Power spectra of the natural input to the visual system

AbstractThe efficient coding hypothesis posits that sensory systems are adapted to the regularities of their signal input so as to reduce redundancy in the resulting representations. It is therefore important to characterize the regularities of natural signals to gain insight into the processing of natural stimuli. While measurements of statistical regularity in vision have focused on photographic images of natural environments it has been much less investigated, how the specific imaging process embodied by the organism’s eye induces statistical dependencies on the natural input to the visual system. This has allowed using the convenient assumption that natural image data are homogeneous across the visual field. Here we give up on this assumption and show how the imaging process in a human model eye influences the local statistics of the natural input to the visual system across the entire visual field. Artificial scenes with three-dimensional edge elements were generated and the influence of the imaging projection onto the back of a spherical model eye were quantified. These distributions show a strong radial influence of the imaging process on the resulting edge statistics with increasing eccentricity from the model fovea. This influence is further quantified through computation of the second order intensity statistics as a function of eccentricity from the center of projection using samples from the dead leaves image model. Using data from a naturalistic virtual environment, which allows generation of correctly projected images onto the model eye across the entire field of view, we quantified the second order dependencies as function of the position in the visual field using a new generalized parameterization of the power spectra. Finally, we compared this analysis with a commonly used natural image database, the van Hateren database, and show good agreement within the small field of view available in these photographic images. We conclude by providing a detailed quantitative analysis of the second order statistical dependencies of the natural input to the visual system across the visual field and demonstrating the importance of considering the influence of the sensory system on the statistical regularities of the input to the visual system

Attractor Metadynamics in Adapting Neural Networks

Slow adaption processes, like synaptic and intrinsic plasticity, abound in the brain and shape the landscape for the neural dynamics occurring on substantially faster timescales. At any given time the network is characterized by a set of internal parameters, which are adapting continuously, albeit slowly. This set of parameters defines the number and the location of the respective adiabatic attractors. The slow evolution of network parameters hence induces an evolving attractor landscape, a process which we term attractor metadynamics. We study the nature of the metadynamics of the attractor landscape for several continuous-time autonomous model networks. We find both first- and second-order changes in the location of adiabatic attractors and argue that the study of the continuously evolving attractor landscape constitutes a powerful tool for understanding the overall development of the neural dynamics

Partial core power transformer

This thesis describes the design, construction, and testing of a 15kVA, 11kV/230V partial core power transformer (PCPT) for continuous operation. While applications for the partial core transformer have been developed for many years, the concept of constructing a partial core transformer, from conventional copper windings, as a power transformer has not been investigated, specifically to have a continuous operation. In this thesis, this concept has been investigated and tested. The first part of the research involved creating a computer program to model the physical dimensions and the electrical performance of a partial core transformer, based on the existing partial core transformer models. Also, since the hot-spot temperature is the key factor for limiting the power rating of the PCPT, the second part of the research investigates a thermal model to simulate the change of the hot-spot temperature for the designed PCPT. The cooling fluid of the PCPT applied in this project was BIOTEMP®. The original thermal model used was from the IEEE Guide for Loading Mineral-Oil-Immersed transformer. However, some changes to the original thermal model had to be made since the original model does not include BIOTEMP® as a type of cooling fluid. The constructed partial core transformer was tested to determine its hot-spot temperature when it is immersed by BIOTEMP®, and the results compared with the thermal model. The third part of the research involved using both the electrical model and the thermal model to design a PCPT. The PCPT was tested to obtain the actual electrical and the thermal performance for the PCPT. The overall performance of the PCPT was very close to the model estimation. However, cooling of the PCPT was not sufficient to allow the PCPT to operate at the design rated load for continuous operation. Therefore, the PCPT was down rated from 15kVA to maintain the hot-spot temperature at 100°C for continuous operation. The actual rating of the PCPT is 80% of the original power rating, which is 12kVA

Spike avalanches in vivo suggest a driven, slightly subcritical brain state

In self-organized critical (SOC) systems avalanche size distributions follow power-laws. Power-laws have also been observed for neural activity, and so it has been proposed that SOC underlies brain organization as well. Surprisingly, for spiking activity in vivo, evidence for SOC is still lacking. Therefore, we analyzed highly parallel spike recordings from awake rats and monkeys, anesthetized cats, and also local field potentials from humans. We compared these to spiking activity from two established critical models: the Bak-Tang-Wiesenfeld model, and a stochastic branching model. We found fundamental differences between the neural and the model activity. These differences could be overcome for both models through a combination of three modifications: (1) subsampling, (2) increasing the input to the model (this way eliminating the separation of time scales, which is fundamental to SOC and its avalanche definition), and (3) making the model slightly sub-critical. The match between the neural activity and the modified models held not only for the classical avalanche size distributions and estimated branching parameters, but also for two novel measures (mean avalanche size, and frequency of single spikes), and for the dependence of all these measures on the temporal bin size. Our results suggest that neural activity in vivo shows a mélange of avalanches, and not temporally separated ones, and that their global activity propagation can be approximated by the principle that one spike on average triggers a little less than one spike in the next step. This implies that neural activity does not reflect a SOC state but a slightly sub-critical regime without a separation of time scales. Potential advantages of this regime may be faster information processing, and a safety margin from super-criticality, which has been linked to epilepsy.DFG, 103586207, GRK 1589: Verarbeitung sensorischer Informationen in neuronalen SystemenBMBF, 01GQ1005B, Bernstein Zentrum für Computational Neuroscience, Göttingen - Kooperative Dynamiken und Adaptivität in neuronalen SystemenBMBF, 01GQ0742, Verbundprojekt Bernstein Partner: Gedächtnis-Netzwerk, Teilprojekt

Dynamic, Task-Related and Demand-Driven Scene Representation

Humans selectively process and store details about the vicinity based on their knowledge about the scene, the world and their current task. In doing so, only those pieces of information are extracted from the visual scene that is required for solving a given task. In this paper, we present a flexible system architecture along with a control mechanism that allows for a task-dependent representation of a visual scene. Contrary to existing approaches, our system is able to acquire information selectively according to the demands of the given task and based on the system’s knowledge. The proposed control mechanism decides which properties need to be extracted and how the independent processing modules should be combined, based on the knowledge stored in the system’s long-term memory. Additionally, it ensures that algorithmic dependencies between processing modules are resolved automatically, utilizing procedural knowledge which is also stored in the long-term memory. By evaluating a proof-of-concept implementation on a real-world table scene, we show that, while solving the given task, the amount of data processed and stored by the system is considerably lower compared to processing regimes used in state-of-the-art systems. Furthermore, our system only acquires and stores the minimal set of information that is relevant for solving the given task

Development of Gaze Following Abilities in Wolves (Canis Lupus)

The ability to coordinate with others' head and eye orientation to look in the same direction is considered a key step towards an understanding of others mental states like attention and intention. Here, we investigated the ontogeny and habituation patterns of gaze following into distant space and behind barriers in nine hand-raised wolves. We found that these wolves could use conspecific as well as human gaze cues even in the barrier task, which is thought to be more cognitively advanced than gazing into distant space. Moreover, while gaze following into distant space was already present at the age of 14 weeks and subjects did not habituate to repeated cues, gazing around a barrier developed considerably later and animals quickly habituated, supporting the hypothesis that different cognitive mechanisms may underlie the two gaze following modalities. More importantly, this study demonstrated that following another individuals' gaze around a barrier is not restricted to primates and corvids but is also present in canines, with remarkable between-group similarities in the ontogeny of this behaviour. This sheds new light on the evolutionary origins of and selective pressures on gaze following abilities as well as on the sensitivity of domestic dogs towards human communicative cues