Electrical Properties of Two Types of Membrane Component Used in Taste Sensors

The lipid phosphoric acid di-n-decyl ester (PADE) has played an important role in the development of taste sensors. As previously reported, however, the concentration of PADE and pH of the solution affected the dissociation of H+, which made the measurement results less accurate and stable. In addition, PADE caused deterioration in the response to bitterness because PADE created the acidic environment in the membrane. To solve these problems, our past study tried to replace the PADE with a completely dissociated substance called tetrakis [3,5-bis (trifluoromethyl) phenyl] borate sodium salt dehydrate (TFPB) as lipid. To find out whether the two substances can be effectively replaced, it is necessary to perform an in-depth study on the properties of the two membranes themselves. In this study, we fabricated two types of membrane electrodes, based on PADE or TFPB, respectively, using 2-nitrophenyl octyl ether (NPOE) as a plasticizer. We measured the selectivity to cations such as Cs+, K+, Na+ and Li+, and also the membrane impedance of the membranes comprising PADE or TFPB of the different concentrations. As a result, we found that any concentration of PADE membranes always had low ion selectivity, while the ion selectivity of TFPB membranes was concentration-dependent, showing increasing ion selectivity with the TFPB concentrations. The ion selectivity order was Cs+>K+>Na+>Li+. The hydration of ions was considered to participate in this phenomenon. In addition, the membrane impedance decreased with increasing PADE and TFPB concentrations, while the magnitudes differed, implying that there is a difference in the dissociation of the two substances. The obtained results will contribute to the development of novel receptive membranes of taste sensors.

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

How neurons filter and integrate their complex patterns of synaptic inputs is central to their role in neural information processing. Synaptic filtering and integration are shaped by the frequency-dependent neuronal membrane impedance. Using single and dual dendritic recordings in vivo, pharmacology, and computational modeling, we characterized the membrane impedance of a collision detection neuron in the grasshopper Schistocerca americana. This neuron, the lobula giant movement detector (LGMD), exhibits consistent impedance properties across frequencies and membrane potentials. Two common active conductances gH and gM, mediated respectively by hyperpolarization-activated cyclic nucleotide-gated (HCN) channels and by muscarine-sensitive M-type K+ channels, promote broadband integration with high temporal precision over the LGMD’s natural range of membrane potentials and synaptic input frequencies. Additionally, we found that a model based on the LGMD’s branching morphology increased the gain and decreased the delay associated with the mapping of synaptic input currents to membrane potential. More generally, this was true for a wide range of model neuron morphologies, including those of neocortical pyramidal neurons and cerebellar Purkinje cells. These findings show the unexpected role played by two widespread active conductances and by dendritic morphology in shaping synaptic integration. NEW & NOTEWORTHY Neuronal filtering and integration of synaptic input patterns depend on the electrochemical properties of dendrites. We used an identified collision detection neuron in grasshoppers to examine how its morphology and two conductances affect its membrane impedance in relation to the computations it performs. The neuronal properties examined are ubiquitous and therefore promote a general understanding of neuronal computations, including those in the human brain.

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

Brains processes information through the coordinated efforts of billions of individual neurons, each encoding 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 sets the gain and timing for synaptic integration, determining a neuron's ability to discriminate between synaptic input patterns. Using single and dual dendritic recordings in vivo, pharmacology, and computational modeling, we characterized the membrane impedance of a collision detection neuron in the grasshopper, Schistocerca americana. We examined how the cellular properties of the lobula giant movement detector (LGMD) neuron are tuned to enable the discrimination of synaptic input patterns key to its role in collision detection. We found that two common active conductances gH and gM, mediated respectively by hyperpolarization-activated cyclic nucleotide gated (HCN) channels and by muscarine sensitive M-type K+ channels, promote broadband integration with high temporal precision over the LGMD's natural range of membrane potentials and synaptic input frequencies. Additionally, we found that the LGMD's branching morphology increased the gain and decreased delays associated with the mapping of synaptic input currents to membrane potential. We investigated whether other branching dendritic morphologies fulfill a similar function and found this to be true for a wide range of morphologies, including those of neocortical pyramidal neurons and cerebellar Purkinje cells. 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.

A Compartmental Model of Linear Resonance and Signal Transfer in Dendrites

Dendrites carry signals between synapses and the soma and play a central role in neural computation. Although they contain many nonlinear ion channels, their signal-transfer properties are linear under some experimental conditions. In experiments with continuous-time inputs, a resonant linear two-port model has been shown to provide a near-perfect fit to the dendrite-to-soma input-output relationship. In this study, we focused on this linear aspect of signal transfer using impedance functions that replace biophysical channel models in order to describe the electrical properties of the dendritic membrane. The membrane impedance model of dendrites preserves the accuracy of the two-port model with minimal computational complexity. Using this approach, we demonstrate two membrane impedance profiles of dendrites that reproduced the experimentally observed two-port results. These impedance profiles demonstrate that the two-port results are compatible with different computational schemes. In addition, our model highlights how dendritic resonance can minimize the location-dependent attenuation of signals at the resonant frequency. Thus, in this model, dendrites function as linear-resonant filters that carry signals between nonlinear computational units.