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In many cortical neurons, HCN1 channels are the major contributors to Ih, the hyperpolarization-activated current, which regulates the intrinsic properties of neurons and shapes their integration of synaptic inputs, paces rhythmic activity, and regulates synaptic plasticity. Here, we examine the physiological role of Ih in deep layer pyramidal neurons in mouse prefrontal cortex (PFC), focusing on persistent activity, a form of sustained firing thought to be important for the behavioral function of the PFC during working memory tasks. We find that HCN1 contributes to the intrinsic persistent firing that is induced by a brief depolarizing current stimulus in the presence of muscarinic agonists. Deletion of HCN1 or acute pharmacological blockade of Ih decreases the fraction of neurons capable of generating persistent firing. The reduction in persistent firing is caused by the membrane hyperpolarization that results from the deletion of HCN1 or Ih blockade, rather than a specific role of the hyperpolarization-activated current in generating persistent activity. In vivo recordings show that deletion of HCN1 has no effect on up states, periods of enhanced synaptic network activity. Parallel behavioral studies demonstrate that HCN1 contributes to the PFC-dependent resolution of proactive interference during working memory. These results thus provide genetic evidence demonstrating the importance of HCN1 to intrinsic persistent firing and the behavioral output of the PFC. The causal role of intrinsic persistent firing in PFC-mediated behavior remains an open question.

Original publication




Journal article


J Neurosci

Publication Date





13583 - 13599


Action Potentials, Animals, Choice Behavior, Cyclic Nucleotide-Gated Cation Channels, Executive Function, Gene Expression Regulation, Green Fluorescent Proteins, Hyperpolarization-Activated Cyclic Nucleotide-Gated Channels, In Vitro Techniques, Maze Learning, Memory, Mice, Mice, Inbred C57BL, Mice, Transgenic, Neurons, Neurotransmitter Agents, Patch-Clamp Techniques, Potassium Channels, Prefrontal Cortex, Serial Learning, Synaptic Potentials