\contentsline {chapter}{Acknowledgments}{iii}
\contentsline {chapter}{Abstract}{v}
\contentsline {chapter}{\numberline {1}Introduction}{1}
\contentsline {section}{\numberline {1.1}Overview of dendrites and their function}{2}
\contentsline {section}{\numberline {1.2}Computation, Noise and Information}{9}
\contentsline {section}{\numberline {1.3}Synaptic efficacy}{16}
\contentsline {section}{\numberline {1.4}Epilogue}{20}
\contentsline {chapter}{\numberline {2}Methods}{21}
\contentsline {section}{\numberline {2.1}Biophysics of electrical activity of neurons}{21}
\contentsline {subsection}{\numberline {2.1.1}Passive cable theory}{21}
\contentsline {subsection}{\numberline {2.1.2}Cable theory for non-uniform passive membrane conductance}{22}
\contentsline {subsection}{\numberline {2.1.3}Compartmental models}{22}
\contentsline {section}{\numberline {2.2}Analysis of spike-trains using information theoretic quantities}{24}
\contentsline {subsection}{\numberline {2.2.1}Background}{24}
\contentsline {subsection}{\numberline {2.2.2}Spike trains as Stochastic Processes and Information Sources}{25}
\contentsline {subsection}{\numberline {2.2.3}Estimation of the entropy of a spike train: The direct method}{26}
\contentsline {subsection}{\numberline {2.2.4}Estimation of the entropy of a spike train: Bayesian method}{27}
\contentsline {subsection}{\numberline {2.2.5}Markov Processes}{29}
\contentsline {paragraph}{Higher order Markov processes}{29}
\contentsline {subsection}{\numberline {2.2.6}Representation of a Markov Source as Suffix Trees}{30}
\contentsline {subsection}{\numberline {2.2.7}Estimation of the entropy rate for a known model}{31}
\contentsline {subsection}{\numberline {2.2.8}Entropy estimation for an unknown model: The Context Tree Weighting}{32}
\contentsline {subsection}{\numberline {2.2.9}Summary of entropy estimation procedure and implementation notes}{34}
\contentsline {subsection}{\numberline {2.2.10}Estimation of Mutual Information}{34}
\contentsline {subsection}{\numberline {2.2.11}Appendix}{35}
\contentsline {paragraph}{Stochastic process and Information source}{35}
\contentsline {paragraph}{conditional entropy}{36}
\contentsline {chapter}{\numberline {3}Results}{37}
\contentsline {coltoctitle}{\numberline {}Signal transfer in passive dendrites with non-uniform membrane conductance}{39}
\contentsline {coltocauthor}{\numberline {}Michael London, Claude Meunier \& Idan Segev \\ \textit {The Journal of Neuroscience}, October 1, 1999, 19(19):8219-8233}{1}
\contentsline {coltoctitle}{\numberline {}Synaptic scaling in-vitro and in-vivo}{82}
\contentsline {coltocauthor}{\numberline {}Michael London \& Idan Segev \\ \textit {Nature Neuroscience}, September 2001, 4(9):853-854}{1}
\contentsline {coltoctitle}{\numberline {}Synaptic information efficacy: Bridging the cleft between biophysics and function}{89}
\contentsline {coltocauthor}{\numberline {}Michael London, Adi Shribman \& Idan Segev \\ Submitted for publication in \textit {Nature Neuroscience}, 2001}{1}
\contentsline {section}{Synaptic Information Efficacy: Preliminary experimental results}{111}
\contentsline {subsection}{Extracellular stimulation}{111}
\contentsline {subsection}{Methods}{111}
\contentsline {paragraph}{Slice preparation}{111}
\contentsline {paragraph}{Electrophysiology}{112}
\contentsline {paragraph}{Histology}{113}
\contentsline {chapter}{\numberline {4}Summary and Discussion}{114}
\contentsline {chapter}{References}{118}