Institut für Theoretische Physik
Start / Aktuell
Juli  2014
Do
31.07.2014
Seminarraum SR11, C3.101
Materialphysik
16:15
Materialphysikalisches Seminar

Andreas Kelling
IMP

In-situ TEM studies of interface fracture



Kontakt: C. Kuba/Doz. Materialphysik

August  2014
Fr
01.08.2014
Ludwig-Prandtl Lecture Hall, Am Fassberg 11, 37077 Goettingen
Nichtlineare Dynamik
09:00
Symposium on Perspectives in Computational Neuroscience

Dr. Christian Rössert
The University of Sheffield, Sheffield, UK

At the edge of chaos: how granular-layer dynamics can provide the basisfor cerebellar function

The cerebellum plays an important role in motor control and motor learning and therefore is essential for everyday tasks in animals and humans. While the cerebellum itself does not initiate movements it strongly contributes to their refinement and correction. Subsequently cerebellar dysfunction leads to erratic, uncoordinated, or incorrectly timed motion. A unifying theory that can explain many aspects of cerebellar function such as learning of internal models, state estimation, (sensory) signal processing and motor control is the highly versatile adaptive-filter model of the original Marr­Albus theoretical framework. The basic function of the adaptive-filter model involves two key features that are very congruent with the cerebellar cortical architecture: in the granular layer the input signals are supposed to be expanded and recoded to provide a foundation from which the Purkinje cells synthesise output filters to implement specific behavioural signals. While many aspects of the adaptive-filter model have been shown to be in agreement with electrophysiological findings, e.g. symmetrical learning at Purkinje cell synapses, the important general mechanism of expansion-recoding has not yet been identified. In this talk I will give a general introduction to cerebellar- function, architecture and theoretical models and will focus on the hypothesis that random recurrent inhibition in the granular layer could indeed provide the necessary input signal separation and lengthening as required for expansion-recoding. I further show that this proposed mechanism is very similar to the well-established machine learning approach of reservoir computing and that the quality of filter construction is best if the network is close to the edge of random chaotic behaviour. In conclusion I will provide an outlook on important potential multi-scale modelling studies ranging from cellular ion-channels to cerebellar-inspired robotic control.

Kontakt: Prof. Dr. Fred Wolf
Fr
01.08.2014
Ludwig-Prandtl Lecture Hall, Am Fassberg 11, 37077 Goettingen
Nichtlineare Dynamik
10:00
Symposium on Perspectives in Computational Neuroscience

Dr. Viola Priesemann

Balancing optimal information processing and sustainable dynamics in vivo

Self-organized criticality (SOC) has been proposed to govern neural activity, because SOC might provide the basis for optimal information processing. However, at SOC also can show runaway activity spontaneously. Evidence for SOC has been obtained for neural activity in vitro, but not yet for spiking activity in vivo. Therefore we analyzed highly parallel spike recordings from awake rats, cats and monkeys and compared these to spike activity from established SOC models. We found clear differences between the neural and the model activity. These differences indicated that the brain operates close to SOC, but in a slightly sub-critical regime. These results suggest that the brain seeks a balance between avoiding runaway activity, which can occur spontaneously at SOC, and optimizing information processing, which has been linked to SOC. For my future research, I aim at characterizing this balance combining information theoretic and physiological perspectives.

Kontakt: Prof. Dr. Fred Wolf
Fr
01.08.2014
Ludwig-Prandtl Lecture Hall, Am Fassberg 11, 37077 Goettingen
Nichtlineare Dynamik
11:15
Symposium on Perspectives in Computational Neuroscience

Dr. Venkatakrishnan Ramaswamy
The Hebrew University of Jerusalem, Jerusalem, Israel

Symposium on Perspectives in Computational Neuroscience

Several efforts are currently underway to decipher the connectome or parts thereof in a variety of organisms. Ascertaining the detailed physiological properties of all the neurons in these connectomes, however, is out of the scope of such projects and indeed out of reach of current experimental technology. It is therefore unclear to what extent knowledge of the connectome alone will advance a mechanistic understanding of computation occurring in these neural circuits, especially when the high-level function of the said circuit is unknown. While it is generally acknowledged that the structure of a neural circuit constrains what it can compute, the nature and scope of these connectomic constraints are not well understood. Theoretical work is therefore called for, in order to achieve a broad understanding of the issues involved and to build a framework within which neuroscientists can think about the connectomics data, formulate meaningful hypotheses and make testable predictions to advance an understanding of the neuronal circuit(s) in question. I will talk about some recent work (with Arunava Banerjee) in which we have examined these issues in the context of feedforward networks. Specifically, for feedforward networks equipped with neurons that obey a deterministic spiking neuron model, we asked if just by knowing the architecture of a network, we can rule out spike-timed computations that it could be doing, no matter what response properties each of its neurons may have. We showed results of this form for certain classes of architectures. We also showed that for certain other classes of network architectures, given the limited assumptions on the individual neurons, there are fundamental limits to constraints imposed by network structure alone. I will close with a brief description of my other work in Computational Neuroscience and future research directions.

Kontakt: Prof. Dr. Fred Wolf
Di
05.08.2014
Ludwig-Prandtl Lecture Hall, Am Fassberg 11, 37077 Goettingen
Nichtlineare Dynamik
17:15
AG-Seminar: MPI für Dynamik und Selbstorganisation - Bernstein Center for Computational Neuroscience/ Bernstein Focus Neurotechnology

Dr. Christian Tetzlaff
Georg-August-Universität, BCCN, Göttingen

The interactions of adaptive processes on different time scales and their functional roles in neural systems

The environment, humans and animals are situated in, changes over a broad range of time scales. To guarantee adequate behaviors in such environments, on the one side, each individual requires the ability to learn, process, and memorize moments of the environment over different time scales. On the other side, the dynamics of neural systems is determined by several adaptive processes which also proceed on a wide variety of time scales. My research concentrates on linking the adaptive processes of neural systems, as synaptic plasticity and scaling, to the learning and memory processes on the behavioral level. This enables us, for instance, to provide potential explanations for intriguing memory effects as memory destabilization and consolidation and to analyze the interactions between different memory systems as working and long-term memory.

Kontakt: Prof. Dr. Fred Wolf

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Last modified: Mon Mar 5 10:17:50 CET 2012