ability to construct autonomous behaving robots that match the competence of even simple animals. The barriers to the realisation of this goal include: the lack of knowledge of system integration issues, engineering limitations and organisational constraints common to many research laboratories. In this paper we describe our approach to addressing these issues by constructing an artificial organism within the framework of the Ada project - a large-scale public exhibit for the Swiss Expo.02 national exhibition.