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Dr. Christian Tetzlaff



Projects: Theory of Precise Timing in Spiking Neural Networks
Address: Friedrich-Hund-Platz 1
37077 Göttingen
Germany
Office: E.01.105
Phone: +49-(0)551-39-10762
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The dynamics of cell assemblies and their relations to psychological processes

Summary

Important mechanisms of cognition in humans and animals are the ability to manipulate new and old memories and (dis-)connect them to other knowledge. In addition, groups of memories can be combined and generalized to create an abstract concept about them. However, the underlying dynamics of neurons and synapses yielding these processes are widely unknown. In this project I bridge this gap by theoretical models based on the well-established ideas of the Synaptic-Plasticity-and-Memory hypothesis. Due to this hypothesis, a memory item is represented by a strongly interconnected group of neurons – a cell assembly. I analyze if the dynamics of such cell assemblies lead to the dynamics of memories measured on the psychological level.

Background

The ability to learn and memorize facts and situations of the environment is an essential property of human’s and animal’s brains or neural circuits. Amongst others, such a circuit consists of a network of neurons and their connections, the synapses. The common idea of how such neural circuits learn and memorize is described by the so-called synaptic-plasticity-and-memory (SPM) hypothesis (Hebb, 1949; Martin et al., 2000; Martin and Morris, 2002). Based on this hypothesis, a learning signal induces changes at the synapses resulting to the formation of strongly interconnected neurons, named cell assemblies. These cell assemblies, in turn, serve as long-term representations of the learned items or facts. On the other hand, psychological experiments show that humans are able to perform higher cognitive tasks with their memories as, for instance, combining memories (association), separating memories (discrimination), or generalizing memories (Bartlett, 1932; Gagné, 1965). Further, the processes of working memory base widely on long-term memory represented by cell assemblies (Baddeley and Hitch, 1974; Kühnel and Markowitsch, 2009). However, although the SPM hypothesis is well established in neuroscience (Kandel, 2001; Martin et al., 2002; Buzsaki, 2010), it is still unknown how the dynamics of neurons and synapses yield these cognitive processes on the psychological scale.

The goal of this project is to analyze, based on the SPM hypothesis, whether and under which circumstances neuronal and synaptic dynamics induce the cognitive processes discussed above. Therefore, I extend theoretical investigations, which already successfully explained single memory dynamics of humans on the basis of the SPM hypothesis (Tetzlaff et al., 2011; Tetzlaff et al., 2012; Tetzlaff et al., 2013), by (i) multi-memory interactions and by (ii) the dynamics of state-of-the-art working memory models.

In summary, this project will deliver further evidences supporting the SPM hypothesis and its links to higher cognitive processes of memory processing.

Project Details

1. Interactions between cell assemblies

The interactions between cell assemblies could determine the basis for higher cognitive abilities. To test this hypothesis I analyze the dynamics of several cell assemblies/memories with theoretical network models.
First, I analyze the resulting dynamics from a two-cell-assembly system given different input structures. These dynamics are compared to basic psychological learning paradigms (Gagné, 1965).
Second, I analyze the dynamics resulting from larger systems consisting of several cell assemblies. Thereby, I focus on the self-organized formation of complex structures of groups of strongly interconnected cell assemblies/memories. Such groups correspond on the psychological level to schemas of knowledge (Bartlett 1932; Tse et al., 2007, 2011) or generalizations of detailed knowledge.

2. The combination of cell assemblies and working memory

Cell assemblies are representations of items and facts in the long-term memory. However, in everyday life this stored knowledge is recalled and processed with respect to new and old information. This processing is done in the so-called working memory. Recent models (e.g., reservoir networks; Buonomano and Maass, 2009) of working memory show that the diversity or rather randomness of neural networks enables the processing of information. However, in these models, it is still unknown how the dynamics corresponding to working memory interact with long-term memory represented by cell assemblies. The goal of this working point is to understand how these two mechanisms, the diverse dynamics of reservoir networks and the ordered structures of cell assemblies, interact with each other. Therefore, I analyze whether the neuronal dynamics within an adaptive network, which is able to form cell assemblies, are comparable to the dynamics needed for reservoir-like information processing.

Selected Publications

Tetzlaff C, Okujeni S, Egert U, Wörgötter F, Butz M, "Self-organized criticality in developing neuronal networks" PLoS Computational Biology, 6(12):e1001013, 2010

Kolodziejski C, Tetzlaff C, Wörgötter F, "Closed-form treatment of the interactions between neuronal activity and spike-timing-dependent plasticity in networks of linear neurons", Frontiers in Computational Neuroscience, 4(134), 2010, doi:10.3389/fncorn.2010.00134

Tetzlaff C, Kolodziejski C, Timme M, Wörgötter F, "Synaptic Scaling in Combination with many generic plasticity mechanisms stabilizes circuit connectivity" Frontiers in Computational Neuroscience, 5(47), 2011, doi: 10.3389/fncom.2011.00047

Tetzlaff C, Kolodziejski C, Timme M, Wörgötter F, "Analysis of synaptic scaling in combination with Hebbian plasticity in several simple networks" Frontiers in Computational Neuroscience, 6(36), 2012, doi:10.3389/ fncom.2012.00036

Tetzlaff C, Kolodziejski C, Markelic I, Wörgötter F, "Time scales of memory, learning, and plasticity" Biological Cybernetics, 106(11), 715-726, 2012

Tetzlaff C, Kolodziejski C, Timme M, Tsodyks M, Wörgötter F, "Synaptic Scaling Enables Dynamically Distinct Short- and Long-Term Memory Formation" PLoS Comput Biol 9(10), e1003307, 2013 doi:10.1371/journal.pcbi.1003307