Figure 5.

Architecture of the feature integration and clustering processes. The schematic illustrates the various stages involved in segregated the acoustic patterns represented in the feature analysis stage. The incoming sensory inputs I(t) are compared to predicted patterns ${\widehat{I}}^{\mathrm{A}}\left(t\right)$ and ${\widehat{I}}^{\mathrm{B}}\left(t\right)$. The features which are most consistent with ${\widehat{I}}^{\mathrm{A}}\left(t\right)$ are “passed through” the stream A branch into a cortical integrative array. This neural cluster plays a dual role: it accumulates the information and outputs a representation of stream A (Y^A(t)) and it uses the available information (I(t),Y(t)) to update the current memory trace of stream A via a Kalman-filter estimation. This information is in turn used to build an expectation about the next input ${\widehat{I}}^{\mathrm{A}}(t+1)$, hence closing the feedback loop. The upper (yellow) panel indicates that a similar process takes place over a second cortical cluster (B), which tracks the evolution of another stream in the environment.