Industrial handwriting recognition solutions. In a lively collaboration with PLANET intelligent systems GmbH, MINDS was regularly supported by PLANET through stipends for PhD students and student projects concerned with Echo State Networks algorithms for handwriting recognition.
Quadratic observable operator models. Funded by the DFG (contract JA 1210/1-1&2, 2005-2009), this project developed a number of statistically and computationally efficient learning algorithms for observable operator models (OOMs), and established a theory of quadratic OOMs. These describe stochastic processes by linear operators in an intriguing analogy to the formalism of quantum mechanics. The results were published in three articles in Neural Computation. Principal researcher: Mingjie Zhao.
Observable operator networks. Funded by the DFG (contract JA 1210/5-1, 2009-2012). A framework for unifying a number of predictive state based theories independently proposed in different disciplines was worked out ([JMRL paper](http://jmlr.org/papers/volume16/thon15a/thon15a.pdf)); the foundations for spectral optimization of OOM learning algorithms were laid; and OOM learning algorithms were extended to data with missing values ([PhD thesis](https://opus.jacobs-university.de/files/774/phd20171201_thon.pdf) of Michael Thon).
ORGANIC (Self-Organized Recurrent Neural Learning for Language Processing) was a European FP7 project (IST-231267, 2009-2012) whose mission was to establish neurodynamical architectures as viable alternative to statistical methods for speech and handwriting recognition. Coordinated by MINDS, it comprised six European research groups from reservoir computing, cognitive neuroscience, and speech and handwriting technology. MINDS contributed layered neural learning architectures based on Echo State Networks. Main contributors at MINDS: Mantas Lukoševičius, Vytenis Sakenas
AMARSi (Adaptive Modular Architectures for Rich Motor Skills) was a European Collaborative Project (IST-248311, 2010-2014) which aimed at a qualitative jump in robotic motor skills toward biological richness. Coordinated from the COR Lab at the University of Bielefeld, Amarsi joined 10 partners from robotics, neural computation and computational neuroscience, the motion sciences, and cognitive science. MINDS contributed models of neurocontrollers and led the architectures workpackage.
NeuRAM3 (Neural Computing Architectures in Advanced Monolithic 3D-VLSI Nano-Technologies) is a European H2020 Project (2016-2019) which develops a novel neuromorphic VLSI chip architecture and fabrication technology. Coordinated by the Commisariat à l’énergie atomique et aux énergies alternatives (CEA) and uniting 9 partners (among them gloablly leading chip manufacturers), the consortium aims at a low power consumption (reduction by a factor of 50 compared to current technology) and flexible configurability. MINDS develops novel neural learning architectures for robustness against noise, parameter drift, and low numerical accuracy. Main contributors at MINDS: Fatemeh Hadeaghi, Xu He.