FIAS Scientific Report 2011 - Frankfurt Institute for Advanced Studies ...

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Modeling the effect of transcranial magnetic stimulation on cortical circuits Collaborators: C. Rusu 1,2 , J. Triesch 1 , U. Ziemann 3 1 Frankfurt Institute for Advanced Studies, 2 Romanian Institute of Science and Technology, Cluj-Napoca, Romania, 3 Dept. of Neurology, Goethe University Frankfurt The goal of this research is to better understand how brain activity might be directly controlled from outside the head through magnetic stimulation. Specifically, transcranial magnetic stimulation (TMS) and other non-invasive stimulation techniques have been hypothesized to improve learning, facilitate stroke rehabilitation, treat depression, schizophrenia, chronic pain, or addictions such as alcoholism. Despite recent success in clinical treatments little is known about the cellular mechanisms underlying such stimulation techniques or the nature of the high-frequency repetitive responses (I-waves) they induce along descending motor pathways. Moreover, assessing the nature of I-waves or establishing the biophysical basis underlying the magnetic stimulation in purely experimental settings remains difficult given the scarce recording opportunities and high variability of results across healthy subjects. Models incorporating anatomically detailed neurons could overcome these limitations and provide valuable insight into the effects of TMS at the cellular level. Our model reproduces I-waves similar to those observed in epidural responses during in vivo experiments on conscious humans and explains their formation, frequency, and timing. Furthermore, our model reproduces findings from a range of experiments with different stimulation protocols and pharmacological interventions. We view this as an important first step towards designing optimized protocols for specific clinical purposes. Inhibitory Excitatory Layer 2/3 Figure: The model we have developed includes a reconstructed dendritic tree of a L5 pyramidal cell. A total of 300 excitatory and inhibitory L2/3 cells (ratio 4:1) project synapses on to the L5 cell. Related publications: 1) C. Rusu, U. Ziemann, J. Triesch, A Model of I-Wave Generation during Transcranial Magnetic Stimulation (TMS). COSYNE - Computational and Systems Neuroscience, February 23-26, 2012, Salt Lake City, Utah, USA. 2) C. Rusu, U. Ziemann, J. Triesch, A Model of TMS-induced I-waves in Motor Cortex, in preparation. 80 Layer 5
Self-organization in recurrent neural networks with multiple forms of plasticity Collaborators: P. Zheng 1 , C. Dimitrakakis 1,2 , A. Lazar 1,3 , G. Pipa 1,4 , D. Krieg 1 , P. Sterne 1 , L. Aitchison 5 , J. Triesch 1 1 Frankfurt Institute for Advanced Studies, 2 EPFL, Switzerland, 3 Max-Planck Institute for Brain Research Frankfurt, 4 University of Osnabrück, 5 Gatsby Computational Neuroscience Unit, UK Understanding the network structure of cortical circuits is of fundamental importance for understanding cortex function. The distribution of synaptic strengths of local excitatory connections in the cortex is long-tailed (approximately lognormal), but individual synapses can undergo strong fluctuations from day to day. This raises the question how the brain can maintain stable long-term memories at all. Recent evidence has shown that very strong synapses are relatively more stable than weak ones and could thus be the physiological basis of longlasting memories. At present it is unclear, however, how the measured distribution of synaptic strengths and the pattern of synaptic fluctuations arise. Our explanation is based on concepts from self-organization. We show that the distribution of synaptic strengths and their pattern of fluctuations are explained by self-organization in a recurrent spiking network model combining spike-timing-dependent plasticity, synaptic scaling, structural plasticity, and intrinsic plasticity. In this network, STDP induces a rich-get-richer mechanism for excitatory synapses, while synaptic scaling induces competition between them. The resulting self-organization produces a network with irregular activity patterns consistent with cortical recordings, lognormal-like weight distributions, and patterns of synapse fluctuations closely matching experimental data (see Figure). This self-organization is very robust to parameter changes in the network but critically depends on the presence of the different homeostatic plasticity mechanisms. Our findings have far reaching implications for the development, structure, and function of cortical circuits. First, it reveals the fundamental importance of different homeostatic plasticity mechanisms for the organization of cortex. Second, it sheds light on the structure of spontaneous activity in the cortex, which has traditionally been treated as noise. Our work suggests that this activity is in fact highly organized and essential for shaping network structure and function. In a second line of research we study these self-organizing networks from the viewpoint of information theory and try to understand their learning mechanisms as approximations to the maximization of mutual information. In a similar vein, we have developed a theory of spike-timing dependent plasticity and other forms of synaptic long-term plasticity that tries to bridge biophysical and computational levels of description. Figure: Distribution of synaptic weights in our model matches lognormal distribution of EPSPs in cortex. A: distribution of EPSP sizes from Song et al. (2005) and lognormal fit. B: distribution of weight strength in our model and lognormal fit. Related publications: 1) P. Zheng, C. Dimitrakakis, J. Triesch, Network Self-organization Explains the Statistics and Dynamics of Synaptic Connection Strengths in Cortex, in preparation. 2) A. Lazar, G. Pipa, J. Triesch, Emerging Bayesian Priors in a Self-Organizing Recurrent Network, Proc. of the International Conference on Artificial Neural Networks (ICANN), 2011. 3) D. Krieg, J. Triesch, A unifying theory of synaptic long-term plasticity based on a sparse synaptic strength, submitted. 4) P. Sterne, L. Aitchison, J. Triesch, SORN network dynamics approximately maximise mutual information, in preparation. 81
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FIAS Scientific Report 2011
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FIAS Scientific Report 2011 Table o
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Physics Research highlights 2011 To
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1. Partner Research Centers 9
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ExtreMe Matter Institute EMMI by Ca
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Bernstein Focus Neurotechnology Fra
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2. Graduate Schools 15
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participants reported on their proj
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Courses offered at FIGSS Summer Sem
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3. FIAS Scientific Life 21
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28.07.2011 Prof. Dr. Victor Flambau
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Conferences and meetings (co)organi
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4. Research Reports 4.1 Nuclear Phy
Page 29 and 30: Hydrodynamics of a quark droplet Co
Page 31 and 32: Production of hyper-nuclei in react
Page 33 and 34: On production and properties of mul
Page 35 and 36: Monte Carlo modeling microdosimetry
Page 37 and 38: Monte Carlo simulation of the spall
Page 39 and 40: Dimuon radiation within a (3+1)d hy
Page 41 and 42: Initial state anisotropies and thei
Page 43 and 44: Chiral hadronic model including res
Page 45 and 46: Trace anomaly and the vector coupli
Page 47 and 48: Dileptons from the strongly interac
Page 49 and 50: Space-time evolution of the magneti
Page 51 and 52: Extreme isospin in heavy nuclei Col
Page 53 and 54: Production of heavy and superheavy
Page 55 and 56: The phase diagram in T -µB-Nc spac
Page 57 and 58: Critical Zeeman Fields for Unitary
Page 59 and 60: BCS-BEC Crossover in 2D Fermi Gases
Page 61 and 62: Applications of a chiral SU(3) mode
Page 63 and 64: The phase diagram of black holes in
Page 65 and 66: Black holes in short scale modified
Page 67 and 68: Fractal dimensions and micro-struct
Page 69 and 70: Untangling the interactions between
Page 71 and 72: Stimulus information coded by spike
Page 73 and 74: Non-stationarity of neuronal activi
Page 75 and 76: Timescale of information processing
Page 77 and 78: Discovery of agency in 6 and 8-mont
Page 79: Power spectra of the natural input
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Page 85 and 86: Emerging Bayesian priors in a self-
Page 87 and 88: Non-linear generative models and th
Page 89 and 90: Preference elicitation and Bayesian
Page 91 and 92: 4.3 Biology, Chemistry, Molecules,
Page 93 and 94: DNA unzipping Collaborators: S.N. V
Page 95 and 96: Statistical mechanics of protein fo
Page 97 and 98: Phase transitions in large clusters
Page 99 and 100: Photo-processes in fullerenes and e
Page 101 and 102: Assessment of complex DNA damage Co
Page 103 and 104: Novel light sources: Crystalline un
Page 105 and 106: Monte-Carlo code for channeling dyn
Page 107 and 108: Programs for many-body descriptions
Page 109 and 110: Are 12 C radiation effects in liver
Page 111 and 112: Objective identification of residue
Page 113 and 114: Structural basis for the dual RNA-r
Page 115 and 116: The ALICE High Level Trigger Collab
Page 117 and 118: Arithmetic over Galois fields on mo
Page 119 and 120: High Performance GPU-based DGEMM an
Page 121 and 122: How stable are transport model resu
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Conference and Seminar Talks 2011
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FIAS conference abstracts and poste
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FIAS conference abstracts and poste
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FIAS Publications 2011 - Journal pu
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FIAS publications 2011 - Conference
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FIAS publications 2011 - Patents 24
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The success of FIAS would not have
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