University of Minnesota Ph.D. dissertation. July 2009. Major: Mathematics. Advisor: Duane Q. Nykamp. 1 computer file (PDF); x, 104 pages, appendices A-B. Ill. (some col.)We present an approach for using kinetic theory to capture first and second order statistics of neuronal activity. We coarse grain neuronal networks into populations of neurons and calculate the population average firing rate and output cross-correlation in response to time varying correlated input. We initially derive coupling equations for the populations based only on first and second order statistics of neuronal activity and the network connectivity. This coupling scheme is based on the hypothesis that second order statistics of the network connectivity are sufficient t...
Thesis (Ph.D.)--University of Washington, 2015How does the activity of populations of neurons encode...
textabstractCoincident firing of neurons projecting to a common target cell is likely to raise the p...
Mean-field descriptions of neuronal networks yield stabilityconstraints that guide efficient model d...
Abstract. We present a detailed theoretical framework for statistical descriptions of neuronal netwo...
In the first part of this tutorial, we introduce the mathematical tools to determine firing statisti...
We review a statistical physics approach for reduced descriptions of neuronal network dynamics. From...
I would like to express the deepest appreciation to my supervisor, Professor Du-ane Q. Nykamp, for h...
Pairwise correlations between the activities of neurons exhibittime-dependent modulations with respe...
Uncovering principles of information processing in neural systems continues to be an active field of...
Uncovering principles of information processing in neural systems continues to be an active field of...
Novel experimental techniques reveal the simultaneous activity of larger and larger numbers of neuro...
<div><p>Novel experimental techniques reveal the simultaneous activity of larger and larger numbers ...
When presented with a task or stimulus, the ongoing activity in the brain is perturbed in order to p...
<p>Each neuron in population receives randomly drawn excitatory inputs with weight , randomly dra...
The topic of this dissertation is the study of the emergence of higher-order correlations in recurre...
Thesis (Ph.D.)--University of Washington, 2015How does the activity of populations of neurons encode...
textabstractCoincident firing of neurons projecting to a common target cell is likely to raise the p...
Mean-field descriptions of neuronal networks yield stabilityconstraints that guide efficient model d...
Abstract. We present a detailed theoretical framework for statistical descriptions of neuronal netwo...
In the first part of this tutorial, we introduce the mathematical tools to determine firing statisti...
We review a statistical physics approach for reduced descriptions of neuronal network dynamics. From...
I would like to express the deepest appreciation to my supervisor, Professor Du-ane Q. Nykamp, for h...
Pairwise correlations between the activities of neurons exhibittime-dependent modulations with respe...
Uncovering principles of information processing in neural systems continues to be an active field of...
Uncovering principles of information processing in neural systems continues to be an active field of...
Novel experimental techniques reveal the simultaneous activity of larger and larger numbers of neuro...
<div><p>Novel experimental techniques reveal the simultaneous activity of larger and larger numbers ...
When presented with a task or stimulus, the ongoing activity in the brain is perturbed in order to p...
<p>Each neuron in population receives randomly drawn excitatory inputs with weight , randomly dra...
The topic of this dissertation is the study of the emergence of higher-order correlations in recurre...
Thesis (Ph.D.)--University of Washington, 2015How does the activity of populations of neurons encode...
textabstractCoincident firing of neurons projecting to a common target cell is likely to raise the p...
Mean-field descriptions of neuronal networks yield stabilityconstraints that guide efficient model d...