Research

Research

Memory Formation and Dynamics

Key Publications:

Learning and memory constitute fundamental mechanisms for humans and animals to survive in complex, changing environments. Thereby, two basic problems emerge: (i) how does the brain reliably form memory representations of sensed environmental stimuli to gradually build-up an “internal schema” of the outside world and (ii) how does the brain readapt this schema according to changes in the environment without strongly interfering with the old knowledge. We tackle these problems by investigating the interplay between different learning-related neuronal and synaptic processes. For this, we focus on assessing under which conditions the resulting self-organized neuronal and synaptic dynamics form quasi-stable patterns or so-called Hebbian cell assemblies representing memory and how the relations between multitudes of these patterns can be adapted according to changes in the environment.

Tetzlaff C, et al. (2013). PLoS Computational Biology, 9(10):e10003307.

Tetzlaff C, Dasgupta S, Kulvicius T, Wörgötter F (2015). Scientific Reports, 5:12866.

Key Publications:

Memory Consolidation

After the successful formation of a memory, the memory has to undergo further phases to become gradually stable against external and internal perturbations or rather forgetting. For this, the memory has to be regularly consolidated during wake and sleep implying that the memory representation itself has to be transferred to different states. Considering the interplay of diverse neuronal and synaptic processes, we investigate under which conditions these states are formed and how a memory representation can reach these given different learning protocols. In addition, by linking these states with the underlying neuronal and synaptic dynamics, we bridge the gap between the dynamics of single neurons and synapses and cognitive neuroscience. To test the resulting links, we develop experimental testable predictions.

Tetzlaff C, et al. (2013). PLoS Computational Biology, 9(10):e10003307.

Li Y, Kulvicius T, Tetzlaff C (2016). PLoS One, 11(8):e0161679.

Key Publications:

Structural Plasticity

One of the basic substrates of neuronal systems are the connections between neurons – the synapses. Interestingly, these synapses are continuously deleted and new ones are formed. The resulting variability of the network structure implies the problem of how the brain can reliably form and maintain memories although the underlying substrate is permanently changing. Given experimental data, we develop theoretical models of these dynamics and combine them with models of memory formation to assess the influence of structural changes on the maintenance of memory representations in neuronal networks. By this, we are able to obtain insights into the mechanisms of the brain underlying the formation of long-lasting memories.

Fauth M, Wörgötter F, Tetzlaff C (2015). PLoS Computational Biology, 11(12):e1004684.

Fauth M, Tetzlaff C (2016). Frontiers in Neuroanatomy, 10:75.

Key Publications:

Robotic Application

We investigate whether the interplay between diverse synaptic and neuronal process yields the emergence of functionally relevant, complex behaviors. Besides formulating experimental verifiable predictions, we test the functionality of the resulting network dynamics by controlling diverse robotic platforms. These platforms are mainly hexapods and robot arms in real as well as in simulations. This approach is especially suitable for testing the perfomance of the neuronal networks in complex, changing environments. Furthermore, the resulting link between theoretical neuroscience and robotics enables us to develop new technological advances.

Tetzlaff C, Dasgupta S, Kulvicius T, Wörgötter F (2015). Scientific Reports, 5:12866.

Grinke E, Tetzlaff C, Wörgötter F, Manoopong P (2015). Frontiers in Neurorobotics, 9:11.

Key Publications:

Molecular Dynamics

The interactions and reactions between wide varieties of molecules serve as basis of all synaptic and neuronal processes. To better ground our theoretical formulations of the synaptic and neuronal processes, we develop and analyze detailed theoretical models of the corresponding molecular dynamics in strong connection with experimental data. These detailed models, in turn, are incorporated into the network models to investigate the influence of the molecular dynamics on the dynamics of memory representations. Thus, we are able to link with our theoretical models the dynamics on the molecular, microscopic level with the macroscopic, cognitive level of complex behaviors.

Li Y, Kulvicius T, Tetzlaff C (2016). PLoS One, 11(8):e0161679.

Contact

Dr. Christian Tetzlaff

Third Institute of Physics - Biophysics

Faculty of Physics

Friedrich-Hund-Platz 1

37077 Göttingen

Germany

Phone:

+49 551 3920258

Mail:

tetzlaff [at] phys.uni-goettingen.de

Room:

F01.145