Deep problems with neural network models of human vision

Deep neural networks (DNNs) have had extraordinary successes in classifying photographic images of objects and are often described as the best models of biological vision. This conclusion is largely based on three sets of findings: (1) DNNs are more accurate than any other model in classifying images taken from various datasets, (2) DNNs do the best job in predicting the pattern of human errors in classifying objects taken from various behavioral datasets, and (3) DNNs do the best job in predicting brain signals in response to images taken from various brain datasets (e.g., single cell responses or fMRI data). However, these behavioral and brain datasets do not test hypotheses regarding what features are contributing to good predictions and we show that the predictions may be mediated by DNNs that share little overlap with biological vision. More problematically, we show that DNNs account for almost no results from psychological research. This contradicts the common claim that DNNs are good, let alone the best, models of human object recognition. We argue that theorists interested in developing biologically plausible models of human vision need to direct their attention to explaining psychological findings. More generally, theorists need to build models that explain the results of experiments that manipulate independent variables designed to test hypotheses rather than compete on making the best predictions. We conclude by briefly summarizing various promising modeling approaches that focus on psychological data.

Model-Based Inference of Synaptic Transmission

Synaptic computation is believed to underlie many forms of animal behavior. A correct identification of synaptic transmission properties is thus crucial for a better understanding of how the brain processes information, stores memories and learns. Recently, a number of new statistical methods for inferring synaptic transmission parameters have been introduced. Here we review and contrast these developments, with a focus on methods aimed at inferring both synaptic release statistics and synaptic dynamics. Furthermore, based on recent proposals we discuss how such methods can be applied to data across different levels of investigation: from intracellular paired experiments to in vivo network-wide recordings. Overall, these developments open the window to reliably estimating synaptic parameters in behaving animals.

Physical characteristics of nano-Hydroxyapatite Pickering-emulsions and their adjuvant activity on the antibody response towards the Bothros asper snake venom

Emulsions are crucial in the treatment of snake bites to bust the antibody response of the inmunogen. The widely used Freund's emulsion typically combines 50/50 water-oil (W/O) phase. However, its use is limited because it is associated with tissue damage. We formulated and characterized a Pickering Emulsion 70/30 (W/O) that uses a chemically modified hydrophobic hydroxyapatite as surfactant. This Pickering emulsion has similar rheologic behavior to Freund's emulsion 50/50, but with lower oil and surfactant concentration. Evaluation of cell recruitment, antibody response and adhering tissue in mice immunized with B. asper of Pacific venom and treated with Freund's and Pickering 70/30 emulsions resulted in similar adjuvant activity (only 18% lower in Pickering 70/30 emulsion). However, Pickering 70/30 emulsions minimized negative side effects in the host animals and showed better ease of flow that favors injection of the host. Our results open up room for optimization and improvement of Pickering emulsion based on modified nanoparticles for medical applications.