Dendritic solutions to the credit assignment problem
Section snippets
Introduction: the credit assignment problem
The flexibility of learning in animals indicates that the brain possesses general purpose learning algorithms. A learning algorithm is a set of rules for translating the experiences an animal has into changes in their neural circuits (e.g. synaptic changes). The ultimate goal of a learning algorithm is to alter the behavioral phenotype of the animal, helping it to adapt to the environment. Understanding the brain's learning algorithms is key to understanding the biological basis of animal
What counts as evidence for credit assignment?
The ideal experiment for understanding credit assignment in the brain would be to measure a loss function explicitly, then demonstrate that a given synaptic plasticity mechanism was responsible for ensuring reductions in that loss function during learning. Such experiments are currently outside of our technical reach, though, because it is often unclear how we can identify a loss function in the brain and track its progress over time [3]. Furthermore, there is no reason to assume that the brain
Credit assignment in cortical pyramidal neurons
In the neocortex and hippocampus, pyramidal neurons are part of a hierarchical pathway with multiple sources of potential credit-related feedback. Thus, assigning credit in cortical pyramidal neurons may require more involved calculations than in the output layer of the cerebellum with one-to-one climbing fiber → Purkinje cell error signals. Where might these credit calculations take place? To date, direct experimental evidence for credit assignment calculations in neocortical neurons is
Conclusion
A major goal for researchers in coming years should be a better link between the theory of credit assignment in neural networks [7,9••,10••,11, 12, 13, 14], and our growing knowledge of the biophysics of dendrites and dendritic computation [28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44]. Clearly, there is much more to understand about dendritic computations in pyramidal neurons, how they may signal credit information, and how they contribute to learning, in-turn. Three
Conflict of interest statement
Nothing declared.
References and recommended reading
Papers of particular interest, published within the period of review, have been highlighted as:
• of special interest
•• of outstanding interest
Acknowledgements
This work was supported by the Natural Sciences and Engineering Research Council of Canada (BAR: RGPIN-2014-04947) and the Canadian Institute for Advanced Research (BAR: Learning in Machine and Brains Program).
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