Predictive coding networks for temporal prediction
Fig 2
Possible neural implementations of temporal predictive coding.
A: Potential neural circuit implementing the iterative recurrent predictive coding algorithm. For simplicity, we have depicted each neural ‘layer’ as possessing only two neurons. B: Version of the model where the prediction errors are represented by the difference in membrane potential in soma and at apical dendrites (depicted as ellipses). C: Neural circuitry required to implement the single-iteration predictive coding algorithms. This model no longer includes a separate set of neurons explicitly storing the estimate of the previous timestep, but instead, the temporal prediction errors are computed naturally through recurrent connections. For simplicity, we omitted the control inputs Buk, which can be implemented in a similar way to the recurrent inputs to the error neurons or apical dendrites.