3_31_09_LinearSystemIdentification_1

3_31_09_LinearSystem - Outline of the Lecture Outline of the Lecture Linear System Identification Linear System Identification Back-box Models

Info iconThis preview shows pages 1–10. Sign up to view the full content.

View Full Document Right Arrow Icon

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
This is the end of the preview. Sign up to access the rest of the document.

Unformatted text preview: Outline of the Lecture Outline of the Lecture Linear System Identification Linear System Identification Back-box Models Impulse Response Reverse Back-box Models Impulse Response Reverse Correlation Correlation Reverse Correlation Can Reverse Correlation Can Determine a Neural Linear- Determine a Neural Linear- system’s Impulse Response system’s Impulse Response A recurrent network is a feedforward network with a recurrent synaptic weight matrix. Some neuronal tissues are so massive and complex that network analysis is not too useful. Perception is a constructive process that depends on both the stimulus information and the mental structure of the perceiver. Even when network analysis is not useful, we can still comprehen d aspects of brain functions with models that are more generic. David Marr’s three levels of understanding computations. In a black-box model, we try to describe a system well enough to predict its responses without knowing what is inside the system. If the black box is linear, then we can describe the system fully with the impulse response, as any stimulus is a sum of impulses. The impulse response D(t) is...
View Full Document

This note was uploaded on 06/08/2009 for the course BME 575L taught by Professor Grzywacz during the Spring '09 term at USC.

Page1 / 23

3_31_09_LinearSystem - Outline of the Lecture Outline of the Lecture Linear System Identification Linear System Identification Back-box Models

This preview shows document pages 1 - 10. Sign up to view the full document.

View Full Document Right Arrow Icon
Ask a homework question - tutors are online