Information Processing
in Distributed Neuronal Circuits

Having a stable and unified perception of the world, requires combining different representations of the environment across the partially reliable sensory observations. Bayesian Decision Theory has successfully formulated the underlying process of sensory integration in a wide range of human multisensory perceptual tasks [1]. When sensory signals are generated by separate sources, fusion is not rational anymore; for instance we never combine the sound of mewing with the picture of a dog. Since we are constantly surrounded by multiple sources of information in real world, in addition to fusion, information segregation is also a vital perceptual process.
Taking into account all possible hypotheses and the problem of whether to fuse information or not, involves a probabilistic inference process called Causal Inference (Fig.a) [2]. It is still not fully understood where and through which mechanisms Causal Inference is emerged in the Cortex. But recently it is hypothesized that a hierarchical distributed circuit including parietal and early sensory cortices generates this process [2]. In this work and through a neural modeling approach, we have demonstrated how such a distributed cortical hierarchy (Fig.b) can perform probabilistic causal inference and reproduce human behavior [3].

Contact: Mohsen Firouzi

The recent discovery of grid cells in the rodent brain [1] have sparked an interest in understanding the spatial representation in neural networks. Especially, as the grid cells have now been reported to be present in the human brain as well [2]. These cells play a crucial role in the spatial navigation of rodents. Gaining knowledge about their parallel processing of information to solve navigational tasks may help solving navigation problems on robots [3] in a parallel asynchronous and power efficient way using novel neuromorphic hardware. Ultimately, this may lead to general principles of massively parallel algorithms which operate on locally distributed information in an asynchronous fashion.

Contact: Marcello Mulas, Nicolai Waniek

Human cognitive abilities by far exceed those of today’s most advanced computer systems. Understanding such human cognitive reasoning would bring algorithmic development a large leap forward towards truly cognitive abilities, such as learning from prior experience while interacting in a noisily observable environment. So far, however, imitations of the human brain in algorithms (such as artificial neural networks) have only been partially successful. What has often been neglected in neurobiological modeling is that neither the brain’s computing substrate nor the brain’s computing algorithms developed independently of each other. In this research project, we use graphical models (e.g. Factor Graphs [1]) combined with computational learning techniques to mimic cognitive processing of the forebrain [2] on distributed hardware that is particularly well matched to implement such computations [1,3].

Contact: Indar Sugiarto, Jörg Conradt