Dual face of axonal inhibitory inputs in the modulation of neuronal excitability in cortical pyramid

来源 :Neural Regeneration Research | 被引量 : 0次 | 上传用户:GoAndSeek
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Limited by the tiny structure of axons,the effects of these axonal hyperpolarizing inputs on neuronal activity have not been directly elucidated.Here,we imitated these processes by simultaneously recording the activities of the somas and proximal axons of cortical pyramidal neurons.We found that spikes and subthreshold potentials propagate between somas and axons with high fidelity.Furthermore,inhibitory inputs on axons have opposite effects on neuronal activity according to their temporal integration with upstream signals.Concurrent with somatic depolarization,inhibitory inputs on axons decrease neuronal excitability and impede spike generation.In addition,following action potentials,inhibitory inputs on an axon increase neuronal spike capacity and improve spike precision.These results indicate that inhibitory inputs on proximal axons have dual regulatory functions in neuronal activity(suppression or facilitation)according to neuronal network patterns. Limited by the tiny structure of axons, the effects of these axonal hyperpolarizing inputs on neuronal activity have not been directly elucidated. Here, we imitated these processes by simultaneously recording the activities of the somas and proximal axons of cortical pyramidal neurons. We found that spikes and subthreshold potentials propagate between somas and axons with high fidelity. Focus on axons have opposite effects on neuronal activity according to their temporal integration with upstream signals. Current with somatic depolarization, inhibitory inputs on axons decrease neuronal excitability and impede spike generation. In addition, the following action potentials, inhibitory inputs on an axon increase neuronal spike capacity and improve spike precision. These results that inhibitory inputs on proximal axons have dual regulatory functions in neuronal activity (suppression or facilitation) according to neuronal network patterns.
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