The brain is comprised of an immense number of connections between neurons, and clever strategies are required to achieve the correct wiring during development. One common strategy uses neural activity itself as feedback to instruct individual connections (synapses) through synaptic learning rules that delineate which patterns of activity strengthen the synapse and which weaken it. Throughout life, such activity-dependent synaptic changes are likely driven by experience and are thought to underlie learning and memory, but during early stages of development, they are often driven by activity spontaneously generated within the brain. Here, we study connections in the visual pathway between the retina and lateral geniculate nucleus (LGN), which—to develop correctly—require spontaneous “retinal waves” before the eye is responsive to light. By replaying the retinal wave activity as it appears at single LGN synapses, we observe a novel learning rule that describes a relatively simple computation for the developing synapse in the context of retinal wave activity. We then demonstrate how this learning rule is matched to properties of the retinal waves in order to robustly drive the synaptic refinement that occurs in the visual system.More specifically:
Retinogeniculate synapses have a novel learning rule that depends on the latencies between pre- and postsynaptic bursts on the order of one second: coincident bursts produce long-lasting synaptic enhancement, whereas non-overlapping bursts produce mild synaptic weakening. It is consistent with “Hebbian” development thought to exist at this synapse, and we demonstrate computationally that such a rule can robustly use retinal waves to drive eye segregation and retinotopic refinement. Thus, by measuring plasticity induced by natural activity patterns, synaptic learning rules can be linked directly to their larger role in instructing the patterning of neural connectivity.
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