Optimal Population Codes for Control and Estimation
This work addresses the need to account for specific tasks in sensory adaptation research, particularly for agents in control settings, though it appears incremental in clarifying existing theoretical distinctions.
The study tackled the problem of sensory adaptation in biological systems, showing that optimizing sensors for control tasks differs from optimizing for perception, even in simple setups, which aligns with recent experimental findings.
Agents acting in the natural world aim at selecting appropriate actions based on noisy and partial sensory observations. Many behaviors leading to decision mak- ing and action selection in a closed loop setting are naturally phrased within a control theoretic framework. Within the framework of optimal Control Theory, one is usually given a cost function which is minimized by selecting a control law based on the observations. While in standard control settings the sensors are assumed fixed, biological systems often gain from the extra flexibility of optimiz- ing the sensors themselves. However, this sensory adaptation is geared towards control rather than perception, as is often assumed. In this work we show that sen- sory adaptation for control differs from sensory adaptation for perception, even for simple control setups. This implies, consistently with recent experimental results, that when studying sensory adaptation, it is essential to account for the task being performed.