Ph.D Thesis

Ph.D StudentDvorkin Roman
SubjectThe Relative Parts of Specific Activity Histories and
Spontaneous Processes in Synaptic Remodeling in
Ex-Vivo Networks of Cortical Neurons
DepartmentDepartment of Medicine
Supervisor PROF. Noam Ziv
Full Thesis textFull thesis text - English Version


The idea that synaptic properties are defined by specific pre- and postsynaptic activity histories is one of the oldest and most influential tenets of contemporary neuroscience. Recent studies also indicate, however, that synaptic properties often change spontaneously, even in the absence of specific activity patterns or any activity whatsoever. What, then, are the relative contributions of activity-history dependent and independent processes to changes synapses undergo? To compare the relative contributions of these processes we imaged, in spontaneously active networks of cortical neurons, glutamatergic synapses formed between the same axons and neurons or dendrites, under the assumption that their similar activity histories should result in similar size changes over timescales of days. The size covariance of such commonly innervated (CI) synapses was then compared to that of synapses formed by different axons (non-CI synapses) which differed in their activity histories. We found that the size covariance of CI synapses was greater than that of non-CI synapses; yet overall size covariance of CI synapses was rather modest.  Moreover, momentary and time-averaged sizes of CI synapses correlated rather poorly, in perfect agreement with published electron microscopy-based measurements of mouse cortex synapses. A conservative estimate suggested that ~40% of the observed size remodeling was attributable to specific activity histories, whereas ~10% and ~50% were attributable to cell-wide and spontaneous remodeling processes, respectively. These findings demonstrate that histories of naturally occurring activity patterns can direct glutamatergic synapse size remodeling but also suggest that the contributions of spontaneous, possibly stochastic processes are at least as great.