Ph.D Thesis

Ph.D StudentMinerbi Amir
SubjectLong-Term Relationships between Network Activity, Synaptic
Tenacity and Synaptic Remodeling in Networks of
Cortical Neurons
DepartmentDepartment of Medicine
Supervisor PROF. Noam Ziv
Full Thesis text - in Hebrew Full thesis text - Hebrew Version


Neuronal networks are composed of neurons connected to each other by specialized junctions known as synapses. It is widely believed that activity-dependent modification of individual synaptic connections - synaptic plasticity -is a fundamental mechanism for modifying the function of neuronal networks in a physiologically relevant manner. In this project we aimed to examine long-term interrelationships between changes in network function and changes in synaptic connections within the same networks.  To that end, we developed a system for continuously imaging the structural dynamics of individual synapses over many days, while recording network activity in the same preparations.

In the first set of experiments we studied the effects of the neuromodulator dopamine on network activity and on measures of its synaptic population. We found that dopamine, even at low concentrations, caused a transient decrease in action potential frequencies, and concomitantly affected synaptic structural characteristics: Increases in mean synaptic size and a broadening of synaptic size distributions were observed.

Next, we characterized the dynamics of synaptic sizes in spontaneously active networks and after manipulations of network activity. We found that in spontaneously active networks, distributions of synaptic sizes were generally stable over days. Following individual synapses revealed, however, that the apparently static distributions were actually steady states of synapses exhibiting continual and extensive remodeling. In active networks, large synapses tended to grow smaller, whereas small synapses tended to grow larger, mainly during periods of particularly synchronous activity. Suppression of network activity only mildly affected the magnitude of synaptic remodeling, but dependence on synaptic size was lost, leading to the broadening of synaptic size distributions and increases in mean synaptic size. From the perspective of individual neurons, activity drove changes in the relative sizes of their excitatory inputs, but such changes continued, albeit at lower rates, even when network activity was blocked.

Finally we developed a numerical model of synaptic size behavior and its dependence on activity levels, based on the assumptions of an activity-driven ‘converging force’ that partially counteracts the random drift of synaptic sizes. The model enabled us to simulate aspects of synaptic size population characteristics under a range of activity levels.

Our findings show that activity strongly drives synaptic remodeling, but they also show that significant remodeling occurs spontaneously. Whereas such spontaneous remodeling provides an explanation for "synaptic homeostasis" like processes, it also raises significant questions concerning the reliability of individual synapses as sites for persistently modifying network function.