Soft Matching Distance: A metric on neural representations that captures single-neuron tuning

Understand

Common measures of neural representational (dis)similarity are designed to be insensitive to rotations and reflections of the neural activation space.

• Motivated by the premise that the tuning of individual units may be important, there has been recent interest in developing stricter notions of representational (dis)similarity that require neurons to be individually matched across networks.
• When two networks have the same size (i.e.
• same number of neurons), a distance metric can be formulated by optimizing over neuron index permutations to maximize tuning curve alignment.