Artificial Development by Reinforcement Learning Can Benefit From Multiple Motivations

Research on artificial development, reinforcement learning, and intrinsic motivations like curiosity could profit from the recently developed framework of multi-objective reinforcement learning. The combination of these ideas may lead to more realistic artificial models for life-long learning and goal directed behavior in animals and humans

Bringing Anatomical Information into Neuronal Network Models

For constructing neuronal network models computational neuroscientists have access to wide-ranging anatomical data that nevertheless tend to cover only a fraction of the parameters to be determined. Finding and interpreting the most relevant data, estimating missing values, and combining the data and estimates from various sources into a coherent whole is a daunting task. With this chapter we aim to provide guidance to modelers by describing the main types of anatomical data that may be useful for informing neuronal network models. We further discuss aspects of the underlying experimental techniques relevant to the interpretation of the data, list particularly comprehensive data sets, and describe methods for filling in the gaps in the experimental data. Such methods of `predictive connectomics' estimate connectivity where the data are lacking based on statistical relationships with known quantities. It is instructive, and in certain cases necessary, to use organizational principles that link the plethora of data within a unifying framework where regularities of brain structure can be exploited to inform computational models. In addition, we touch upon the most prominent features of brain organization that are likely to influence predicted neuronal network dynamics, with a focus on the mammalian cerebral cortex. Given the still existing need for modelers to navigate a complex data landscape full of holes and stumbling blocks, it is vital that the field of neuroanatomy is moving toward increasingly systematic data collection, representation, and publication

Efficient Adaptation of Structure Metrics in Prototype-Based Classification

Mokbel B, Paaßen B, Hammer B. Efficient Adaptation of Structure Metrics in Prototype-Based Classification. In: Wermter S, Weber C, Duch W, et al., eds. Artificial Neural Networks and Machine Learning - ICANN 2014 - 24th International Conference on Artificial Neural Networks, Hamburg, Germany, September 15-19, 2014. Proceedings. Lecture Notes in Computer Science. Vol 8681. Springer; 2014: 571-578.More complex data formats and dedicated structure metrics have spurred the development of intuitive machine learning techniques which directly deal with dissimilarity data, such as relational learning vector quantization (RLVQ). The adjustment of metric parameters like relevance weights for basic structural elements constitutes a crucial issue therein, and first methods to automatically learn metric parameters from given data were proposed recently. In this contribution, we investigate a robust learning scheme to adapt metric parameters such as the scoring matrix in sequence alignment in conjunction with prototype learning, and we investigate the suitability of efficient approximations thereof

Towards a Reliable and Rapid Automated Grading System in Facial Palsy Patients: Facial Palsy Surgery Meets Computer Science

Background: Reliable, time- and cost-effective, and clinician-friendly diagnostic tools are cornerstones in facial palsy (FP) patient management. Different automated FP grading systems have been developed but revealed persisting downsides such as insufficient accuracy and cost-intensive hardware. We aimed to overcome these barriers and programmed an automated grading system for FP patients utilizing the House and Brackmann scale (HBS). Methods: Image datasets of 86 patients seen at the Department of Plastic, Hand, and Reconstructive Surgery at the University Hospital Regensburg, Germany, between June 2017 and May 2021, were used to train the neural network and evaluate its accuracy. Nine facial poses per patient were analyzed by the algorithm. Results: The algorithm showed an accuracy of 100%. Oversampling did not result in altered outcomes, while the direct form displayed superior accuracy levels when compared to the modular classification form (n = 86; 100% vs. 99%). The Early Fusion technique was linked to improved accuracy outcomes in comparison to the Late Fusion and sequential method (n = 86; 100% vs. 96% vs. 97%). Conclusions: Our automated FP grading system combines high-level accuracy with cost- and time-effectiveness. Our algorithm may accelerate the grading process in FP patients and facilitate the FP surgeon’s workflow