The Monday Seminars Series will resume on 2/16/2015 with a talk by Dr. Zayd Khaliq entitled Excitability and Synaptic Integration in Midbrain Dopamine Neurons.]]>
This seminar has been canceled due to inclement weather, but we hope to reschedule Dr. Khaliq for a later date.
The goal of our research is to understand the mechanism of synaptic integration and excitability of neurons located in the basal ganglia. Initial efforts in the lab have focused primarily on synaptic and dendritic properties of dopamine-releasing neurons located in the substantia nigra, which play a central role in reward and motor behaviors. In particular, our work focuses on how synaptic inputs interact with active ionic conductances present in the dendrites of dopamine neurons to produce reward-relevant firing patterns such as “burst firing” – high-frequency bursts of action potentials that result in a transient spike in the concentration of dopamine released in the striatum, a brain region critical for action selection and goal-directed behaviors. The presentation will cover our recent work that examines how background tonic activity shapes dendritic excitability and burst firing in dopamine neurons.]]>
We recently developed a free-propagator reweighting (FPR) algorithm that solves reaction-diffusion (RD) dynamics of systems at single particle resolution both accurately and efficiently. The full spatio-temporal resolution of this RD method allows us to model complex biological systems where details like spatial gradients or particle assembly render simple rate-based kinetics insufficient. The treatment of proteins at single-particle resolution also allows us to begin building in further molecular details into the physics of the binding interactions, such as multiple domains in proteins, rotational and orientational effects, and interaction potentials. We have recently used our method to quantify the effect of membrane recruitment in altering the equilibrium and time-scales of protein binding interactions relative to their behavior in solution, which has implications for understanding the mechanisms of clathrin-coat formation in the early stages of endocytosis. Finally, we have used our RD simulations and new theoretical results to characterize the limitations of modeling reactions dynamics in 2D using simple rate-based kinetics. The use of rate equations assumes a well-mixed system over the lengthscale of the (sub)volume being modeled, and additionally assumes a single rate-constant parameterizes association between binding partners. In 2D, however, the second assumption of a single characteristic rate constant is not generally true. Understanding when this approximation breaks down is critical both for accurate simulations and for robust parameterization of experimental binding data. We determine in what regimes a single rate is appropriate, and when additional parameterization is necessary. Because the use of rate equations provide an efficient and widely used tool for simulating reaction dynamics, we introduce a concentration dependent rate constant in 2D as an approximate representation of the more microscopic dynamics dependent on the multi-parameter model.]]>
Abstract. Neuromorphic engineering takes inspiration from biology to design brain-like systems that are extremely low-power, fault-tolerant, and capable of adaptation to complex environments. The field of neurorobotics has grown into an exciting area of research and engineering. The common goal is twofold: 1) Developing systems that demonstrate some level of cognitive ability could lead to a better understanding of the neural machinery that realizes cognitive function. 2) Deep theoretical understanding of cognition, neurobiology and behavior obtained by constructing physical systems could lead to a system that demonstrates capabilities commonly found in the animal kingdom, but rarely found in artificial systems. Because of limitations in computation, sensor technology, and robot platforms, combining large-scale neural models with robotics was difficult in the past. In a recent project, we used our GPU accelerated spiking neural network simulator to develop a large-scale model of the visual motion perception pathway in the mammalian cortex. I will present results in which we embody this model on an autonomous mobile robot that leverages smartphone technology. I will discuss the advantages of this approach and how it might lead to future neuromorphic applications.]]>
Fear and anxiety disorder have a lifetime incidence of over 25% of the population. Although the neural circuitry involved in fear conditioning in mature organisms is well understood, the development of these circuits is less well studied. However, the extant literature does suggest that the third and forth weeks of life in rodents appear to be a time of significant change in both the cognitive and behavioral mechanisms of fear as well as the underlying neurobiology. The current experiments further examine the behavioral and neurological changes that occur during this period, with a focus on medial temporal lobe cortex, the hippocampus and the amygdala. Here, I will discuss two sets of experiments. The first examines ontogeny of contextual fear conditioning by separating the contextual and aversive learning. These experiments will conclude that some aspects of hippocampus-dependent learning may be occurring earlier than previously believed. The second set of experiments examines immediate early gene (IEG) expression in the amygdala, hippocampus, perirhinal cortex, and hypothalamus during auditory and contextual fear conditioning and expression. These studies will suggest that the amygdala and perirhinal cortex are likely sites of continuing development during the periweaning period.]]>
As our understanding of biological systems as increased, so has the complexity of our questions and the need for more advanced optical tools to answer them. For example, there is a hundred-fold gap between the resolution of conventional optical microscopy and the scale at which molecules self-assemble to form sub-cellular structures. Furthermore, as we attempt to peer more closely at the dynamic complexity of living systems, the actinic glare of our microscopes can adversely influence the specimens we hope to study. Finally, the heterogeneity of living tissue can seriously impede our ability to image at high resolution, due to the resulting warping and scattering of light rays. I will describe three areas focused on addressing these challenges: super-resolution microscopy for imaging specific proteins within cells down to near-molecular resolution; plane illumination microscopy using non-diffracting beams for noninvasive imaging of three-dimensional dynamics within live cells and embryos; and adaptive optics to recover optimal images from within optically heterogeneous specimens.]]>
Corticostriatal plasticity facilitates action selection and skill learning through dynamic enhancement (“long term potentiation” or LTP) and reduction (“long term depression” or LTD) in communication strength between neurons. Striatal primary neurons are divided into two classes: motor-enhancing “direct” and motor-suppressing “indirect” pathway neurons. The regulation of plasticity in these two classes is critical because pathway imbalance is a noted feature in Parkinson’s disease, and strong class-specific plasticity accompanies exposure to drugs of abuse. Thus, it is important to understand striatal plasticity not only to identify neural learning mechanisms, but also because dysregulation of plasticity processes serving learning contributes to disease states.
Dorsal striatal LTP has been difficult to induce in brain slices without resorting to unrealistic electrical or chemical treatments. Thus, the first research aim is to develop a striatal LTP induction paradigm that resembles brain activity observed during learning behavior. I achieve this by developing a theta-burst stimulation (TBS) protocol modeled after in vivo striatal activity during learning. I show the evoked LTP is indeed reliant on kinases and neurotransmitter receptors implicated in learning. This is a powerful tool for any researcher interested in recreating naturalistic striatal plasticity in acute brain slice.
The second research aim addressed within this dissertation is to clarify the relationship between striatal plasticity and learning behavior. Prior works show a transition in the engaged dorsal striatal subregion as skill performance shifts from an attentive phase to a more habitual phase. In addition, increased striatal activity in one hemisphere is known to generate contralateral turning behavior. Thus, I analyze striatal subregional plasticity at different time points as animals learn to execute a consistently rewarded T-maze turn, and further characterize lateralized striatal plasticity as animals are trained to turn. I find that modifications in evoked plasticity and in intrinsic neuronal excitability differ between hemispheres relative to the direction of the trained turn. More significantly, I find that striatal LTP and LTD are independently modulated during learning rather than reciprocally related as previously suggested. Finally, analysis of neuronal morphology reveals novel dendritic pruning in trained animals, without a change in spine density. This dendritic pruning may enhance signal to noise ratio of information transmission through the striatum.
The third research aim addressed within this dissertation is to identify additional factors influencing induction of LTP, such as pathway specificity, and involvement of other signaling molecules. Direct and indirect pathway neurons co-release distinct neuropeptides, including opioids known to influence motivational and addictive states. Whether LTP is naturally expressed in both pathways is not known, and little is known about potential intra-striatal pathway interaction via the co-released neuropeptides. Recordings from single striatal neurons suggest that both pathways express LTP. By genetically expressing channel rhodopsin in either pathway to elevate pathway-specific co-release during TBS LTP induction, we identify a mechanism whereby direct pathway neurons suppress corticostriatal LTP, possibly via reduced intra-striatal dopamine release.
In summary, the work comprising this dissertation furthers the field of striatal learning and plasticity by supplying a robust, physiological LTP induction method, and by using this new method to demonstrate altered striatal plasticity consequent to striatal dependent learning. Finally, revealing LTP modulation by endogenous opioids has major implications for understanding the aberrant learning involved in addiction to drugs of abuse.]]>
Much of the discussion of complexity focuses on the science of complexity. In this talk I will focus on the implications of complexity for public policy–how the advances in complexity science changes the way economists frame policy. I begin by reviewing how the economics profession developed its current neoclassical “market failure” policy frame. I then discuss how complexity science provides an alternative policy frame that encompases a wide range of political and ideological views.. I argue that this complexity policy frame reflects much more of a Millian classical economic policy approach in which economic science and policy are much more strongly separated than does the current neoclassical policy frame. I conclude with some specific examples of how economist’s policy analysis will change if the complexity vision of the economy is adopted.]]>