Identifying Learning Rules From Neural Network Observables (2010.11765v2)

Abstract: The brain modifies its synaptic strengths during learning in order to better adapt to its environment. However, the underlying plasticity rules that govern learning are unknown. Many proposals have been suggested, including Hebbian mechanisms, explicit error backpropagation, and a variety of alternatives. It is an open question as to what specific experimental measurements would need to be made to determine whether any given learning rule is operative in a real biological system. In this work, we take a "virtual experimental" approach to this problem. Simulating idealized neuroscience experiments with artificial neural networks, we generate a large-scale dataset of learning trajectories of aggregate statistics measured in a variety of neural network architectures, loss functions, learning rule hyperparameters, and parameter initializations. We then take a discriminative approach, training linear and simple non-linear classifiers to identify learning rules from features based on these observables. We show that different classes of learning rules can be separated solely on the basis of aggregate statistics of the weights, activations, or instantaneous layer-wise activity changes, and that these results generalize to limited access to the trajectory and held-out architectures and learning curricula. We identify the statistics of each observable that are most relevant for rule identification, finding that statistics from network activities across training are more robust to unit undersampling and measurement noise than those obtained from the synaptic strengths. Our results suggest that activation patterns, available from electrophysiological recordings of post-synaptic activities on the order of several hundred units, frequently measured at wider intervals over the course of learning, may provide a good basis on which to identify learning rules.

• The paper demonstrates that aggregate statistics from activations and weights can effectively differentiate various learning rules in artificial neural networks.
• It uses a virtual experimental design to simulate neuroscience experiments across diverse network architectures, learning rules, and training configurations.
• The findings indicate that activation patterns are more robust to noise and undersampling, offering practical insights for both neuroscience research and AI system design.

Overview of "Identifying Learning Rules From Neural Network Observables"

The paper "Identifying Learning Rules From Neural Network Observables" offers a unique approach to understanding neural learning mechanisms by simulating neuroscience experiments in AI settings, specifically using artificial neural networks (ANNs). It addresses one of the central questions in both neuroscience and AI: how learning rules are effectively determined and what indicators might be used to identify these rules in biological systems.

Methodology

The authors utilize a "virtual experimental" design in which idealized neuroscience experiments are performed on ANNs across diverse architectures, learning rules, loss functions, and initializations. They generate extensive datasets that encapsulate learning trajectories and aggregate statistics, measuring weights of layers, activations, and instantaneous layer-wise activity changes throughout training. These observables are analogous to synaptic strengths, post-synaptic activities, and paired-neuron input-output relations in biological systems.

Subsequent analysis involved training classifiers to identify distinct learning rules from the metrics obtained. The classifiers used were linear and simple non-linear, including SVM, Random Forest, and a Conv1D MLP. This methodology allowed the researchers to isolate specific observable statistics that proved most reliable for distinguishing between learning rules.

Findings

The paper demonstrates that various classes of learning rules can be differentiated using aggregate statistics of weights, activations, or instantaneous activity changes, independent of network architecture or loss function specifics. Notably, the statistics derived from activations were found to be more resilient to noise and undersampling compared with those from synaptic strengths, indicating their potential viability in a biological context.

Furthermore, the authors established that activation patterns captured through extensive electrophysiological recordings offer a promising basis to hypothesize and identify synaptic learning rules. A key insight is that sparse sampling across the learning trajectory proves robust for identifying learning rules, rather than focusing on consecutive portions of the trajectory.

Implications

The implications of this research extend into practical applications within neuroscience and AI. By demonstrating a method to discern learning rules purely based on observable measures, the authors suggest possible pathways for experimental designs in neuroscience aimed at identifying or rejecting proposed plasticity rules. The results highlight the potential for recording broad activation patterns over time, rather than focusing solely on synaptic strengths or neuron pairs, which could refine how neuroscientists approach their investigations into brain mechanisms of learning.

Similarly, in AI, the research provides insights into how artificial systems might be designed to more closely mirror biological processes, not only for the sake of achieving efficient learning but also for the goal of making artificial systems interpretable and explainable based on their internal dynamics.

Future Directions

Speculation regarding future developments in AI and computational neuroscience includes a refinement of techniques to assess observables in ANNs more finely, perhaps integrating better theory with empirical approaches as proposed by the authors. For neuroscience, leveraging findings from AI might inspire more complex experimental designs or sensor technologies aimed at capturing neural data in a manner similar to the simulated experiments. In AI, the trajectory of integrating biologically plausible learning rules within architectures might accelerate, fostering systems that operate more similarly to human cognition.

In conclusion, the paper, through its innovative approach, provides a deeper understanding of ANN-based metrics to examine and hypothesize about biological learning rules, advancing both theoretical and practical frameworks across disciplines.