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Neural backpropagation is the phenomenon in which the action potential of a neuron creates a voltage spike both at the end of the axon (normal propagation) and back through to the dendritic arbor or dendrites, from which much of the original input current originated. It has been shown that this simple process can be used in a manner similar to the backpropagation algorithm used in multilayer perceptrons, a type of artificial neural network. In addition to active backpropagation of the action potential, there is also passive electrotonic spread.
When a neuron fires an action potential, it is initiated at the axon hillock. An action potential spreads down the axon because of the gating properties of voltage-gated sodium channels and voltage-gated potassium channels. However, the cell body or soma can also become depolarized when an action potential is initiated, and this depolarization can spread out to the dendritic tree where there are voltage-gated calcium channels. Voltage-gated calcium channels can then lead to a propagating (most of the time) dendritic action potential. EPSPs from synaptic activation are not large enough to activate the dendritic voltage-gated calcium channels (usually on the order of a couple milliAmps each) so backpropagation is believed to happen only when the cell is activated to fire an action potential.
Since the 1950s, evidence has existed that neurons in the central nervous system generate an action potential, or voltage spike, that travels both through the axon to signal the next neuron and backpropagates through the dendrites sending a retrograde signal to its presynaptic signaling neurons. This current decays significantly with travel length along the dendrites, so effects are predicted to be more significant for neurons whose synapses are near the postsynaptic cell body, with magnitude depending mainly on sodium-channel density in the dendrite. It is also dependent on the shape of the dendritic tree and, more importantly, on the rate of signal currents to the neuron. On average, a backpropagating spike loses about half its voltage after traveling nearly 500 micrometres.
Backpropagation occurs actively in the neocortex, hippocampus, substantia nigra, and spinal cord, while in the cerebellum it occurs relatively passively. This is consistent with observations that synaptic plasticity is much more apparent in areas like the hippocampus, which controls memory, than the cerebellum, which controls more unconscious and vegetative functions.
The backpropagating current also causes a voltage change that increases the concentration of Ca2+ in the dendrites, an event which coincides with certain models of synaptic plasticity. This change also affects future integration of signals, leading to at least a short-term response difference between the presynaptic signals and the postsynaptic spike.
There are a number of hypotheses regarding the function of backpropagation of action potentials. In addition to synaptic plasticity, it is also hypothesized to be involved in dendrodendritic inhibition, boosting synaptic responses, resetting membrane potential, retrograde actions at synapses and conditional axonal output. Backpropagation is believed to help form LTP (long term potentiation) and Hebbian plasticity at hippocampal synapses. Since artificial LTP induction, using microelectrode stimulation, voltage clamp, etc. requires the postsynaptic cell to be slightly depolarized when EPSPs are elicited, backpropagation can serve as the means of depolarization of the postsynaptic cell.
While a backpropagating action potential can presumably cause changes in the weight of the presynaptic connections, there is no simple mechanism for an error signal to propagate through multiple layers of neurons, as in the computer backpropagation algorithm. However, simple linear topologies have shown that effective computation is possible through signal backpropagation in this biological sense.
- ↑ Stuart, Greg, Nelson Spruston, Bert Sakmann, Michael Häusser (1997). Action potential initiation and backpropagation in neurons of the mammalian CNS. TINS 20 (3).
- ↑ Bogacz, Rafal, Malcolm W. Brown, Christophe Giraud-Carrier (2000). Frequency-based Error Back-propagation in a Cortical Network. Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks, Como (Italy) 2: 211–216. 0-7695-0619-4.
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