Linking the computational structure of variance adaptation to biophysical mechanisms
Abstract/Contents
- Abstract
- Neurons have a limited dynamic range. To more efficiently encode the large range of natural inputs, neural circuits adapt by dynamically changing their output range as a function of the input statistics. Variance adaptation provides an informative example of this process, whereby neurons change their response characteristics as a function of variance of their input. When their input distribution changes, sensory systems shift and scale their response curves to efficiently cover the new range of input values and they focus on different segments of the frequency spectrum, for example by choosing to average out the noise in a low signal-to-noise ratio environment by low-pass filtering their input and sacrificing resolution. In multiple sensory systems, adaptation to the variance of a sensory input changes the sensitivity, kinetics and average response over timescales ranging from < 100 ms to tens of seconds. Here we present a simple biophysically relevant model of retinal contrast adaptation that accurately captures both the membrane potential response and all adaptive properties. The adaptive component of this model is a first-order kinetic process of the type used to describe ion channel gating and synaptic transmission. We conclude that all adaptive dynamics can be accounted for by depletion of a signaling mechanism, and that contrast adaptation can be explained as adaptation to the mean of a thresholded signal. A diverse set of adaptive properties that implement theoretical principles of efficient coding can be generated by a single type of molecule or synapse with just a few microscopic states. The LNK model helps to highlight important aspects of adaptation by letting us focus on individual computational blocks separately. By using the LNK model, we investigate the source of the adaptive process in On-Off retinal ganglion cells, which show strong changes in their kinetics as a function of contrast. By analyzing properties of the LNK model, we conclude that most of the adaptive effect is due to differences in the threshold of the two pathways, with a smaller contribution from different adaptive kinetics. Adaptive temporal decorrelation in the retina arises due to differential thresholding in two parallel neural pathways.
Description
| Type of resource | text |
|---|---|
| Form | electronic; electronic resource; remote |
| Extent | 1 online resource. |
| Publication date | 2011 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Associated with | Ozuysal, Yusuf |
|---|---|
| Associated with | Stanford University, Department of Electrical Engineering |
| Primary advisor | Baccus, Stephen A |
| Thesis advisor | Baccus, Stephen A |
| Thesis advisor | Boahen, Kwabena (Kwabena Adu) |
| Thesis advisor | Shenoy, Krishna V. (Krishna Vaughn) |
| Advisor | Boahen, Kwabena (Kwabena Adu) |
| Advisor | Shenoy, Krishna V. (Krishna Vaughn) |
Subjects
| Genre | Theses |
|---|
Bibliographic information
| Statement of responsibility | Yusuf Ozuysal. |
|---|---|
| Note | Submitted to the Department of Electrical Engineering. |
| Thesis | Thesis (Ph.D.)--Stanford University, 2011. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2011 by Yusuf Ozuysal
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