September, 22nd - 26th, 2008
Fall Course on Computational Neuroscience in Göttingen
As in the last years there will again be a fall course on computational
neuroscience hosted by the BCCN
Göttingen from mon 22 until fri 26 of September 2008. It is ment as a tutorial for BCCN students as well as an
open fall course for external students.
- Gaute Einevoll: 'How are extracellularly recorded potentials such as local field potentials (LFP) and multi-unit activity (MUA) related to the underlying neural activity?'
- Daniel Durstewitz: 'Biophysics-
and physiology-based models of neural circuit dynamics and
- Marc van Rossum: 'Synaptic plasticity: learning and forgetting'
- Hamutal Slovin: 'Spatio-temporal patterns of information processing in the cortex: perspective from VSDI'
- Alessandro Treves: 'Structure and function of hippocampal networks'
Abstracts and Readings
Gaute T. Einevoll
(Norwegian University of Life Sciences)
and multi-unit activity (MUA) related to the underlying neural activity?
Computational neuroscience rely on experimental data to make progress; data are needed to constrain and test mathematical models. For cortical neural network models the essential experimental method in vivo has so far been single-unit extracellular recordings: when a sharp electrode is placed sufficiently close to the soma of a particular neuron, the recorded potential reliably measures the firing of individual action potentials in this neuron. This information is contained in the high-frequency part of the recorded potentials. The low-frequency part, that is, the local field potentials (LFP), is in general due to complicated weighted sums of contributions from many neurons. They have thus proved much more difficult to interpret and have thus in general been discarded from further analysis. However, while the LFP measured from a single electrode is difficult to interpret, simultaneous recordings of the LFP at many spatial positions by means of various types of multi-electrodes offers a way to probe neural population activity in vivo. Such experimental methods are sorely needed to facilitate development of biologically relevant cortical network models. Techniques for such large-scale recordings are rapidly improving, and there is a need for new methods for extraction of relevant information from such data. In the tutorial lectures the neural origin of extracellular potentials, various electrode configurations used to measure them, and example applications will be presented. Furthermore, various new methods of analysis of such data and results from various modeling studies on the relationship between extracellular potentials and single-neuron or population activities will be discussed.
K.H. Pettersen, A. Devor, I. Ulbert, A.M. Dale and G.T. Einevoll: Current-source density estimation based on inversion of electrostatic forward solution: Effects of finite extent of neuronal activity and conductivity discontinuities, Journal of Neuroscience Methods 154:116-133 (2006)
K.H. Pettersen, E. Hagen, and G.T. Einevoll: Estimation of population firing rates and current source densities from laminar electrode recordings, Journal of Computational Neuroscience 24:291-313 (2008)
K.H. Pettersen and G.T. Einevoll: Amplitude variability and extracellular low-pass filtering of neuronal spikes, Biophysical Journal 94:784-802 (2008)
C. Bedard, H. Kroger and A. Destexhe: Does the 1/f frequency scaling of brain signals reflect self-organized critical states? Physical Review Letters 97:118102 (2006)
H.A. Swadlow, A.G. Gusev, T. Bezdudnaya: Activation of a cortical column by a thalamocortical impulse, Journal of Neuroscience 22:7766-7773 (2002)
(Centre for Theoretical and Computational Neuroscience, Plymouth)
In my lecture I will give an overview of neural models deeply anchored in in-vitro electrophysiological, morphological, and anatomical data, and how they may inform us about principles of neural computation. Single cortical neurons are equipped with a rich repertoire of different ionic channels that regulate the integration of impinging synaptic signals on different time scales, and may generate temporally structured outputs on different scales, such as self-maintained (autonomous) oscillatory bursting behavior. Moreover, many neurons in the brain possess complex spatial layouts (morphologies) that may partly reflect adaptations to particular computational demands. The specific biophysical and morphological properties of cortical neurons in turn can have a tremendous impact on dynamical phenomena at the network level, and hence on the implementation of computational functions. My lecture will revolve around these relationships between basic biophysical and morphological properties of neurons, the implications for network dynamics, and their potential computational/cognitive role.
D. Durstewitz: Self-Organizing Neural Integrator Predicts Interval Times through Climbing Activity. The Journal of Neuroscience, June 15, 2003 • 23(12):5342–5353
E. Fransen, B. Tahvildari, A. V. Egorov, M. E. Hasselmo and A. A. Alonso Mechanism of Graded Persistent Cellular
Activity of Entorhinal Cortex Layer V Neurons. Neuron 49, 735–746, March 2, 2006
P. Poirazi, T. Brannon and B. W. Mel: Pyramidal Neuron as Two-Layer Neural Network. Neuron, Vol. 37, 989–999, March 27, 2003
J. K. Seamans, D. Durstewitz, B. R. Christie, C. F. Stevens and T. J. Sejnowski: Dopamine D1yD5 receptor modulation of excitatory synaptic inputs to layer V prefrontal cortex neurons. PNAS (2001) vol. 98 no. 1, 301–306
Y. Wang, H. Markram, P. H. Goodman, T. K. Berger, J. Ma and P. S. Goldman-Rakic: Heterogeneity in the pyramidal network of the medial prefrontal cortex. Vol 9, Nr 4, APRIL 2006, NATURE NEUROSCIENCE
(University of Edinburgh)
G. Billings and M. C. W. van Rossum: Memory retention and Spike Timing Dependent Plasticity, (submitted)
U. Frey and R. G. M. Morris: Synaptic tagging and long-term potentiation. Nature Vol. 385 (1997)
R. C. Froemke, M. M. Merzenich & C. E. Schreiner: A synaptic memory trace for cortical receptive field plasticity. Nature, Vol 450 (2007)
E. Pastalkova, P. Serrano, D. Pinkhasova, E. Wallace, A. A. Fenton, T. C. Sacktor: Storage of Spatial Information by the Maintenance Mechanism of LTP. SCIENCE, Vol 313 (2006)
M. C. W. van Rossum, G. Q. Bi and G. G. Turrigiano: Stable Hebbian Learning from Spike Timing-Dependent Plasticity. The Journal of Neuroscience, (2000), 20(23):8812–8821
(International School for Advanced Studies, Trieste - Sector of Cognitive Neuroscience)
A. Bakker, C. B. Kirwan, M. Miller, C.E.L. Stark: Pattern Separation in the Human Hippocampal CA3 and Dentate Gyrus. Science 319, 1640 (2008)
S. Leutgeb, J.K. Leutgeb, A. Treves, M.-B. Moser, E.I. Moser: Distinct Ensemble Codes in Hippocampal Areas CA3 and CA1. Science, Vol 305, (2004)
J. K. Leutgeb, S. Leutgeb, M.-B. Moser, E. I. Moser: Pattern Separation in the Dentate Gyrus and CA3 of the Hippocampus. Science315, 961 (2007)
I. Samengo and A. Treves: Representational Capacity of a set of independent neurons. Physical Review E, (2000)
A. Treves, E.T. Rolls: Computational Constraints Suggest the Need for Two Distinct Input Systems to the Hippocampal CA3 Network. Hippocampus, 2(2), 189-200, (1992)
(Bar-Ilan University, Israel)
H. Slovin, A. Arieli, R. Hildesheim and A.Grinvald: Long-Term Voltage-Sensitive Dye Imaging Reveals Cortical Dynamics in Behaving Monkeys. Journal of Neurophysiology (2002)
E. Seidemann, A.Arieli, A. Grinvald and H. Slovin: Dynamics of Depolarization and Hyperpolarization in the Frontal Cortex and Saccade Goal. Science (2002)
T. Kenet, D. Bibitchkov, M. Tsodyks, A. Grinvald and A. Arieli: Spontaneously Emerging Cortical Representations of Visual Attributes. Nature (2003)
M. Tsodyks, T. Kenet, A. Grinvald, A. Arieli: Linking Spontaneous Activity of the Single Cortical Neurons and the underlying Functional Architecture. Science (1999)
Y. Ikegaya, G. Aaron, R. Cossart, D. Aronov, I. Lampl, D. Ferster and R. Yuste: Synfire Chains and Cortical Songs: Temporal Modules of Cortical Activity. Science (2004)
|Mon. 22.||Tue. 23.
|16:30-18:00||presentation||city tour||excursion to Plesseburg
The course is
intended to provide graduate students and young
researchers from all parts of neuroscience with working knowledge of
theoretical and computational methods in neuroscience and to acquaint
them with recent developments in this field.
The speakers provide an overview on important aspects and recent developments in their fields of expertise by means of three-hour tutorials. In addition to the tutorials, participants will gather in small groups and study one out of a number of recent research papers that are related to the respective tutorial. The "self-study" will be supervised by the speakers such that (based on the introduction given in the tutorials) a profound insight in the main ideas can be obtained. The results of the self-study will be shared with other participants and discussed with them and the speakers during the presentations.
The particular form of the course has proven successful in previous courses (1999 and 2001 at Bochum, 2003, 2004, 2005, 2006 and 2007 at Göttingen). It combines lecturing with an active interaction with the main ideas of the topical fields in a way which has proven efficient given the time constraints of the course.
One of the main objectives of the course will be to enable participants from any field of neuroscience to study recent research papers on their own. Each day of the course is devoted to a different topic. Part of the teaching will be in form of a tutorial, but there shall be room for the activities of the participants. In previous years each day has been divided into three phases:
Before noon there will be a tutorial consisting of two lectures (2 times 90 min) where (as a rule) the first one should introduce the topic of research and the second one more specifically should provide background information for the study of a number of recent key papers in the field.
Each paper is then assigned to a group consisting of about four participants. The papers shall be scanned already before the course (the papers can be downloaded some weeks before the course). In the early afternoon, participants discuss the paper and prepare a presentation to the members of the other groups. Speakers will be available during the self-study phase to answer questions related to the papers.
Later in the afternoon there are slots of 20 min presentations of these papers by one or more representatives of each group and discussions together with the tutorial speakers and the members of other groups.
In the evening there will be opportunities to participate in various social activities.
Former Fall Courses on Computational Neuroscience in Göttingen