Inferring neural circuit structure from datasets of heterogeneous tuning curves.

Tuning curves characterizing the response selectivities of biological neurons can exhibit large degrees of irregularity and diversity across neurons. Theoretical network models that feature heterogeneous cell populations or partially random connectivity also give rise to diverse tuning curves. Empirical tuning curve distributions can thus be utilized to make model-based inferences about the statistics of single-cell parameters and network connectivity. However, a general framework for such an inference or fitting procedure is lacking. We address this problem by proposing to view mechanistic network models as implicit generative models whose parameters can be optimized to fit the distribution of experimentally measured tuning curves. A major obstacle for fitting such models is that their likelihood function is not explicitly available or is highly intractable. Recent advances in machine learning provide ways for fitting implicit generative models without the need to evaluate the likelihood and its gradient. Generative Adversarial Networks (GANs) provide one such framework which has been successful in traditional machine learning tasks. We apply this approach in two separate experiments, showing how GANs can be used to fit commonly used mechanistic circuit models in theoretical neuroscience to datasets of tuning curves. This fitting procedure avoids the computationally expensive step of inferring latent variables, such as the biophysical parameters of, or synaptic connections between, particular recorded cells. Instead, it directly learns generalizable model parameters characterizing the network's statistical structure such as the statistics of strength and spatial range of connections between different cell types. Another strength of this approach is that it fits the joint high-dimensional distribution of tuning curves, instead of matching a few summary statistics picked a priori by the user, resulting in a more accurate inference of circuit properties. More generally, this framework opens the door to direct model-based inference of circuit structure from data beyond single-cell tuning curves, such as simultaneous population recordings.

Oral Squamous Cell Carcinoma in the paediatric patient: a literature review.

AIM: Paediatric Oral Squamous Cell Carcinoma (OSCC) is rare, but its incidence is increasing, bringing forward the issue of the common pathogenic factors. The aim of this study is to verify the actual incidence of oral carcinoma reported in paediatric patients up to the age of 15 by thoroughly reviewing the available literature. Setting this cut-off age has allowed us to emphasise possible risk factors other than those always associated with the onset of this neoplasia, which are not present in this age bracket yet. METHODS: In the first stage of the research, generic key words concerning OSCC in childhood were entered into two search engines. In the second stage, terms related to predisposing diseases connected to childhood oral carcinoma and those initially found were searched. RESULTS: The literature review consisted of 55 documented cases from 1894 to 2011, of which 15 were part of complete published case reports. CONCLUSION: Paediatric OSCC, though uncommon, is not rare. The review has strongly highlighted the need to carry out an objective, thorough and standardised examination of the child's oral cavity, especially when systemic predisposing diseases, such as Epidermolysis bullosa, Xeroderma pigmentosum, Juvenile papillomatosis and Fanconi's anaemia, are present.