Statistical Analysis of Visual Fits: Answer to J. Ninio

Abstract

REPLY: We thank Dr. Ninio for his interest in our 25-yr-old publications and for his attempt to analyze our data and interpretations using a strategy different from the one we used for this pioneering work. However, his approach is exclusively concerned with curve fitting. It avoids the more demanding problem of the recovery of parameters of explicit statistical models of biological processes. That is, he decided to discriminate between fits of our published histograms visually, or “by eye.” The field long ago progressed to the point where the standard for confronting data with models is statistical analysis, not visual fitting. Reevaluation of old methods can be a good thing (Lowen et al. 1997), provided there is a sound scientific questions at stake. Yet, we are surprised that Dr. Ninio raises concerns about the validity of aspects of these seminal papers but does not want to review the subsequent literature that appeared about our results and conclusions. This is difficult to understand because the aim of a meaningful scientific approach is to progress in the understanding of natural phenomena and processes. In this respect, it is worth noting that subsequent research carried out independently by a number of eminent scientists supported our proposal of the “One Vesicle Hypothesis” of transmitter release at several synapses in the CNS. In this response we address the flaws in the logic of Ninio’s analysis and his criticisms and misunderstandings of our published work. Because of the progress in this field, and given that Dr. Ninio is not an expert in this area, we believe our response here will be of interest to other investigators who are concerned with using rigorous methods to extract information from experimental distributions. BACKGROUND Experimental context We performed simultaneous intracellular recordings and dye injections, in vivo, to collect data sets of fluctuating inhibitory postsynaptic potentials (IPSPs) evoked in the Mauthner cell of teleost fish by single presynaptic action potentials and used an optimization procedure to determine the best-fitting binomial and Poisson descriptions of the data. We then compared those results with histological reconstructions of the presynaptic cells, including the axonal branching pattern and the identification of contacts with the postsynaptic neuron. This was one of the earliest attempts to obtain such structure‐function correlations. We were particularly interested in two questions that had been raised previously: 1) Were the fluctuations in PSP amplitude better described by a binomial or a Poisson distribution? and 2) Could the statistical parameters be correlated with physical structures?