Past events

Multiple lines of evidence link numerous diseases to inherited errors in alternative splicing, the process connecting different exon and intron sequences to diversify gene expression. We explore potential involvement of acquired alternative splicing changes in non-familial Alzheimer's and Parkinson's diseases (AD, PD), where synaptic functioning fails and cholinergic or dopaminergic neurons die prematurely. Using whole genome microarrays, we found massive decline in exon exclusion events in the AD entorhinal cortex. In brain-injected mice, blocking exon exclusion caused learning and memory impairments and destruction of cholinergic neurons caused AD-like changes in exon exclusion. Suggesting physiological relevance, blocking exon exclusion in primary neuronal cells was preventable by cholinergic stimulation and caused dendritic and synapse loss. In comparison, blood leukocytes from advanced PD patients showed different alternative splicing changes. These were largely reversed by deep brain stimulation (DBS), which reduces motor symptoms, and were reversed again after disconnecting the stimulus. Measured modifications correlated with neurological treatment efficacy and classified controls from advanced PD patients and pre- from post-surgery patients. In an independent patient cohort, a "molecular signature" (6 out of the modified transcripts) further classified controls from patients with early PD or other neurological diseases. Our findings demonstrate functionally relevant disease-specific alternative splicing changes in the AD brain and PD leukocytes; highlight acquired alternative splicing changes as causally involved in different neurodegenerative diseases and identify new targets for intervention in DBS-treatable neurological diseases.

Neuronal processing has a high energetic cost, all of which is supplied through brain vasculature. What are the design rules for this system? How is flow controlled by neuronal activity? How do neurons respond to failures in the vasculature? Theses questions will be addressed at the level of necortex in rat and mouse. An essential aspect of this work is the use of nonlinear optical tools to measure and perturb vasodynamics and automate the large-scale mapping of brain angioarchitecture.

One main alteration in neural function observed in human cocaine addicts is reduced function in the medial prefrontal cortex (mPFC). However, whether altered function of the mPFC precede, or result from, excessive self-administration of cocaine, and the exact neurochemical changes it consists of, is still unknown. To answer these questions, one needs an appropriate animal model of addiction. As, it is well established that differences in the route of, and control over, cocaine-administration, or in the frequency and size of the daily-dose of cocaine, result in significant differences in cocaine-induced neurochemical effects; then if we are to better understand the neuroadaptations that underlie the development of addiction in humans, we should employ animal models that mimic as closely as possible the human situation. Hence, my lab utilize an animal model that employs intravenous self-administration of cocaine, under conditions (based on Ahmed & Koob, 1998) that distinguish the effects of brief versus extended daily access to cocaine upon both behavior and neural substrates. This permits the investigation of neuroadaptations associated with the transition from the drug-naïve state to controlled drug-use, versus the further adaptations associated with the transition from controlled to compulsive drug-use. Using this model, we measured basal, as well as cocaine-induced, release of glutamate and dopamine within the mPFC during and after various levels of exposure to cocaine. The differences we found between controlled and compulsive drug-states, will be discussed in this talk.

In order to function adaptively in a complex environment, individuals must both react to environmental threats and modify their reactions as circumstances change. A large body of work employing Pavlovian conditioning paradigms has generated a detailed neuroscientific understanding of how fear responses are acquired. More recent research has begun to probe the various means by which learned fear can be diminished. The vast majority of this research focuses on the mechanisms that underlie typical responding in an idealized “average” individual. A robust model of fear learning must also account for the substantial variability in fear reactivity and regulation that exists between individuals. The experiments presented here explore neurobiological and experiential factors that are associated with individual variation in the expression and regulation of conditioned fear using psychophysiology, neuroimaging, and behavioral genetics.

Abstract: The intent of this presentation is to help bridge the gap between the clinical realm and the research laboratory. The clinical literature has a growing mass of evidence showing how disorders such as epilepsy, Alzheimer’s disease, stroke, or surgically-induced injury to peripheral nerve, can have devastating effects on olfactory and gustatory functions. A loss of function might be an early symptom with diagnostic value that helps the clinician identify the disease state. The presentation will introduce the non-clinician to common diagnostic and experimental tests of olfactory and taste functions. Various causes of olfactory loss will be discussed, plus their therapy

Several state-of-the-art computer vision systems for face detection, e.g., Viola-Jones [1], rely on region-based features that compute contrast by adding and subtracting average image intensity within different regions of the face. This is a powerful strategy due to the invariance of these features across changes in illumination (as proposed by Sinha [2]). The computational mechanisms underlying face detection in biological systems, however, remain unclear. We set to investigate the role of region-based features in the macaque middle face patch, an area that consists of face-selective neurons. We found that individual neurons were tuned to subsets of contrast relationships between pairs of face regions. The sign of tuning for these relationships was strikingly consistent across the population (for example, almost all neurons preferred a lower average intensity in the eye region relative to the nose region). Furthermore, the pairs and polarity of tuning were fully consistent with Sinha’s proposed ratio-template model of face detection [2]. Non-face images from the CBCL dataset that contained correct contrast polarities in pre-defined regions (facial parts) did not elicit increased firing in face-selective neurons, suggesting that the neurons are not only computing averaged intensity according to a fixed template, but are also sensitive to the specific shape of features within a region. [1] Robust Real-time Object Detection, Paul Viola and Michael Jones. Second International Workshop on Statistical and Computational Theories of Vision – Modeling, Learning, Computing, and Sampling. Vancouver, Canada, July, 2001. [2] Qualitative Representations for Recognition, Pawan Sinha. Proceedings of the Second International Workshop on Biologically Motivated Computer Vision, Tubingen, November, 2002.

During the first year of life, babies learn skills of movement which serve them not only for their present stage, but are building blocks for future stages. There are special qualities of the learning process in early development stages, which allow the learned experiences to be generalized in later stages. For example the skills that are learned in horizontal locomotion (crawling) are also applied in walking. This learning process is playful and rich with mistakes It is complex in the sense that at each moment there is an overlap of a few functions. For example, keeping the balance while lifting a toy.By observing video clips of a few babies playing, we will see some of the necessary qualities of the learning process. We will also see movement lessons given to disabled children, providing them with the normal ingredients of the learning process, in spite of their disabilities.

Since the first recordings made of evoked action potentials it has become apparent that the responses of individual neurons to ongoing physiologically relevant input, are highly variable. This variability is manifested in non-stationary behavior of practically every observable neuronal response feature. We introduce the Neuronal Response Clamp, a closed-loop technique enabling full control over two important single neuron activity variables: response probability and stimulus-spike latency. The technique is applicable over extended durations (up to several hours), and is effective even on the background of ongoing neuronal network activity. The Response Clamp technique is a powerful tool, extending the voltage-clamp and dynamic-clamp approaches to the neuron's functional level, namely-its spiking behavior.