RESUMEN
Whole-cell patch neurophysiology and pharmacological manipulations have provided unprecedented insight into the functions of central neurons, but their combined use has been largely restricted to in vitro preparations. We describe a method for performing whole-cell patch recording and focal application of pharmacological agents in vivo. A key feature of this technique involves iontophoresis of glutamate to establish proximity of drug and recording pipettes. We show data from iontophoresis of glutamate during extracellular and whole-cell recordings made in vivo from auditory neurons in the midbrain of the leopard frog, Rana pipiens, and the effects of blocking GABA(A) receptors while making a whole-cell recording. This methodology should accelerate our understanding of the roles of particular neurotransmitter systems in normal and pathological conditions, and facilitate investigation of the in vivo effects of drugs and the mechanisms underlying computations.
Asunto(s)
Sistema Nervioso Central/efectos de los fármacos , Neuronas/efectos de los fármacos , Técnicas de Placa-Clamp/métodos , Estimulación Acústica , Animales , Corteza Auditiva/fisiología , Sistema Nervioso Central/citología , Interpretación Estadística de Datos , Electrodos Implantados , Espacio Extracelular/química , Antagonistas de Receptores de GABA-A/farmacología , Ácido Glutámico/farmacología , Iontoforesis , Mesencéfalo/fisiología , Rana pipiensRESUMEN
We present a method for the reconstruction of three stimulus-evoked time-varying synaptic input conductances from voltage recordings. Our approach is based on exploiting the stochastic nature of synaptic conductances and membrane voltage. Starting with the assumption that the variances of the conductances are known, we use a stochastic differential equation to model dynamics of membrane potential and derive equations for first and second moments that can be solved to find conductances. We successfully apply the new reconstruction method to simulated data. We also explore the robustness of the method as the assumptions of the underlying model are relaxed. We vary the noise levels, the reversal potentials, the number of stimulus repetitions, and the accuracy of conductance variance estimation to quantify the robustness of reconstruction. These studies pave the way for the application of the method to experimental data.
