Some of the most intriguing, powerful, and complex features of biological systems arise from the collective properties of large networks of relatively simple elements. This is particularly true in neural systems, where the bewilderingly complex behavior of whole organisms emerges from the comparatively simple activities of billions of neurons. Unfortunately, the rigorous study of neural collective behavior is very challenging from both a theoretical and an experimental perspective. Experimentally, studying collective properties requires the ability to record from large numbers of neurons in a connected network with single cell resolution. Technologies suitable for this task have only recently begun to emerge, and are still in their early stages, with an enormous amount of work left to be done. Theoretically, the study of collective systems can be computationally intractable if approached naively, as the number of collective states of a system tends to increase exponentially with the number of elements in the system. Efforts to render this problem more tractable are also in their early stages.
This work presents four novel but related advances on both the experimental and theoretical fronts. The retina is used as a model system, but most of the technologies and methods presented here are widely applicable to other neural systems and in some cases to biological systems in general. Part I presents an improvement to microelectrode array technology which enables very high quality recording of almost all the spiking activity in a small patch of retinal ganglion cells. Part II presents a new signal processing technique for identifying and differentiating the spikes recorded using the device described in part I, enabling us to record from almost all of the 200+ cells in a 0.5 x 0.5 mm patch of retina. I also discuss cell labeling experiments verifying that we have in fact recorded from almost all the cells in this patch. In part III, we use the data collected in parts I and II to empirically test a set of models of collective neural activity (data-driven MaxEnt models) at much larger scales than had been possible before. We find that the model performs well, and we also develop a more advanced model which captures the neural behavior even better than the Ising model. We find strong preliminary evidence of critical behavior in both the Ising model and the newly developed model, which confirms a key theoretical prediction made several years earlier.
Finally, in part IV, we present early progress towards a completely novel recording technology, designed to improve the capabilities and the scale of intracellular recording, eventually enabling its application to network-scale studies. The new device is a customized patch clamp electrode, and opens up the eventual possibility of recording from many cells in a small region of tissue simultaneously as well as performing internal dialysis in tissue. Although not yet fully mature, this device potentially represents a new frontier in intracellular recording.
|Advisor:||Berry, Michael J., Bialek, William|
|Commitee:||Berry, Michael J., Bialek, William, Petta, Jason, Shaevitz, Joshua|
|School Location:||United States -- New Jersey|
|Source:||DAI-B 73/02, Dissertation Abstracts International|
|Keywords:||Collective behavior, Intracellular recording, Neural circuits|
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