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3_31_09_LinearSystemIdentification_1

3_31_09_LinearSystemIdentification_1 - A recurrent network...

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A recurrent network is a feedforward network with a recurrent synaptic weight matrix.
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Some neuronal tissues are so massive and complex that network analysis is not too useful.
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Perception is a constructive process that depends on both the stimulus information and the mental structure of the perceiver.
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Even when network analysis is not useful, we can still comprehend aspects of brain functions with models that are more generic.
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David Marr’s three levels of understanding computations.
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In a black-box model, we try to describe a system well enough to predict its responses without knowing what is inside the system.
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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.
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The impulse response D(t) is the reaction to a very short stimulus at time zero. One can use this model to estimate (rest(t)) the response of a linear system to stimulus s(t): r est t ( ) = r 0 + d τ D τ ( ) s t τ ( ) 0
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