Faculty Bibliography

Dystonia is a movement disorder that produces involuntary muscle contractions. Current pharmacological treatments are of limited efficacy. Dystonia, like epilepsy is a disorder involving excessive activty of motor areas including motor cortex and several causal gene mutations have been identified. In order to evaluate potential novel agents for multitarget therapy for dystonia, we have developed a computer model of cortex that includes some of the complex array of molecular interactions that, along with membrane ion channels, control cell excitability.

KEY POINTS:receptors, probably on bipolar cell axon terminals. The GABAergic masking inhibition appears independent of dopaminergic circuitry that has been shown also to affect RGC sensitivity. The results indicate a novel mechanism whereby inhibition controls the sensitivity of different cohorts of RGCs. This can limit and thereby ensure that appropriate signals are carried centrally in scotopic conditions when sensitivity rather than acuity is crucial. ABSTRACT:The responses of rod photoreceptors, which subserve dim light vision, are carried through the retina by three independent pathways. These pathways carry signals with largely different sensitivities. Retinal ganglion cells (RGCs), the output neurons of the retina, show a wide range of sensitivities in the same dark-adapted conditions, suggesting a divergence of the rod pathways. However, this organization is not supported by the known synaptic morphology of the retina. Here, we tested an alternative idea that the rod pathways converge onto single RGCs, but inhibitory circuits selectively mask signals so that one pathway predominates. Indeed, we found that application of GABA receptor blockers increased the sensitivity of most RGCs by unmasking rod signals, which were suppressed. Our results indicate that inhibition controls the threshold responses of RGCs under dim ambient light. This mechanism can ensure that appropriate signals cross the bottleneck of the optic nerve in changing stimulus conditions.

Sporadic spontaneous network activity emerges during early central nervous system (CNS) development and, as the number of neuronal connections rises, the maturing network displays diverse and complex activity, including various types of synchronized patterns. These activity patterns have major implications on both basic research and the study of neurological disorders, and their interplay with network morphology tightly correlates with developmental events such as neuronal differentiation, migration and establishment of neurotransmitter phenotypes. Although 2D neural cultures models have provided important insights into network activity patterns, these cultures fail to mimic the complex 3D architecture of natural CNS neural networks and its consequences on connectivity and activity. A 3Din-vitromodel mimicking early network development while enabling cellular-resolution observations, could thus significantly advance our understanding of the activity characteristics in the developing CNS. Here, we longitudinally studied the spontaneous activity patterns of developing 3Din-vitroneural network "optonets," an optically-accessible bioengineered CNS model with multiple cortex-like characteristics. Optonet activity was observed using the genetically encodable calcium indicator GCaMP6m and a 3D imaging solution based on a standard epi-fluorescence microscope equipped with a piezo-electric z-stage and image processing-based deconvolution. Our results show that activity patterns become more complex as the network matures, gradually exhibiting longer-duration events. This report characterizes the patterns over time, and discusses how environmental changes affect the activity patterns. The relatively high degree of similarity between the network's spontaneously generated activity patterns and the reported characteristics ofin-vivoactivity, suggests that this is a compelling model system for brain-in-a chip research.

Linear-nonlinear (LN) models and their extensions have proven successful in describing transformations from stimuli to spiking responses of neurons in early stages of sensory hierarchies. Neural responses at later stages are highly nonlinear and have generally been better characterized in terms of their decoding performance on prespecified tasks. Here we develop a biologically plausible decoding model for classification tasks, that we refer to as neural quadratic discriminant analysis (nQDA). Specifically, we reformulate an optimal quadratic classifier as an LN-LN computation, analogous to "subunit" encoding models that have been used to describe responses in retina and primary visual cortex. We propose a physiological mechanism by which the parameters of the nQDA classifier could be optimized, using a supervised variant of a Hebbian learning rule. As an example of its applicability, we show that nQDA provides a better account than many comparable alternatives for the transformation between neural representations in two high-level brain areas recorded as monkeys performed a visual delayed-match-to-sample task.

Economists, psychologists, and neuroscientists have long been interested in methods that elicit individuals' true valuations of goods. In this paper, we take 1 of the most popular of such mechanisms, the Becker-DeGroot-Marschak (BDM) procedure, and study the nature of the dependence of the valuations obtained using the BDM procedure on the distribution of prices presented to subjects when the method is implemented. In a within-subject design with products with a high market value, we show that this effect of price distribution occurs quite frequently, significantly impacts reported valuations, and is unlikely to be caused by misconceptions about the BDM procedure. This effect is largest when pricing distributions show a large peak close to an individual's average valuation of the good. A simple nonincentive-compatible subjective rating of the desirability of goods can be used to predict the likelihood that pricing distributions will influence BDM valuations; valuations for goods that subjects report to most want to purchase are most likely to be influenced by distributional structure. Our results challenge some of the dominant theoretical models of how BDM-like valuation procedures relate to standard notions of utility and shed light on how to interpret the data obtained using the BDM method.