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Peter J. Thomas

Publications and source records attributed to Peter J. Thomas.

39 records · Page 3Linked to original sources

Phase Resetting in an Asymptotically Phaseless System: On the Phase Response of Limit Cycles Verging on a Heteroclinic Orbit

Rhythmic behaviors in neural systems often combine features of limit cycle dynamics (stability and periodicity) with features of near heteroclinic or near homoclinic cycle dynamics (extended dwell times in localized regions of phase space). Proximity of a limit cycle to one or more saddle equilibria can have a profound effect on the timing of trajectory components and response to both fast and slow perturbations, providing a possible mechanism for adaptive control of rhythmic motions. Reyn showed that for a planar dynamical system with a stable heteroclinic cycle (or separatrix polygon), small perturbations satisfying a net inflow condition will generically give rise to a stable limit cycle (Reyn, 1980; Guckenheimer and Holmes, 1983). Here we consider the asymptotic behavior of the infinitesimal phase response curve (iPRC) for examples of two systems satisfying Reyn's inflow criterion, (i) a smooth system with a chain of four hyperbolic saddle points and (ii) a piecewise linear system corresponding to local linearization of the smooth system about its saddle points. For system (ii), we obtain exact expressions for the limit cycle and the iPRC as a function of a parameter, mu>0, representing the distance from a heteroclinic bifurcation point. In the limit, as mu approaches zero, we find that perturbations parallel to the unstable eigenvector direction in a piecewise linear region lead to divergent phase response, as previously observed (Brown, Moehlis and Holmes (2004), Neural Computation). In contrast to previous work, we find that perturbations parallel to the stable eigenvector direction can lead to either divergent or convergent phase response, depending on the phase at which the perturbation occurs. In the smooth system (i), we show numerical evidence of qualitatively similar phase specific sensitivity to perturbation.

math.DS↗

A Lower Bound for the First Passage Time Density of the Suprathreshold Ornstein-Uhlenbeck Process

We prove that the first passage time density $ρ(t)$ for an Ornstein-Uhlenbeck process $X(t)$ obeying $dX=-βX dt + σdW$ to reach a fixed threshold $θ$ from a suprathreshold initial condition $x_0>θ>0$ has a lower bound of the form $ρ(t)>k \exp\left[-p e^{6βt}\right]$ for positive constants $k$ and $p$ for times $t$ exceeding some positive value $u$. We obtain explicit expressions for $k, p$ and $u$ in terms of $β$, $σ$, $x_0$ and $θ$, and discuss application of the results to the synchronization of periodically forced stochastic leaky integrate-and-fire model neurons.

math.PR↗

Information Maximization Fails to Maximize Expected Utility in a Simple Foraging Model

Information theory has explained the organization of many biological phenomena, from the physiology of sensory receptive fields to the variability of certain DNA sequence ensembles. Some scholars have proposed that information should provide the central explanatory principle in biology, in the sense that any behavioral strategy that is optimal for an organism's survival must necessarily involve efficient information processing. We challenge this view by providing a counterexample. We present an analytically tractable model for a particular instance of a perception-action loop: a creature searching for a food source confined to a one-dimensional ring world. The model incorporates the statistical structure of the creature's world, the effects of the creature's actions on that structure, and the creature's strategic decision process. The model takes the form of a Markov process on an infinite dimensional state space. To analyze it we construct an exact coarse graining that reduces the model to a Markov process on a finite number of "information states". This technique allows us to make quantitative comparisons between the performance of an information-theoretically optimal strategy with other candidate strategies on a food gathering task. We find that: 1. Information optimal search does not necessarily optimize utility (expected food gain). 2. The rank ordering of search strategies by information performance does not predict their ordering by expected food obtained. 3. The relative advantage of different strategies depends on the statistical structure of the environment, in particular the variability of motion of the source. We conclude that there is no simple relationship between information and utility. Behavioral optimality does not imply information efficiency, nor is there a simple tradeoff between gaining information about a food source versus obtaining the food itself.

q-bio.OT↗