Understanding Sample Efficiency in Predictive Coding
Predictive Coding (PC) is an influential account of cortical learning. Much of recent work has focused on comparing PC to Backpropagation (BP) to find whether PC offers any advantages. Small scale experiments show that PC enables learning that is more sample efficient and effective in many contexts, though a thorough theoretical understanding of the phenomena remains elusive. To address this, we quantify the efficiency of learning in BP and PC through a metric called ``target alignment'', which measures how closely the change in the output of the network is aligned to the output prediction error. We then derive and empirically validate analytical expressions for target alignment in Deep L...
| Authors | Gaspard Oliviers, Elene Lominadze, Rafal Bogacz |
|---|---|
| Primary category | cs.LG |
| Published | 12 May 2026 |
| Last updated at source | 10 June 2026 |
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