Ensemble perspective for understanding temporal credit assignment
arXiv:2102.03740 · doi:10.1103/PhysRevE.107.024307
Abstract
Recurrent neural networks are widely used for modeling spatio-temporal sequences in both nature language processing and neural population dynamics. However, understanding the temporal credit assignment is hard. Here, we propose that each individual connection in the recurrent computation is modeled by a spike and slab distribution, rather than a precise weight value. We then derive the mean-field algorithm to train the network at the ensemble level. The method is then applied to classify handwritten digits when pixels are read in sequence, and to the multisensory integration task that is a fundamental cognitive function of animals. Our model reveals important connections that determine the overall performance of the network. The model also shows how spatio-temporal information is processed through the hyperparameters of the distribution, and moreover reveals distinct types of emergent neural selectivity. To provide a mechanistic analysis of the ensemble learning, we first derive an analytic solution of the learning at the infinitely-large-network limit. We then carry out a low-dimensional projection of both neural and synaptic dynamics, analyze symmetry breaking in the parameter space, and finally demonstrate the role of stochastic plasticity in the recurrent computation. Therefore, our study sheds light on mechanisms of how weight uncertainty impacts the temporal credit assignment in recurrent neural networks from the ensemble perspective.
arXiv comment61 pages, 33 figures, revised version to journal
References in corpus (5)
- Linking connectivity, dynamics and computations in low-rank recurrent neural networks
- Edge of chaos and avalanches in neural networks with heavy-tailed synaptic weight distribution
- Minimal model of permutation symmetry in unsupervised learning
- Learning credit assignment
- Statistical physics of unsupervised learning with prior knowledge in neural networks