Bulletin of the American Physical Society
APS March Meeting 2010
Volume 55, Number 2
Monday–Friday, March 15–19, 2010; Portland, Oregon
Session H7: Optimization Principles in Biological Physics |
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Sponsoring Units: DBP Chair: William Bialek, Princeton University Room: Portland Ballroom 254 |
Tuesday, March 16, 2010 8:00AM - 8:36AM |
H7.00001: Balanced excitation and inhibition lead to statistical and dynamical criticality Invited Speaker: We present a simple abstract model, an anti-Hebbian network which spontaneously poises itself, by balancing excitation and inhibition, at a dynamically critical state: an extensive number of degrees of freedom approach Hopf bifurcations, becoming arbitrarily sensitive to external perturbations (PRL~102, 258102 - 2009). As the dynamics controlling this state has itself a marginal fixed point, the eigenvalues fluctuate close to the imaginary axis; when they become slightly unstable, the corresponding mode ``breaks out'' and becomes more prominent, and as they become slightly stable the mode slowly damps out. This breakout dynamics displays avalanche-like activity bursts whose sizes may be power-law distributed, i.e. statistically critical. Within these epochs the neurons of our model are slightly correlated; yet, as the number of small but significant correlations is high, the model has strongly correlated network states. This system is, on the short time-scale, sensitive in bulk to any outside input, even if applied only to a small subset of the neurons. We also present preliminary results showing that human brain electro-physiological recordings display both statistical and dynamical criticality. [Preview Abstract] |
Tuesday, March 16, 2010 8:36AM - 9:12AM |
H7.00002: Optimal Performance in Sensorimotor Behavior Invited Speaker: Suppose that the variability in our movements is caused not by noise in muscle contraction, nor by fluctuations in our intentions or plans, but rather by errors in our sensory estimates of the external parameters that define the appropriate action. For tasks where precision is at a premium, performance would be optimal if no noise were added in movement planning and execution: motor output would be as accurate as possible given the quality of sensory inputs. We have used visually-guided smooth pursuit eye movements in primates as a testing ground for this notion of optimality. In response to repeated presentations of identical target motions, nearly 92\% of the variance in eye trajectory can be accounted for as a consequence of errors in sensory estimates of the speed, direction and timing of target motion, plus a small background noise that is observed both during eye movements and during fixations. The magnitudes of the inferred sensory errors agree with the observed thresholds for sensory discrimination by perceptual systems, suggesting that these very different neural processes are limited by the same noise sources. Computing the signal to noise ratio of pursuit movements allows us to estimate a ``behavioral threshold'' akin to a threshold for reliable perceptual discrimination of a change in target motion. We find that pursuit thresholds agree quite well with perceptual thresholds throughout the sensory-driven period of movement initiation. These results suggest that pursuit can be a reliable read-out of the evolving sensory estimate of target motion. [Preview Abstract] |
Tuesday, March 16, 2010 9:12AM - 9:48AM |
H7.00003: In silico evolution of biochemical networks Invited Speaker: We use computational evolution to select models of genetic networks that can be built from a predefined set of parts to achieve a certain behavior. Selection is made with the help of a fitness defining biological functions in a quantitative way. This fitness has to be specific to a process, but general enough to find processes common to many species. Computational evolution favors models that can be built by incremental improvements in fitness rather than via multiple neutral steps or transitions through less fit intermediates. With the help of these simulations, we propose a kinetic view of evolution, where networks are rapidly selected along a fitness gradient. This mathematics recapitulates Darwin's original insight that small changes in fitness can rapidly lead to the evolution of complex structures such as the eye, and explain the phenomenon of convergent/parallel evolution of similar structures in independent lineages. We will illustrate these ideas with networks implicated in embryonic development and patterning of vertebrates and primitive insects. [Preview Abstract] |
Tuesday, March 16, 2010 9:48AM - 10:24AM |
H7.00004: Information processing in small gene regulatory networks and cascades Invited Speaker: Many of the biological networks inside cells can be thought of as transmitting information from the inputs (e.g., the concentrations of transcription factors or other signaling molecules) to their outputs (e.g., the expression levels of various genes). On the molecular level, the relatively small concentrations of the relevant molecules and the intrinsic randomness of chemical reactions provide sources of noise that set physical limits on this information transmission. Given these limits, not all networks perform equally well, and maximizing information transmission provides a optimization principle from which we might hope to derive the properties of real regulatory networks. I will discuss the properties of specific small networks that can transmit the maximum information. Concretely, I will show how the form of molecular noise drives predictions not just of the qualitative network topology but also the quantitative parameters for the input/output relations at the nodes of the network. In an attempt to link these general theoretical considerations to real biological systems, I will illustrate the predictions on the example of transmission of positional information in the early development of the fly embryo. [Preview Abstract] |
Tuesday, March 16, 2010 10:24AM - 11:00AM |
H7.00005: Predictive information in the retina Invited Speaker: Prediction is important for almost all modes of behavior and our research focuses on how a population of neurons implements predictive computations. We have examined how groups of retinal ganglion cells (RGCs) encode predictive information in their collective firing patterns. Predictive information is defined here as the mutual information between firing patterns across several cells in the retina at a particular time, and the firing patterns of the same neurons at a time $\Delta t$ in the future. Put simply, we are asking how well the firing of the retina `now' specifies the firing of the retina in the future. We find substantial predictive information in groups of retinal ganglion cells that grows with the number of neurons pooled. This predictive information is due, in part, to the intrinsic firing properties of the ganglion cells, as well as to correlations in the stimulus. We attempt to disentangle these effects by examining responses to temporally uncorrelated while noise stimuli. We find that roughly half of the predictive information we observe can be accounted for by intrinsic properties of RGCs, while the remaining half is induced by stimulus correlations. To assess what collective properties of ganglion cell firing account for the observed predictive information, we break correlations between cells and within cells in time. We find that the predictive information in groups of ganglion cells outstretches the summed contribution from individual cells' predictive capacities, leading to substantial synergy in larger groups of RGCs. We also assess whether the way in which the retina encodes stimulus information is optimized for prediction. Preliminary evidence suggests that the retina does indeed compress information about past stimuli such that information about the future is maximally preserved. [Preview Abstract] |
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