AI Article Synopsis
- The 'credit assignment problem' in neural networks involves figuring out how to attribute learning success to specific neurons in complex processing stages.
- Assigning credit is difficult because different inputs can overlap, making it hard for synaptic changes to know which signals are responsible for learning.
- Research indicates that the distal apical dendrites in cortical pyramidal neurons may help differentiate important feedback signals, aiding in the credit assignment process for effective learning.*
Article Abstract
Guaranteeing that synaptic plasticity leads to effective learning requires a means for assigning credit to each neuron for its contribution to behavior. The 'credit assignment problem' refers to the fact that credit assignment is non-trivial in hierarchical networks with multiple stages of processing. One difficulty is that if credit signals are integrated with other inputs, then it is hard for synaptic plasticity rules to distinguish credit-related activity from non-credit-related activity. A potential solution is to use the spatial layout and non-linear properties of dendrites to distinguish credit signals from other inputs. In cortical pyramidal neurons, evidence hints that top-down feedback signals are integrated in the distal apical dendrites and have a distinct impact on spike-firing and synaptic plasticity. This suggests that the distal apical dendrites of pyramidal neurons help the brain to solve the credit assignment problem.
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| http://dx.doi.org/10.1016/j.conb.2018.08.003 | DOI Listing |
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