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Living organisms perform multiple tasks, often using the same or shared neural networks. Such multifunctional neural networks are composed of neurons that contribute to different degrees in the different behaviors. In this work, we take a computational modeling approach to evaluate the extent to which neural resources are specialized or shared across different behaviors. To this end, we develop multifunctional feed-forward neural networks that are capable of performing three control tasks: inverted pendulum, cartpole balancing and single-legged walker. We then perform information lesions of individual neurons to determine their contribution to each task. Following that, we investigate the ability of two commonly used methods to estimate a neuron's contribution from its activity: neural variability and mutual information. Our study reveals the following: First, the same feed-forward neural network is capable of reusing its hidden layer neurons to perform multiple behaviors; second, information lesions reveal that the same behaviors are performed with different levels of reuse in different neural networks; and finally, mutual information is a better estimator of a neuron's contribution to a task than neural variability.
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Continual Learning with Deep Artificial Neurons
Neurons in real brains are complex computational units, capable of input-specific damping, inter-trial memory, and context-dependent signal processing. Artificial neurons, on the other hand, are usually implemented as simple weighted sums. Here we explore if increasing the computational power of individual neurons can yield more powerful neural networks. Specifically, we introduce Deep Artificial Neurons (DANs)—small neural networks with shared, learnable parameters embedded within a larger network. DANs act as filters between nodes in the net-work; namely, they receive vectorized inputs from multiple neurons in the previous layer, condense these signals into a single output, then send this processed signal to the neurons in the subsequent layer. We demonstrate that it is possible to meta-learn shared parameters for the various DANS in the network in order to facilitate continual and transfer learning during deployment. Specifically, we present experimental results on (1) incremental non-linear regression tasks and (2)unsupervised class-incremental image reconstruction that show that DANs allow a single network to update its synapses (i.e., regular weights) over time with minimal forgetting. Notably, our approach uses standard backpropagation, does not require experience replay, and does need separate wake/sleep phases.
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- Award ID(s):
- 1849946
- PAR ID:
- 10340606
- Date Published:
- Journal Name:
- International Conference on Learning Representations (ICLR) Workshop: From Cells to Societies
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
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