Contact details

Department of Mathematics
University of Utah
155 South 1400 East
Salt Lake City
Utah 84112

Tel.: 801 585 1633
Fax.: 801 581 4148

Email: bressloff@math.utah.edu


Mathematical Neuroscience and Theoretical Biophysics

I am a Professor of Mathematics in the Department of Mathematics at the University of Utah, where I am a faculty member of the Mathematical Biology Group and the Brain Institute. I am also a Visiting Professor at the Mathematical Institute, University of Oxford.

My main research interests lie in the areas of mathematical neuroscience and theoretical biophysics. The particular focus of my work in neuroscience is to understand how the brain functions as a complex dynamical system at multiple spatial and temporal scales, both in healthy and diseased brains. From the biological perspective, this involves many different levels of description from the molecular basis of memory and learning at an individual synapse to the large-scale structure of cortex responsible for higher cognitive function. From the modeling perspective, the work draws upon a wide range of techniques in applied mathematics and theoretical physics including dynamical systems theory, differential equations, pattern formation and nonlinear wave theory, non-equilibrium statistical physics, biophysics and the theory of self-organizing systems. My interests in theoretical biophysics focus on stochastic models of intracellular transport, including molecular motors, protein receptor trafficking, secretory trafficking, ion channels, and intracellular waves.

The Journal of Mathematical Neuroscience The Journal of Mathematical Biology Physical Review E Biological Cybernetics

Books

NEW! Paul C. Bressloff Waves in Neural Media: From Single Neurons to Neural Fields Lecture Notes on Mathematical Modelling in the Life Sciences (Springer) Due: October 31 (2013)

Stephen Coombes and Paul C. Bressloff(eds.) Bursting: the Genesis of Rhythm in the Nervous System World Scientific (2005)


Reviews

NEW! Paul C. Bressloff and J. Newby. Stochastic models of intracellular transport Rev. Mod. Phys. 85 135-196 (2013)

NEW! Paul C. Bressloff. Spatiotemporal Dynamics of Continuum Neural Fields J. Phys. A 45 (2012) 033001

P. C. Bressloff. Lectures in Mathematical Neuroscience In: Mathematical Biology, IAS/Park City Mathematical Series.(M. A. Lewis, M. A. J. Chaplain, J. P. Keener and P. K. Maini (eds) 14 293-398 (American Mathematical Society, 2009).

P. C. Bressloff. Pattern formation in visual cortex. Les Houches Lectures in Neurophysics (2005).

P. C. Bressloff and S. Coombes. Physics of the extended neuron. Int. J. Mod. Phys. B 11:2343-2393 (1997).


Selected papers (2001-)

Intracellular waves and secretory trafficking

P. C. Bressloff. Propagation of CaMKII translocation waves in heterogeneous spiny dendrites. J. Math. Biol. 66 1499-1525 (2013).

P. C. Bressloff. Two-pool model of cooperative vesicular transport Phys. Rev. E 86 0319111 (2012).

B. A. Earnshaw and P. C. Bressloff. Diffusion-activation model of CaMKII translocation waves in dendrites. J. Comput. Neurosci. 28 77-89 (2010).

S. Coombes and P. C. Bressloff, Saltatory waves in the spike-diffuse-spike model of active dendrites. Phys. Rev. Lett. 91:028102 (2003).

Molecular motors and random intermittent search

P. C. Bressloff and J. M. Newby. Filling of a Poisson trap by a population of random intermittent searchers. Phys. Rev. E 85 031909 (2012).

P. C. Bressloff and J. Newby. Quasi-steady state analysis of motor-driven transport on a two-dimensional microtubular network. Phys. Rev. E 83 061139 (2011).

J. M. Newby and P. C. Bressloff. Random intermittent search and the tug-of-war model of motor-driven transport. J. Stat. Mech. P04104 (2010).

J. Newby and P. C. Bressloff. Local synaptic signalling enhances the stochastic transport of motor-driven cargo in neurons. Phys. Biol. 7 036004 (2010).

J. Newby and P. C. Bressloff. Directed intermittent search for a hidden target on a dendritic tree. Phys. Rev. E 80 021913 (2009).

P. C. Bressloff and J. Newby. Directed intermittent search for hidden targets. New J. Phys. 11 023033 (2009).

P. C. Bressloff, A stochastic model of intraflagellar transport Phys. Rev. E 73 061916 (2006).

Protein receptor trafficking and synaptic plasticity

V. M. Burlakov, N.Emptage, A.Goriely and P. C. Bressloff. Synaptic bistability due to nucleation and evaporation of receptor clusters. Phys. Rev. Lett. 108 028101 (2012).

P. C. Bressloff. Cable theory of protein receptor trafficking in a dendritic tree. Phys. Rev. E 79 041904 (2009).

P. C. Bressloff and B. A. Earnshaw. A dynamical corral model of protein trafficking in spines. Biophys. J. 96 1786-1802 (2009).

P. C. Bressloff, B. A. Earnshaw and M. J. Ward. Diffusion of protein receptors on a cylindrical dendritic membrane with partially absorbing traps. SIAM J. Appl. Math. 68 1223-1246 (2008).

P. C. Bressloff and B. A. Earnshaw. Diffusion-trapping model of receptor trafficking in dendrites. Phys. Rev. E 75: 041916 (2007)

B. A. Earnshaw and P. C. Bressloff, A biophysical model of AMPA receptor trafficking and its regulation during LTP/LTD. J. Neurosci. 26 12362-12373 (2006).

P. C. Bressloff, A stochastic model of protein receptor trafficking prior to synaptogenesis Phys. Rev. E 74 031910 (2006).

Stochastic population oscillators and noise-induced synchronization

P. C. Bressloff and Yi Ming Lai. Dispersal and noise: Various modes of synchrony in ecological oscillators. J. Math. Biol. In press (2013)

Y-M Lai, J. Newby and P. C. Bressloff. Effects of demographic noise on the synchronization of metacommunities by a fluctuating environment. Phys. Rev. Lett 107 118102 (2011).

P. C. Bressloff and Y-M Lai. Stochastic synchronization of neuronal populations with intrinsic and extrinsic noise. J. Math. Neurosci. 1 2 (2011).

W. H. Nesse, C. A. DelNegro and P. C. Bressloff. Oscillation regularity in noise-driven excitable systems with multi-timescale adaptation. Phys. Rev. Lett. 101 088101 (2008).

W. H. Nesse, A. Borisyuk and P. C. Bressloff. Fluctuation-driven rhythmogenesis in an excitatory neuronal network with slow adaptation. J. Comput. Neurosci. 25 317-333 (2008).

Stochastic neural networks and fields

P. C. Bressloff and J. Newby. Metastability in a stochastic neural network modeled as a velocity jump Markov process. SIAM J. Appl. Dyn. Systs. In press.

P. C. Bressloff and J.Wilkerson. Traveling pulses in a stochastic neural field model of direction selectivity. Front. Comp. Neurosci. 6 90 (14 p.) (2012)

P. C. Bressloff and M. A. Webber. Front Propagation in stochastic neural fields SIAM J. Appl. Dyn. Syst. 11 708-740 (2012).

P. C. Bressloff. Metastable states and quasicycles in a stochastic Wilson-Cowan model of neural population dynamics. Phys. Rev. E 82 051903 (2010).

P. C. Bressloff. Stochastic neural field theory and the system-size expansion. SIAM J. Appl. Math 70 1488-1521 (2009).

Adaptive neural fields and binocular rivalry

M. A. Webber and P. C. Bressloff. The effects of noise on binocular rivalry waves: a stochastic neural field model. J. Stat. Mech. (2012).

P. C. Bressloff and M. A. Webber. Neural field model of binocular rivalry waves. J. Comput. Neurosci. 32 233-252 (2012).

Z. P. Kilpatrick and P. C. Bressloff. Binocular rivalry in a competitive neural network with synaptic depression. SIAM J. Appl. Dyn. Syst. 9 1303-1347 (2010).

Z. P. Kilpatrick and P. C. Bressloff. Spatially structured oscillations in a 2D excitatory neuronal network with synaptic depression. J. Comput. Neurosci. 239 1048-1060 (2010).

Z. P. Kilpatrick and P. C. Bressloff. Stability of bumps in piecewise smooth neural fields with nonlinear adaptation. Physica D 239 1048-1060 (2010).

Waves propagation in continuum neural fields

P. C. Bressloff. From invasion to extinction in heterogeneous neural fields. J. Math. Neurosci. 2 art. 6 (2012).

Z. P. Kilpatrick, S. E. Folias and P. C. Bressloff. Traveling pulses and wave propagation failure in an inhomogeneous neural network. SIAM J. Appl. Dyn. Syst. 7 161-185 (2008).

S. E. Folias and P. C. Bressloff, Stimulus-locked waves and breathers in an excitatory neural network.SIAM J. Appl. Math 65:2067-2092 (2005).

S. E. Folias and P. C. Bressloff, Breathing pulses in an excitatory neural network. SIAM J. Appl. Dyn. Syst. 3,: 378-407(2004).

P. C. Bressloff and S. E. Folias, Front bifurcations in an excitatory neural network. SIAM J. Appl. Math. 65: 131-151 (2004).

P. C. Bressloff, Traveling fronts and wave propagation failure in an inhomogeneous neural network Physica D 155 :83-100 (2001).

Mathematical models of primary visual cortex

M. Galtier, O. Faugeras and P. C. Bressloff. Hebbian learning of recurrent connections: a geometrical perspective. Neural Comput. 24 2346-2383 (2012).

Shushruth, P. Mangapathy, J. M. Ichida, P. C. Bressloff, L. Schwabe and A. Angelucci. Strong recurrent networks compute the orientation-tuning of surround modulation in primate V1. J. Neurosci. 32 308-321 (2012).

P. C. Bressloff and A. M. Oster. A theory for the alignment of cortical feature maps during development. Phys. Rev. E 82 021920 (2010).

L. Schwabe, K. Obermayer, A. Angelucci and P. C. Bressloff. The role of feedback in shaping the extra-classical receptive field of cortical neurons: a recurrent network model J. Neurosci. 26 9117-9129 (2006).

P. C. Bressloff, Spontaneous symmetry breaking in self-organizing neural fields. Biol. Cybern. 93: 256-274 (2005).

P. C. Bressloff, Spatially periodic modulation of cortical patterns by long-range horizontal connections. Physica D 185:131-157 (2003).

P. C. Bressloff and J. D. Cowan, Spherical model of orientation and spatial frequency tuning in a cortical hypercolumn. Phil. Trans. Roy. Soc. B 358:1643-1667 (2003).

P. C. Bressloff and J. D. Cowan, An amplitude equation approach to contextual effects in primary visual cortex. Neural Comput. 14 :493-525 (2002).

P. C. Bressloff, J. D. Cowan, M. Golubitsky, P. J. Thomas and M. Wiener, Geometric visual hallucinations, Euclidean symmetry and the functional architecture of striate cortex Phil. Trans. Roy. Soc. B 40 :299-330 (2001).


Conferences and Workshops

Search and Exploration, Cargese Research Institute, April 25-30, 2011

SIAM Conference on Applications of Dynamical Systems, Snowbird, May 22 - 26, 2011

Spatio-Temporal Evolution Equations and Neural Fields, CIRM, Marseilles, October 24-28, 2011

Stochastic Modelling in Biological Systems, Oxford, March 18-23, 2012


Utah

Canyonlands

Dead Horse Point

Colorado River nr. Moab

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