Instead, these processing units can themselves learn to implement much more complex input-output functions as previously thought. This changes the classical view that learning in the brain is realized by rewiring simple processing units as formalized by the neural network theory. An error expressed as mismatch between somatic firing and membrane potential may be backpropagated to the active dendritic branches where it modulates synaptic plasticity. Here we show that this algorithm may be implemented within a single neuron equipped with nonlinear dendritic processing. Whether and how this technical algorithm is implemented in cortical structures, however, remains elusive. Our framework makes specific experimental predictions and highlights the unique advantage of active dendrites for implementing powerful synaptic plasticity rules that have access to downstream information via backpropagation of action potentials.Įrror-backpropagation is a successful algorithm for supervised learning in neural networks. When modulated by a delayed reward signal, the same plasticity is shown to maximize the expected reward in reinforcement learning for various coding scenarios. For supervised learning, the rule can be seen as a biological version of the classical error-backpropagation algorithm applied to the dendritic case. We present a theoretically derived synaptic plasticity rule for supervised and reinforcement learning that depends on the timing of the presynaptic, the dendritic and the postsynaptic spikes. But how these nonlinearities can be incorporated into the synaptic plasticity to optimally support learning remains unclear. It has been suggested that these nonlinearities raise the computational power of a single neuron, making it comparable to a 2-layer network of point neurons. In the last decade dendrites of cortical neurons have been shown to nonlinearly combine synaptic inputs by evoking local dendritic spikes.
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