A major goal in neuroscience is to understand the relationship between an animal’s behavior and how this is encoded in the brain. Therefore, a typical experiment involves training an animal to perform a task and recording the activity of its neurons – brain cells – while the animal carries out the task. To complement these experimental results, researchers “train” artificial neural networks – simplified mathematical models of the brain that consist of simple neuron-like units – to simulate the same tasks on a computer. Unlike real brains, artificial neural networks provide complete access to the “neural circuits” responsible for a behavior, offering a way to study and manipulate the behavior in the circuit. One open issue about this approach has been the way in which the artificial networks are trained. In a process known as reinforcement learning, animals learn from rewards (such as juice) that they receive when they choose actions that lead to the successful completion of a task. By contrast, the artificial networks are explicitly told the correct action. In addition to differing from how animals learn, this limits the types of behavior that can be studied using artificial neural networks. Recent advances in the field of machine learning that combine reinforcement learning with artificial neural networks have now allowed Song et al. to train artificial networks to perform tasks in a way that mimics the way that animals learn. The networks consisted of two parts: a “decision network” that uses sensory information to select actions that lead to the greatest reward, and a “value network” that predicts how rewarding an action will be. Song et al. found that the resulting artificial “brain activity” closely resembled the activity found in the brains of animals, confirming that this method of training artificial neural networks may be a useful tool for neuroscientists who study the relationship between brains and behavior. The training method explored by Song et al. represents only one step forward in developing artificial neural networks that resemble the real brain. In particular, neural networks modify connections between units in a vastly different way to the methods used by biological brains to alter the connections between neurons. Future work will be needed to bridge this gap.
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Neural reuse in multifunctional neural networks for control tasks
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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- Award ID(s):
- 1845322
- NSF-PAR ID:
- 10174172
- Date Published:
- Journal Name:
- ALIFE 2020: The 2020 Conference on Artificial Life
- Issue:
- 32
- Page Range / eLocation ID:
- 210 - 218
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1162/isal_a_00319
