In reverse chronological order:
POST-DIGITAL is a European H2020 Marie Curie Innovative Training Network (2020-2023) whose 15 partner institutions will provide academic and applied training opportunities to a cohort of 15 early stage researchers (ESRs) in neuromorphic computational technologies and their applications. A strong industrial presence in the network will provide our ESRs with the experience of practical applications and solutions beyond traditional digital methods. MINDS is leading the mathematical theory branch of research in this consortium.
MeM-scales (Memory technologies with multi-scale time constants for neuromorphic architectures) is a European H2020 ICT project (2020-2022) with a consortium of 9 academic and industrial partners. The mission is to build ultra-low power embedded and pervasive computing systems which realize core aspects of cognitive computing, in particular integrating information across several memory timescales. MINDS is leading the mathematical theory branch of research in this consortium.
Projects before 2020 (when the MINDS group was hosted in Jacobs University Bremen):
NeuRAM3 (Neural Computing Architectures in Advanced Monolithic 3D-VLSI Nano-Technologies) was a European H2020 Project (2016-2019) which developed 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 aimed at a low power consumption (reduction by a factor of 50 compared to current technology) and flexible configurability. MINDS developed novel neural learning architectures for robustness against noise, parameter drift, and low numerical accuracy.
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.
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.
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; the foundations for spectral optimization of OOM learning algorithms were laid; and OOM learning algorithms were extended to data with missing values (PhD thesis of Michael Thon).
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.
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.