# Pavlov Learning Machines

arXiv: https://arxiv.org/abs/2207.00790 · HTML: https://mlsanity.com/paper/2207.00790/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 35 references that are in the map.
- First posted on arXiv 2 Jul 2022.

## Builds on (most cited first, 4 of 4)

- [What is the State of Neural Network Pruning?](https://mlsanity.com/paper/2003.03033/) (2020)
- [Importance Estimation for Neural Network Pruning](https://mlsanity.com/paper/1906.10771/) (2019)
- [Dreaming neural networks: forgetting spurious memories and reinforcing pure ones](https://mlsanity.com/paper/1810.12217/) (2018)
- [Recurrent neural networks that generalize from examples and optimize by dreaming](https://mlsanity.com/paper/2204.07954/) (2022)

## Abstract (arXiv)

As well known, Hebb's learning traces its origin in Pavlov's Classical Conditioning, however, while the former has been extensively modelled in the past decades (e.g., by Hopfield model and countless variations on theme), as for the latter modelling has remained largely unaddressed so far; further, a bridge between these two pillars is totally lacking. The main difficulty towards this goal lays in the intrinsically different scales of the information involved: Pavlov's theory is about correlations among concepts that are (dynamically) stored in the synaptic matrix as exemplified by the celebrated experiment starring a dog and a ring bell; conversely, Hebb's theory is about correlations among pairs of adjacent neurons as summarized by the famous statement em neurons that fire together wire together. In this paper we rely on stochastic-process theory and model neural and synaptic dynamics via Langevin equations, to prove that -- as long as we keep neurons' and synapses' timescales largely split -- Pavlov mechanism spontaneously takes place and ultimately gives rise to synaptic weights that recover the Hebbian kernel.