Past Colloquium Speakers 2011 and 2012

Colloquium Series

Colloquia are generally on Tuesdays at 3:30 p.m., once per month. They are usually held in the new Centre for Theoretical Neuroscience (CTN) seminar room in the Psychology, Anthropology, Sociology building (PAS) room 2464; exceptions will be noted). Abstracts are posted as available. If you'd like to be on the mailing list announcing these events, please contact us.

Here is a link for past events, colloquia, and speakers.

  1. Sept. 20, 2011 - Rutsuko Ito (University of Toronto)
  2. Oct. 25, 2011 - Peter Shizgal (Concordia University)
  3. Nov. 22, 2011 - Oleg Michailovich (University of Waterloo)
  4. Dec. 6, 2011 - Steven Scott (Queen's University)
  5. Jan. 24, 2012 - Masami Tatsuno (University of Lethbridge)
  6. Feb. 14, 2012 - Astrid Prinz (Emory University)
  7. Mar. 6, 2012 - Bard Ermentrout (University of Pittsburgh)
  8. April 4, 2012 - Waterloo Brain Day, PAS 2083

Date: Tues., Sept. 20, 2011
Location: PAS 2464
Time: 3:30 p.m.
Speaker: Rutsuko Ito (University of Toronto)
Title: Cortico-striatal Regulation of Hippocampal- and Amygdala-Dependent Reward Learning

Abstract: Traditionally, topographically organized cortico-striatal loops have been associated with the control and execution of cognitive and motor functions in the mammalian brain. A potentially important role that is not so readily attributed to the cortico-striatal system is that of its role in regulating learning and memory processes, despite knowledge that cortico-striatal circuits interface closely with key learning and memory (limbic) structures, such as the hippocampus (HPC) and basolateral amygdala (BLA). Extensive neuroanatomical and neurophysioloical evidence suggest that limbic inputs converge and overlap in discrete regions within the medial prefrontal cortex and the ventral striatum, providing support to the notion that these discrete loci in the cortico-striatal system may represent sites at which limbic inputs competitively interact to gain control over motivational behaviour. My research has thus focused on identifying the neural and neurochemical basis of the competitive interaction between hippocampal and amygdala-mediated associative information. In this talk, I will present work demonstrating that the nucleus accumbens shell may be a site at which the integration of BLA and HPC-dependent associative information occurs. Furthermore, I will present evidence that implicates the dopaminergic innervation of the nucleus accumbens in regulating the balance of limbic control over motivated behaviour.

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Date: Tues., Oct. 25, 2011
Location: PAS 2464
Time: 3:30 p.m.
Speaker: Peter Shizgal, (Concordia University) Research Chair
Title: The Neural Computation of Utility: Contributions from the Study of Brain Stimulation Reward

Abstract: Foraging entails repeated decisions about which prey objects to select, how much effort to invest in their pursuit, when to persist, and when to desist. These decisions are based on estimates of returns, costs, and risks. The phenomenon of intracranial selfstimulation has been used to implement a laboratory analog of foraging in which costs, and risks can be controlled precisely, and returns arise from an observable stream of action potentials in an identifiable population of neurons. Although the directly activated neurons responsible for intracranial self-stimulation are largely non-dopaminergic, performance is altered profoundly by changes in dopaminergic neurotransmission. Consensus has yet to be reached concerning the stage(s) of processing at which dopamine neurons intervene and how the influence of these neurons is partitioned between the investment of effort and the evaluation of returns, costs, and risks. I have developed a model linking allocation of behavior to the subjective strength, cost, and likelihood of the rewarding stimulation. Simulations of model output and analysis of empirical data demonstrate that the methods used previously to assess performance for brain stimulation reward produce fundamentally ambiguous results. Using a new measurement method that eliminates this ambiguity, we have reassessed the contribution of dopamine to performance for brain stimulation reward. The model linking behavioral allocation to the subjective strength, cost, and likelihood of reward will be described along with the changes in intracranial self-stimulation produced by cocaine and by the selective dopamine re-uptake inhibitor, GBR-12909 (this is a drug). These results will be discussed in terms of the stage(s) of processing at which dopamine influences the pursuit of rewards and in terms of the distinction between the sensitivity and gain of brain reward circuitry.

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Date: Tues., Nov. 22, 2011
Location: PAS 2464
Time: 3:30 p.m.
Speaker: Oleg Michailovich (University of Waterloo)
Title: HARDI-based Diagnosis of First Episode Schizophrenia using Isometric Embedding and Compressed Sensing

Abstract: The unique ability of diffusion-weighted MRI (DW-MRI) to generate contrast based on the morphological properties of white matter opens the door to developing qualitatively new methods of early detection and diagnosis of many brain-related disorders. Unfortunately, practical implementation of DW-MRI still poses a number of challenges which hamper its wide-spread integration into standard clinical practice. Chief among these is the problem of prohibitively long scanning times, which necessitates the development of time-efficient methods for acquisition of diffusion data. In many such methods, however, the acceleration entails a trade-off between the time efficiency and the accuracy of signal reconstruction. In such a case, it is imperative for one to be able to understand the effect the above trade-off might have on the accuracy of diagnostic inference. Accordingly, the objective of this talk is twofold. First, using high-angular resolution diffusion imaging (HARDI) as a specific instance of DW-MRI, we will introduce the notion of a directional diffusion structure which, in combination with multidimensional scaling, allows representing HARDI data in a lower dimensional Euclidean space. Subsequently, based on this representation, we will develop an algorithm for detection and classification of first episode schizophrenia. Finally, the above algorithm will be applied to HARDI data acquired by means of compressed sensing and we will demonstrate that the resulting classification error increases insignificantly when the sampling density is reduced to as low as a fourth of its conventional value.

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Date: Fri., Dec. 6, 2011
Location: PAS 2464
Time: 10:30 a.m.
Speaker: Steven Scott (Queen's University)

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Date: Tues., Jan. 24, 2012
Location: PAS 2464
Time: 3:30 p.m.
Speaker: Masami Tatsuno (University of Lethbridge)
Title: Information-geometric Measure for Estimation of Connection Strength under Correlated Input

Abstract: The brain processes information in a highly parallel manner. Determining the relationship between neural spikes and synaptic connections plays a key role in analyzing electrophysiological data. Information geometry (IG) has been proposed as a powerful analysis tool for multiple spike data, providing useful insights into the statistical interactions within a population of neurons. Previous work has demonstrated that IG measures can be used to infer the connection strength between two neurons in a neural network. This property is useful in neuroscience because it provides a way to estimate learning-induced changes in synaptic strengths from extracellular neuronal recording. A previous study has also shown, however, that this property would hold only when inputs to neurons are not correlated. Since neurons in the brain often receive common inputs, this would hinder application of the IG to real data. We investigated two-neuron-IG measures in higher-order log-linear models to overcome this limitation. First we mathematically showed that the estimation of symmetrically connected synaptic weight under correlated inputs can be improved by taking into account higher-order interactions. Second we numerically showed that the estimation can be also improved for more general asymmetrically connected networks. Considering the size of practically available data in an experiment and the number of connections in the brain, we showed that the two-neuron-IG measure calculated with 4-5 neuronal interactions would provide estimation of connection strength within approximately 10% accuracy. These studies suggest that the two-neuron-IG measure with higher-order interactions is a robust estimator of connection strength, providing a useful analytical tool for real multi-neuronal spike data.

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Date: Tues., Feb. 14, 2012
Location: PAS 2464
Time: 3:30 p.m.
Speaker: Astrid Prinz (Emory University)

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Date: Tues., Mar. 6, 2012
Location:  MC 5158
Time: 3:30 p.m.
Speaker: Bard Ermentrout (University of  Pittsburgh)
Title: Flicker Hallucinations: Faraday Waves in the Brain

Abstract: In this talk which should be accessible to both students and faculty, I first describe a striking visual illusion that occurs when the visual system is subjected to uniform bright flickering light in the 8-20 Hz frequency range. Subjects consistenlty report specific patterns at certain frequencies. We use a simple firing rate model that is spatially distributed in one or two dimensions to model the phenomena. We then use Floquet theory to analyze the regimes of stability and finally use symmetric bifurcation theory to explain why some patterns are associated with some frequencies.

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