Optimization of error distributions as a design principle for neural representations
Neural circuits encode and transmit information to support a diversity of tasks. Such neural coding is corrupted by noise and uncertainty, which in turn results in different magnitudes of error in the signals that are received by downstream regions. Therefore, even in the same task and given a fixed distribution of input signals, the performance of a neural code is characterized by a distribution of errors. Different task demands might necessitate distributions with different properties; for example, a task that requires avoiding failure at all costs might necessitate coding schemes that minim
Sat, Mar 19, 2022