Abstract
Synaptic plasticity is thought to be the principal neuronal mechanism underlying learning. Models of plastic networks typically combine point neurons with spike-timing-dependent plasticity (STDP) as the learning rule. However, a point neuron does not capture the local non-linear processing of synaptic inputs allowed for by dendrites. Furthermore, experimental evidence suggests that STDP is not the only learning rule available to neurons. By implementing biophysically realistic neuron models, we study how dendrites enable multiple synaptic plasticity mechanisms to coexist in a single cell. In these models, we compare the conditions for STDP and for synaptic strengthening by local dendritic spikes. We also explore how the connectivity between two cells is affected by these plasticity rules and by different synaptic distributions. Finally, we show that how memory retention during associative learning can be prolonged in networks of neurons by including dendrites.
Highlights
Models of plastic networks typically combine point neurons with spike-timing-dependent plasticity (STDP) as the learning rule
Since NMDA spikes are more evoked in the distal regions of a dendrite (Fig. 1c, d), we investigated whether dependent LTP (dLTP), without the need for action potentials, is more evoked at the distal locations
We investigated how dendrites allow for various plasticity mechanisms within a neuron, and how these depend on the location of the synapse along the dendrite
Summary
Models of plastic networks typically combine point neurons with spike-timing-dependent plasticity (STDP) as the learning rule. By implementing biophysically realistic neuron models, we study how dendrites enable multiple synaptic plasticity mechanisms to coexist in a single cell. In these models, we compare the conditions for STDP and for synaptic strengthening by local dendritic spikes. When the presynaptic neuron fires just before the postsynaptic neuron, the synapse is potentiated; reversing the firing order leads to synaptic depression[7,8,9] This form of plasticity, where the precise timing of spikes determines the subsequent synaptic change, is called spike-timing-dependent plasticity (STDP), and is a widely used learning rule in computational studies[10,11,12]
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