Seminars

The study of altered states of consciousness has long had the potential to provide important insight into the nature of consciousness. In recent years there has been a resurgence of research and public interest in atypical or altered states of consciousness. These have focused both on conditions in which consciousness is considered to be impaired due to brain trauma or enhanced in some way through mediation practices or ingestion of psychedelics. The talk will begin with a brief overview of recent scientific approaches to understanding these different types of altered consciousness. The remainder of the talk will focus on lab-based experiments conducted by myself and others looking at the effects of serotoninergic hallucinogens (i.e. psilocybin and LSD) on perceptual and cognitive function. Together this body of research provides important new insights for the scientific study of consciousness and an initial understanding of the neuropharmacological mechanisms underlying conscious experience.

In this talk I’ll discuss translation of findings of multisensory facilitation of learning to cognitive training. I’ll first review some early findings of multisensory facilitation of learning and then discuss how we have been translating these basic science approaches into gamified training interventions to improve cognitive functions. I’ll touch on approaches to training vision, hearing and working memory that we are developing at the UCR Brain Game Center for Mental Fitness and Well-being. I look forward to discussing both the basic science but also the complexities of how to translate approaches from basic science into the more complex frameworks often used in interventions.

In recent years, more and more evidence has accumulated showing that many studies in psychological research cannot be replicated, effects are often overestimated, and little is publicly known about unsuccessful studies. What are the mechanisms behind this crisis? In this talk, I will explain how we got there and why it is still difficult to break free from the current system. I will further explain which role Open Science plays within the replication crisis and how it can help to improve science. This might sound like a pessimistic, negative talk, but I will end it on a positive note, I promise!

Obesity and neurodegeneration lead to millions of premature deaths each year and lack broadly effective treatments. Obesity is largely caused by the abnormal function of cell populations in the hypothalamus that regulate appetite. We have developed methods generate human hypothalamic neurons from hPSCs to study how they respond to nutrients and hormones (e.g. leptin) and how disease-associated mutations alter their function. Since human hypothalamic neurons can be produced in large numbers, are functionally responsive, have a human genome that can be readily edited, and are in culture environment that can be readily controlled, there is an unprecedented opportunity to study the genetic and environmental factors underlying obesity. In addition, we are fascinated by the fact that mid-life obesity is a risk factor for dementia later in life, and caloric restriction, exercise, and certain anti-obesity drugs are neuroprotective, suggesting that there are shared mechanisms between obesity and neurodegeneration. Studies of HPSC-derived hypothalamic neurons may help bridge the mechanistic gulf between human genetic data and organismic phenotypes, revealing new therapeutic targets. ​

The idea that behavior results from a dynamic interplay between automatic and controlled processing underlies much of decision science, but has also generated considerable controversy. In this talk, I will highlight behavioral and neural data showing how recently-developed computational models of decision making can be used to shed new light on whether, when, and how decisions result from distinct processes operating at different timescales. Across diverse domains ranging from altruism to risky choice biases and self-regulation, our work suggests that a model of prioritized attentional sampling and evidence accumulation may provide an alternative explanation for many phenomena previously interpreted as supporting dual process models of choice. However, I also show how some features of the model might be taken as support for specific aspects of dual-process models, providing a way to reconcile conflicting accounts and generating new predictions and insights along the way.

The cerebellum contains more than half of the brain’s neurons and it is essential for motor control. Its neural circuits have a distinctive architecture comprised of a large, sparse expansion from the input mossy fibres to the granule cell layer. For years, theories of how cerebellar architectural features relate to cerebellar function have been formulated. It has been shown that some of these features can facilitate pattern separation. However, these theories don’t consider the need for it to learn fast in order to control smooth and accurate movements. Here, we confront this gap. This talk will show that the expansion to the granule cell layer in the cerebellar cortex improves learning speed and performance in the context of motor control by considering a cerebellar-like network learning an internal model of a motor apparatus online. By expressing the general form of the learning rate for such a system, this talk will provide a calculation of how increasing the number of granule cells diminishes the effect of noise and increases the learning speed. The researchers propose that the particular architecture of cerebellar circuits modifies the geometry of the error function in a favourable way for learning faster. Their results illuminate a new link between cerebellar structure and function.

Dr. Rajan and her lab design neural network models based on experimental data, and reverse-engineer them to figure out how brain circuits function in health and disease. They recently developed a powerful framework for tracing neural paths across multiple brain regions— called Current-Based Decomposition (CURBD). This new approach enables the computation of excitatory and inhibitory input currents that drive a given neuron, aiding in the discovery of how entire populations of neurons behave across multiple interacting brain regions. Dr. Rajan’s team has applied this method to studying the neural underpinnings of behavior. As an example, when CURBD was applied to data gathered from an animal model often used to study depression- and anxiety-like behaviors (i.e., learned helplessness) the underlying biology driving adaptive and maladaptive behaviors in the face of stress was revealed. With this framework Dr. Rajan's team probes for mechanisms at work across brain regions that support both healthy and disease states-- as well as identify key divergences from multiple different nervous systems, including zebrafish, mice, non-human primates, and humans.

The settling speeds of small biological particles influence the distribution of organisms such as plants, corals, and phytoplankton, but these speeds are difficult to quantify without magnification. In this talk, I highlight my novel method, using dynamic scaling principles and 3D printed models to solve this problem. Dynamic scaling involves creating models with differ in size to the original system and match the physical forces acting upon the model to the original system. I discuss the methodology behind the technique and show how it differs to previous works using dynamically scaled models. I show the flexibility of the technique and suggest how it can be applied to other free-falling particles (e.g. seeds and spores).

Self-organized complex structures in nature, e.g., viral capsids, hierarchical biopolymers, and bacterial flagella, offer efficiency, adaptability, robustness, and multi-functionality. Can we program the self-assembly of three-dimensional (3D) complex structures using simple building blocks, and reach similar or higher level of sophistication in engineered materials? Here we present an analytic theory for the self-assembly of polyhedral nanoparticles (NPs) based on their crystal structures in non-Euclidean space. We show that the unavoidable geometrical frustration of these particle shapes, combined with competing attractive and repulsive interparticle interactions, lead to controllable self-assembly of structures of complex order. Applying this theory to tetrahedral NPs, we find high-yield and enantiopure self-assembly of helicoidal ribbons, exhibiting qualitative agreement with experimental observations. We expect that this theory will offer a general framework for the self-assembly of simple polyhedral building blocks into rich complex morphologies with new material capabilities such as tunable optical activity, essential for multiple emerging technologies.