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High-probability grants
According to our matching algorithm, Marc D. Binder is the likely recipient of the following grants.
| Years |
Recipients |
Code |
Title / Keywords |
Matching
score |
| 1982 — 1985 |
Binder, Marc [⬀] |
N/AActivity Code Description:
No activity code was retrieved: click on the grant title for more information |
Central Actions of Muscle Afferents @ University of Washington |
0.915 |
| 2000 — 2003 |
Binder, Marc [⬀]
Powers, Randall (co-PI) [⬀] |
N/AActivity Code Description:
No activity code was retrieved: click on the grant title for more information |
Nonlinear Systems Analysis of Spike Encoding in Motoneurons @ University of Washington
"Nonlinear Systems Analysis of Spike Encoding in Montoneurons"
Individual nerve cells (neurons) transform the chemical and electrical signals they receive from other neurons into a series of electrical impulses or spikes. The frequency and pattern of these spikes forms the "neural code" by which information is transmitted throughout a network or system of neurons. Thus far, descriptions of the basic input-output transform of neurons, called spike encoding, have been largely limited to steady-state conditions in which the input signals that the cell receives are held constant. The objective of this proposed research program is to derive a general, quantitative description of spike encoding that will apply to both steady-state and dynamic conditions in which the input signals vary as they do under normal physiological conditions.
In the proposed experiments, the responses of mammalian neurons to brief injected current transients will be measured. The injected current transients are constructed to mimic real inputs as they appear in the cell body of a neuron. Non-linear systems identification procedures originally developed for electrical engineering applications will be used to characterize how these input signals affect the generation of spikes by the neuron. The advantage of this approach is that it yields a basic input-output function that is not as computationally complex as those derived from more detailed neuron models, but still accurately reproduces a wide range of neural behaviors. This compact input-output function can be then incorporated into the neural elements used in models operating at the network and systems levels, increasing the degree to which such models can accurately represent their biological counterparts.
|
0.915 |