Automated High-Throughput Characterization of Single Neurons by Means of Simplified Spiking Models

Single-neuron models are useful not only for studying the emergent properties of neural circuits in large-scale simulations, but also for extracting and summarizing in a principled way the information contained in electrophysiological recordings. Here we demonstrate that, using a convex optimization procedure we previously introduced, a Generalized Integrate-and-Fire model can be accurately fitted with a limited amount of data. The model is capable of predicting both the spiking activity and the subthreshold dynamics of different cell types, and can be used for online characterization of neuronal properties. A protocol is proposed that, combined with emergent technologies for automatic patch-clamp recordings, permits automated, in vitro high-throughput characterization of single neurons.Author Summary: Large-scale, high-throughput data acquisition is revolutionizing the field of neuroscience. Single-neuron electrophysiology is moving from the situation where a highly skilled experimentalist can patch a few cells per day, to a situation where robots will collect large amounts of data. To take advantage of this quantity of data, this technological advance requires a paradigm shift in the experimental design and analysis. Presently, most single-neuron experimental studies rely on old protocols—such as injections of steps and ramps of current—that rarely inform theoreticians and modelers interested in emergent properties of the brain. Here, we describe an efficient protocol for high-throughput in vitro electrophysiology as well as a set of mathematical tools that neuroscientists can use to directly translate experimental data into realistic spiking neuron models. The efficiency of the proposed method makes it suitable for high-throughput data analysis, allowing for the generation of a standardized database of realistic single-neuron models.