Active membrane conductances and morphology of a collision detection neuron broaden its impedance profile and improve membrane synchrony

Abstract

Our brain processes information through the coordinated efforts of billions of individual neurons, each of which transforms a small part of the overall information stream. Central to this is how neurons integrate and transform complex patterns of synaptic inputs. The neuronal membrane impedance determines the change in membrane potential in response to input currents, and therefore sets the gain and timing for synaptic integration. Using single and dual dendritic recordings in vivo, pharmacology, and computational modeling, we characterized the role of two active conductances gH and gM, meditated respectively by hyperpolarization-activated cyclic nucleotide gated (HCN) channels and by muscarine sensitive M-channels, in shaping the membrane impedance of a collision detection neuron in female Schistocerca americana grasshoppers. The neuron is known by its acronym LGMD, which stands for lobula giant movement detector. In contrast to other neurons where these conductances have been studied, we found that gH and gM promote broadband, synchronous integration over the LGMDs functional range of membrane potentials and input frequencies. Additionally, we found that the branching morphology of the LGMD helped increase both the gain and synchrony associated with the neurons membrane impedance. The same result held for a wide range of dendritic morphologies, including those of mammalian neocortical pyramidal neurons and cerebellar Purkinje cells. Thus, these findings further our understanding of the integration properties of individual neurons by showing the unexpected role played by two widespread active conductances and by dendritic morphology in shaping synaptic integration.\n\nSignificance StatementInformation in the brain is processed by neurons that receive thousands of synaptic inputs. Understanding how neurons integrate these inputs is critical to neuroscience. Neuronal integration depends on complex interactions of synaptic input patterns and the electrochemical properties of dendrites. Although examining the input patterns and dendritic processing in vivo is not yet possible in the mammalian brain, it is within simpler nervous systems. Here, we used an identified collision detection neuron in grasshoppers to examine how its morphology and membrane properties determine the gain and synchrony of synaptic integration in relation to the computations it performs. The neuronal properties examined are ubiquitous and therefore will further a general understanding of neuronal computations, including those in our own brain.