Search for: All records

Sub-additivity and variability are ubiquitous response motifs in primary visual cortex (V1). Response sub-additivity provides a sign of the brain processes that enable us to construct useful interpretations of the visual environment (i.e., nonlinear input transformations), while response variability provides a sign of the brain processes that limit the precision with which we can do this (i.e., neural information loss). Historically, these two motifs have been studied independently of each other. Yet, there is increasing evidence that experimen- tal manipulations that elicit response sub-additivity often also quench response variability. Here we provide a unifying review of these phenomena, suggesting that response sub-additivity and variability quenching may have a common origin. We review empirical findings as well as recent model-based insights into the functional operations, computational objectives, and circuit mechanisms underlying V1 activity. Although these model- ing approaches address different aspects of cortical activity, they all predict that response sub-additivity and variability quenching will often co-occur. Response sub-additivity and variability quenching are not limited to V1 but are widespread cortical phenomena. Many of the insights we review generalize to other cortical areas, suggesting that the connection between response sub-additivity and variability quenching may be a canonical motif across cortex. 

The restricted rotation of chemical bonds may lead to the formation of stable, conformationally chiral molecules. While the asymmetry in chiral molecules is generally observed in the presence of one or more stereocenters, asymmetry exhibited by conformational chirality in compounds lacking stereocenters, called atropisomerism, depends on structural and temperature factors that are still not fully understood. This atropisomerism is observed in natural diarylether heptanoids where the length of the intramolecular tether constrains the compounds to isolable enantiomers at room temperature. In this work, we examine the impact tether length has on the activation free energies to isomerization of a diarylether cyclophane substructure with a tether ranging from 6 to 14 carbons. Racemization activation energies are observed to decay from 48 kcal/mol for a 7-carbon tether to 9.2 kcal/mol for a 14-carbon tether. Synthetic efforts to experimentally test these constraints are also presented. This work will likely guide the design and synthesis of novel asymmetric cyclophanes that will be of interest in the catalysis community given the importance of atropisomeric ligands in the field of asymmetric catalysis. 

A cornerstone of theoretical neuroscience is the circuit model: a system of equations that captures a hypothesized neural mechanism. Such models are valuable when they give rise to an experimentally observed phenomenon -- whether behavioral or a pattern of neural activity -- and thus can offer insights into neural computation. The operation of these circuits, like all models, critically depends on the choice of model parameters. A key step is then to identify the model parameters consistent with observed phenomena: to solve the inverse problem. In this work, we present a novel technique, emergent property inference (EPI), that brings the modern probabilistic modeling toolkit to theoretical neuroscience. When theorizing circuit models, theoreticians predominantly focus on reproducing computational properties rather than a particular dataset. Our method uses deep neural networks to learn parameter distributions with these computational properties. This methodology is introduced through a motivational example of parameter inference in the stomatogastric ganglion. EPI is then shown to allow precise control over the behavior of inferred parameters and to scale in parameter dimension better than alternative techniques. In the remainder of this work, we present novel theoretical findings in models of primary visual cortex and superior colliculus, which were gained through the examination of complex parametric structure captured by EPI. Beyond its scientific contribution, this work illustrates the variety of analyses possible once deep learning is harnessed towards solving theoretical inverse problems.