Dr Inden is a Lecturer in Computing. He is currently module leader for Artificial Intelligence (ISYS30221, undergraduate level 6) and Applied Artificial Intelligence (COMP40511, postgraduate level 7), and contributes to teaching Python and C++ programming.
Dr Inden was a research assistant at the Max Planck Institute for Mathematics in the Sciences (Leipzig, Germany) from 2003 to 2007, and received his PhD from the University of Leipzig in 2007 . From 2008 to 2014, he worked as a postdoctoral researcher at Bielefeld University (Germany) From 2014 to 2016, he was a lecturer at the University of Bedfordshire (UK). He joined Nottingham Trent University in September 2016.
Next to his degrees in Computer Science, he also holds a degree in Life Sciences from the Open University (UK), and is a Fellow of the Higher Education Academy.
Dr Inden is a member of the Computational Intelligence and Applications Group (CIA). His research interests include evolving artificial neural networks for controlling autonomous agents, in particular:
- Evolution of locomotion and navigation strategies
- Pattern recognition and multisensory integration in evolving neural networks
- Genetic representations for large and partly regular neural networks
- Intelligent selection techniques
- Conditions that lead to sustained increases in the complexity of agent behaviours
- Creation of abstract individual based models of coevolution with a particular emphasis on examining complexification
- Robots that interact with animal and plant populations
- Modeling entrainment between speakers and listeners by using oscillator models and neural networks, transfer of models to and evaluation with embodied conversational agents
- Using genetic programming to learn compositional rules of music
Opportunities to carry out postgraduate research towards an MPhil / PhD exist and further information may be obtained from the NTU Doctoral School.
Dr Inden has been a member of the program committee for Robotics: Science and Systems (2015), and a reviewer for various journals including Soft Computing, Theory in Biosciences, IEEE Transactions on Neural Networks, IEEE Transactions on Evolutionary Computation, IEEE Transactions on Cybernetics, and Neurocomputing.
Inden, B. & Jost, J. 2015 “Effects of Several Bioinspired Methods on the Stability of Coevolutionary Complexification”, in: Proceedings of the 2015 IEEE Symposium Series on Computational Intelligence.
Inden, B., Malisz, Z., Wagner, P., & Wachsmuth, I. 2013. “Timing and entrainment of multimodal backchanneling behavior for an embodied conversational agent”, in: J. Epps, F. Chen, S. Oviatt, K. Mase, A. Sears, K. Jokinen, et al. (Eds), Proceedings of the 15th International Conference on Multimodal Interaction, ICMI'13 - Sydney. New York: ACM.
Inden, B., & Jost, J. 2013. “Neural agents can evolve to reproduce sequences of arbitrary length”, in: P. Liò, O. Miglino, G. Nicosia, S. Nolfi, & M. Pavone (Eds), Advances in Artificial Life, ECAL 2013: Proceedings of the Twelfth European Conference on the Synthesis and Simulation of Living Systems. Cambridge (MA, USA): The MIT Press.
Inden, B., Y. Jin, R. Haschke, H. Ritter, & B. Sendhoff. 2013. “An examination of different fitness and novelty based selection methods for the evolution of neural networks” Soft Computing, 17(5), 753–767.
Inden, B., Z. Malisz, P. Wagner, & I. Wachsmuth. 2012. “Rapid entrainment to spontaneous speech: A comparison of oscillator models”, in: N. Miyake, D. Peebles, & R.P. Cooper (Eds.), Proceedings of the 34th Annual Conference of the Cognitive Science Society. Austin, TX: Cognitive Science Society.
Inden, B. 2012. “Open-ended Coevolution and the Emergence of Complex Irreducible Functional Units in Iterated Number Sequence Games”, in: Proceedings of the 14th annual conference on genetic and evolutionary computation. New York, NY, USA: ACM, 113 - 200.
Inden, B., Y. Jin, R. Haschke, & H. Ritter. 2012. “Evolving neural fields for problems with large input and output spaces”. Neural Networks, 28, 24 - 39.
Inden, B., Y. Jin, R. Haschke, & H. Ritter. 2011. “Evolution of multisensory integration in large neural fields”, in: Proceedings of the Tenth International Conference on Artificial Evolution, Evolution Artificielle. Lecture Notes in Computer Science, 7401. Springer-Verlag, 181 - 192.