Publication Date


Document Type


Committee Members

Kathrin Engisch (Advisor); Mark Rich (Committee Member), David Ladle (Committee Member), Michael Raymer (Committee Member), Courtney Sulentic (Committee Member)

Degree Name

Doctor of Philosophy (PhD)


Synaptic plasticity, the ability of neurons to modulate their inputs in response to changing stimuli, occurs in two forms which have opposing effects on synaptic physiology. Hebbian plasticity induces rapid, persistent changes at individual synapses in a positive feedback manner. Homeostatic plasticity is a negative feedback effect that responds to chronic changes in network activity by inducing opposing, network-wide changes in synaptic strength and restoring activity to its original level. The changes in synaptic strength can be measured as changes in the amplitudes of miniature post-synaptic excitatory currents (mEPSCs). Together, the two forms of plasticity underpin nervous system functions such as movement, learning and memory, and perception, while preventing pathological states of hyper- or hypoactivity that could occur if network activity were not maintained. The current hypothesis of homeostatic plasticity states that mEPSC amplitudes exhibit uniform multiplicative scaling, a transformation in which the amplitudes are scaled up or down globally by a multiplicative factor. This hypothesis constrains the possible mechanism of homeostatic plasticity, which remains unknown despite intensive study. Here, we compare an experimental data set previously collected in our laboratory to the results of an empirical simulation of uniform multiplicative scaling and conclude that the homeostatic increase in mEPSC amplitudes in our data is not uniform. We develop and validate a novel method, comparative standardization, for calculating the scaling transformation between treated and untreated mEPSC amplitudes and identifying the transformation as either uniform, divergent, or convergent. When applied to our experimental data, comparative standardization finds divergent scaling, in which the homeostatic effect increases with synaptic strength, causing the control and treated mEPSC amplitude distributions to diverge. The divergent scaling transformation computed by comparative standardization is also more accurate than the transformations computed by existing methods. Finally, we generalize our findings by applying our approach to several additional homeostatic plasticity data sets obtained from our collaborators: All additional data exhibit divergent scaling, and comparative standardization consistently outperforms both existing methods for computing the homeostatic scaling transformation.

Page Count


Department or Program

Department of Biomedical Sciences

Year Degree Awarded