Wednesday, February 17, 2010 - 11:00 , Location: Skiles 255 , Raoul-Martin Memmesheimer , Center for Brain Science, Faculty of Arts and Sciences Harvard University , Organizer:
Mean field theory for infinite sparse networks of spiking neurons shows that a balanced state of highly irregular activity arises under a variety of conditions. The state is considered to be a model for the ground state of cortical activity. In the first part, we analytically investigate its irregular dynamics in finite networks keeping track of all individual spike times and the identities of individual neurons. For delayed, purely inhibitory interactions, we show that the dynamics is not chaotic but in fact stable. Moreover, we demonstrate that after long transients the dynamics converges towards periodic orbits and that every generic periodic orbit of these dynamical systems is stable. These results indicate that chaotic and stable dynamics are equally capable of generating the irregular neuronal activity. More generally, chaos apparently is not essential for generating high irregularity of balanced activity, and we suggest that a mechanism different from chaos and stochasticity significantly contributes to irregular activity in cortical circuits. In the second part, we study the propagation of synchrony in front of a background of irregular spiking activity. We show numerically and analytically that supra-additive dendritic interactions, as recently discovered in single neuron experiments, enable the propagation of synchronous activity even in random networks. This can lead to intermittent events, characterized by strong increases of activity with high-frequency oscillations; our model predicts the shape of these events and the oscillation frequency. As an example, for the hippocampal region CA1, events with 200Hz oscillations are predicted. We argue that these dynamics provide a plausible explanation for experimentally observed sharp-wave/ripple events.
Wednesday, February 10, 2010 - 11:00 , Location: 255 Skiles , Steven Ellner , Cornell , firstname.lastname@example.org , Organizer:
Emerging diseases have played an important role in the major recent declinesof coral reef cover worldwide. I will present some theoretical efforts aimedat understanding processes of coral disease development and itsconsequences: (1) how the development of coral disease is regulated bymicrobial population interactions within the mucus layer surrounding thecoral, and (2) the effects of a recent fungal epizootic on populations of aCaribbean sea fan coral, focusing on how this species was able to recover tohigh abundance and low disease prevalence. Collaborators on this workinclude John Bruno (UNC-CH); C. Drew Harvell, Laura Jones, and JustinMao-Jones (Cornell), and Kim Ritchie (MOTE Marine Lab).
Wednesday, February 3, 2010 - 11:00 , Location: Skiles 255 , Ilya Nemenman , Emory University , Organizer:
Even the simplest biochemical networks often have more degrees of freedoms than one can (or should!) analyze. Can we ever hope to do the physicists' favorite trick of coarse-graining, simplifying the networks to a much smaller set of effective dynamical variables that still capture the relevant aspects of the kinetics? I will argue then that methods of statistical physics provide hints at the existence of rigorous coarse-grained methodologies in modeling biological information processing systems, allowing to identify features of the systems that are relevant to their functions. While a general solution is still far away, I will focus on a specific example illustrating the approach. Namely, for a a general stochastic network exhibiting the kinetic proofreading behavior, I will show that the microscopic parameters of the system are largely important only to the extent that they contribute to a single aggregate parameter, the mean first passage time through the network, and the higher cumulants of the escape time distribution are related to this parameter uniquely. Thus a phenomenological model with a single parameter does a good job explaining all of the observable data generated by this complex system.
Wednesday, January 20, 2010 - 11:00 , Location: SKiles 269 , Lee Childers , Georgia Tech, School of Applied Physiology , email@example.com , Organizer:
Cycling represents an integration of man and machine. Optimizing this integration through changes in rider position or bicycle component selection may enhance performance of the total bicycle/rider system. Increasing bicycle/rider performance via mathematical modeling was accomplished during the US Olympic Superbike program in preparation for the 1996 Atlanta Olympic Games. The purpose of this presentation is to provide an overview on the science of cycling with an emphasis on biomechanics using the track pursuit as an example. The presentation will discuss integration and interaction between the bicycle and human physiological systems, how performance may be measured in a laboratory as well as factors affecting performance with an emphasis on biomechanics. Then reviewing how people pedal a bicycle with attention focused on forces at the pedal and the effect of position variables on performance. Concluding with how scientists working on the US Olympic Superbike program incorporated biomechanics and aerodynamic test data into a mathematical model to optimize team pursuit performance during the 1996 Atlanta Olympic Games.
The Reynolds number (Re) is often used to describe scaling effects in ﬂuid dynamics and may be thought of as roughly describing the ratio of inertial to viscous forces in the ﬂuid. It can be shown that ’reciprocal’ methods of macroscopic propulsion (e.g. ﬂapping, undulating, and jetting) do not work in the limit as Re approaches zero. However, such macroscopic forms of locomotion do not appear in nature below Re on the order of 1 − 10. Similarly, macroscopic forms of feeding do not occur below a similar range of Reynolds numbers. The focus of this presentation is to describe the scaling effects in feeding and swimming of the upside down jellyﬁsh (Cassiopeia sp.) using computational fluid dynamics and experiments with live animals. The immersed boundary method is used to solve the Navier-Stokes equations with an immersed, flexible boundary. Particle image velocimetry is used to quantify the flow field around the live jellyfish and compare it to the simulations.
Host: Meghan Duffy (School of Biology, Georgia Tech)
Why do parasites cause disease? Theory has shown that natural selection could select for virulent parasites if virulence is correlated with between-host parasite transmission. Because ecological conditions may affect virulence and transmission, theory further predicts that adaptive levels of virulence depend on the specific environment in which hosts and parasites interact. To test these predictions in a natural system, we study monarch butterflies (Danaus plexippus) and their protozoan parasite (Ophryocystis elektroscirrha). Our studies have shown that more virulent parasites obtain greater between-host transmission, and that parasites with intermediate levels of virulence obtain highest fitness. The average virulence of wild parasite isolates falls closely to this optimum level, providing additional support that virulence can evolve as a consequence of natural selection operating on parasite transmission. Our studies have also shown that parasites from geographically separated populations differ in their virulence, suggesting that population-specific ecological factors shape adaptive levels of virulence. One important ecological factor is the monarch larval host plants in the milkweed family. Monarch populations differ in the milkweed species they harbor, and experiments have shown that milkweeds can alter parasite virulence. Our running hypothesis is that plant availability shapes adaptive levels of parasite virulence in natural monarch populations. Testing this hypothesis will improve our understanding of why some parasites are more harmful than others, and will help with predicting the consequences of human actions on the evolution of disease.
Wednesday, November 11, 2009 - 11:00 , Location: Skiles 269 , Gennady Cymbalyuk , Georgia State University, Neuroscience Institute and Dept. of Physics and Astronomy , Organizer:
Bursting, tonic spiking, sub-threshold oscillations and silence are basic robust regimes of activity of a single neuron. The talk will be focused on the co-existence of regimes of activity of neurons. Such multistability enhances potential flexibility to the nervous system and has many implications for motor control and decision making. I will identify different scenarios leading to multistability in the neuronal dynamics and discuss its potential roles in the operation of the central nervous system under normal and pathological conditions.
Wednesday, November 4, 2009 - 11:00 , Location: Skiles 269 , Tanya Berger-Wolf , Department of Computer Science, University of Illinois at Chicago , Organizer: Christine Heitsch
Computation has fundamentally changed the way we study nature. Recent breakthroughs in data collection technology, such as GPS and other mobile sensors, are giving biologists access to data about wild populations that are orders of magnitude richer than any previously collected. Such data offer the promise of answering some of the big ecological questions about animal populations. The data are not unique to animal domain but is now prevalent in human interactions: emails, blogs, and online social networks. Unfortunately, our ability to analyze these data lags substantially behind our ability to collect it. In particular, interactions among individuals are often modeled as social networks where nodes represent individuals and an edge exists if the corresponding individuals have interacted during the observation period. The model is essentially static in that the interactions are aggregated over time and all information about the time and ordering of social interactions is discarded. We show that suchtraditional social network analysis methods may result in incorrect conclusions on dynamic data about the structure of interactions and the processes that spread over those interactions. We have extended computational methods for social network analysis to explicitly address the dynamic nature of interactions among individuals. We have developed techniques for identifying persistent communities, influential individuals, and extracting patterns of interactions in dynamic social networks. We will present our approach and demonstrate its applicability by analyzing interactions among zebra populations.
Many vertebrate motor and sensory systems "decussate," or cross the midline to the opposite side of the body. The successful crossing of millions of axons during development requires a complex of tightly controlled regulatory processes. Since these processes have evolved in many distinct systems and organisms, it seems reasonable to presume that decussation confers a significant functional advantage - yet if this is so, the nature of this advantage is not understood. In this talk, we examine constraints imposed by topology on the ways that a three dimensional processor and environment can be wired together in a continuous, somatotopic, way. We show that as the number of wiring connections grows, decussated arrangements become overwhelmingly more robust against wiring errors than seemingly simpler same-sided wiring schemes. These results provide a predictive approach for understanding how 3D networks must be wired if they are to be robust, and therefore have implications both regenerative strategies following spinal injury and for future large scale computational networks.