Technical Cognition: Related research
The Frankfurt Vision Initiative is centered on visual perception, one of the most important and most promising areas of computational neuroscience and neurotechnology. The central goal of the initiative is to understand the principles linking the neural and the cognitive level of brain and mind and to apply and exploit these principles for the development of new cognitive vision technology.
Our initiative involves substantial efforts in the area of massive computing, including the development of specific new hardware inspired by biological and neural principles. Specifically, the results of the project are expected to provide significant technological advances to the areas of sensor systems for robots, for surround sensors in driver assistance systems, vision-based security systems, traffic control and surveillance, and in many other areas where perception tasks in complex visual environments have to be solved.
The vision of the CITEC scientists are technical systems that can be operated easily and intuitively, ranging from everyday objects to fully-blown humanoid robots. The future technology should adapt itself to its human users instead of forcing us humans to adjust to the often cumbersome operation of the current equipment.
The Boston University Department of Cognitive and Neural Systems (CNS) and the CNS Technology Lab serve as the home base for neural technology research and development. CNS research and training programs address two broad questions: How does the brain control behavior? How can technology emulate biological intelligence?
The first question is a modern form of the Mind/Body problem. The second question supports the development of intelligent technologies that are well suited to human societies. These two scientific and technological foci are symbiotic because brains are unparalleled in their ability to adapt intelligently to complex and novel environments.
Cognitive capabilities such as perception, reasoning, learning, and planning turn technical systems into systems that "know what they are doing". Starting from the human brain the Cluster of Excellence "CoTeSys" investigates cognition for technical systems such as vehicles, robots, and factories. Technical systems that are cognitive will be much easier to interact and cooperate with, and will be more robust, flexible, and efficient.
The "Electronic Vision(s) Group" at the "Kirchhoff-Institut für Physik" was founded in 1995. During the last years, the research focus has shifted from sensors to information processing. Evolutionary algorithms have been used to configure analog circuits in flexible transistor arrays as well as artificial neural network chips, both developed and built by the group. New ideas from neurobiology have recently given the neural network research strong impulses. The computing with transient states, also known as 'liquid computing', as well as contemporary ideas of synaptic plasticity have generated new interest in the realization of hardware models of neural circuitry.
The goal of the FACETS project is to create a theoretical and experimental foundation for the realization of novel computing paradigms which exploit the concepts experimentally observed in biological nervous systems. The continuous interaction and scientific exchange between biological experiments, computer modelling and hardware emulations within the project provides a unique research infrastructure that will in turn provide an improved insight into the computing principles of the brain.
MIIND is a highly modular multi-level C++ framework, that aims to shorten the development time for models in Cognitive Neuroscience (CNS). It offers reusable code modules (libraries of classes and functions) aimed at solving problems that occur repeatedly in modelling, but tries not to impose a specific modelling philosophy or methodology.
The vast majority of current-generation computing devices are based on the Von Neumann architecture. Although wonderfully generic and multi-purpose, the Von Neumann architecture comes with a deep, fundamental limit. It cannot execute a program faster than it can fetch instructions and data from memory. This limit is know as the “Von Neumann bottleneck.”
The exponential increase in clock speed allowed chips to grow exponentially faster without addressing the Von Neumann bottleneck at all. As anyone who has purchased a computer in the last few years can attest, though, this exponential growth has already stopped. Beyond a clock speed of a few gigahertz, processors dissipate too much power to use economically.
The vision for the Systems of Neuromorphic Adaptive Plastic Scalable Electronics (SyNAPSE) program is to develop electronic neuromorphic machine technology that scales to biological levels.
SyNAPSE is a complex, multi-faceted project, but traces its roots to two fundamental problems. First, traditional algorithms perform poorly in the complex, real-world environments that biological agents thrive. Biological computation, in contrast, is highly distributed and deeply data-intensive. Second, traditional microprocessors are extremely inefficient at executing highly distributed, data-intensive algorithms. SyNAPSE seeks both to advance the state-of-the-art in biological algorithms and to develop a new generation of nanotechnology necessary for the efficient implementation of those algorithms.