Chris Eliasmith, Director of the Centre for Theoretical Neuroscience, received the prestigious John C. Polanyi Award and is also an inaugural member of the Royal Society of Canada's College of New Scholars, Artists, and Scientists.
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 (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 sign up here.
Here is a list of our upcoming speakers for the 2013 and 2014 academic year:
September 10, 2013 - Ian Bruce (McMaster University)
October 8, 2013 - Kaori Nishiuchi-Takehara (University of Toronto)
October 29, 2013 - Surya Ganguli (Stanford University)
December 3, 2013 - Jose Carmena (UC Berkeley)
January 14, 2014 - Victoria Booth (University of Michigan)
March 18, 2014 - Richard Zemel (University of Toronto)
April 7, 2014 - Waterloo Brain Day (8th annual)
Date: Tues., Sept 10, 2013
Location: PAS 2464
Time: 3:30 p.m.
Speaker: Ian Bruce (McMaster University)
Title: When Things Go Bad with Cortical Plasticity: Modeling the Central Effects of Hearing Loss
Abstract: The mammalian auditory periphery performs a highly-sophisticated nonlinear, time-varying, context-dependent time-frequency analysis of acoustic signals. On top of this, plasticity in the primary auditory cortex allows its neurons to develop a multiplicity of receptive fields that it can switch between, depending on the acoustic environment or perceptual task the listener is engaged in. When damage or disease in the auditory sensory epithelium gives rise to “hearing loss”, the resulting distortions to the neural representation of acoustic signals cause substantial difficulty in many listening situations, even with the use of hearing aids. Further compounding these peripheral impairments are subsequent changes in the central auditory system that give rise to perceptual problems such as loudness recruitment (an abnormally-steep growth of loudness with acoustic intensity) and tinnitus (a phantom auditory percept in the absence of any external acoustic stimulus).
In this talk, I will describe a physiologically-accurate model of the mammalian auditory periphery, including the effects of hearing loss, that has been developed in my lab, as well as a model of primary auditory cortex developed with colleagues at McMaster. Using the combined models, we have investigated how impaired peripheral representations may induce maladaptive plastic changes in the neural circuits of the auditory cortex. The models are able to replicate a wide range of data from animal neurophysiological experiments. Simulation results indicate that homeostatic plasticity (HSP) and spike-timing-dependent plasticity (STDP) may each contribute to different aspects of the aberrant response properties of cortical neurons following peripheral hearing loss. HSP appears to generate abnormally-high levels of spontaneous activity and synchrony, which are likely neural correlates of tinnitus, while STDP can lead to cortical frequency map reorganization if the hearing loss is severe enough.
Date: Tues., Oct. 8, 2013
Location: PAS 2464
Time: 3:30 p.m.
Speaker: Kaori Nishiuchi-Takehara (University of Toronto)
Title: Physiology of Frontal-temporal Lobe Memory Network and Its Dysfunction in Early Stage Alzheimer’s Disease
Abstract: Memories of daily experiences consist of complex associations of sensory, action, and emotional variables. Accumulating evidence suggests that this type of memory is supported by a broad network that includes the hippocampus as well as rhinal and medial prefrontal cortices. Yet, network mechanisms of these memories remain largely unknown. My talk will first highlight our latest research on the representation of associative memory in rat medial prefrontal cortex. Second, I will present physiological activity patterns that suggest that the medial prefrontal cortex interacts with the rhinal cortex during memory formation and expression. Finally, I will also present observations of abnormal memory-related neurophysiological processes in a rat model for early stage Alzheimer’s disease. Together, these results provide new insight into how cortical neurons mediate associative memory and how they are disrupted with pathologies in early stage Alzheimer’s disease.
Date: Tues., Oct. 29, 2013
Location: PAS 2464
Time: 3:30 p.m.
Speaker: Surya Ganguli (Stanford University)
Title: A Theory of Neural Dimensionality and Dynamics
In a wide variety of experimental paradigms, neuroscientists tightly control behavior, record many trials, and obtain trial averaged neuronal firing rate data from hundreds of neurons, in circuits containing millions to billions of behaviorally relevant neurons. Such datasets are often analyzed by dimensionality reduction methods that allow us to visualize neuronal dynamics through their projections onto a number of basis patterns. Strikingly, recordings from hundreds of neurons can often be described using a much smaller number of dimensions (basis patterns), and the resulting projections yield a remarkably insightful dynamical portrait of neural circuit computation. Thus many neuronal datasets are surprisingly simple, and we seem to be able to extract reasonable collective neuronal dynamics despite overwhelming levels of neuronal subsampling. This ubiquitous simplicity raises several profound and timely conceptual questions. What is the origin of this simplicity? What does it tell us about the complexity of brain dynamics? Would neuronal datasets become more complex if we recorded more neurons? How and when can we trust dynamical portraits obtained from only hundreds of neurons in a circuit containing billions of neurons? More generally, what, if anything, can we learn about a complex dynamical system by measuring an infinitesimal fraction of its degrees of freedom? We present a theory of neural dimensionality and dynamics that answers all of these questions, and we further test this theory in neural recordings from monkeys performing reaching movements.
Date: Tues., Dec. 3, 2013
Location: PAS 2464
Time: 3:30 p.m.
Speaker: Jose Carmena (UC Berkeley)
This event has been CANCELLED
Date: Tues., Jan. 14, 2014
Location: PAS 2464
Time: 3:30 p.m.
Speaker: Victoria Booth (University of Michigan)
Title: Dynamics of Sleep-wake Regulation
Sleep and wake states are regulated by the interactions among a number of brainstem and hypothalamic neuronal populations and the expression of their neurotransmitters. Based on experimental studies, several different structures have been proposed for this sleep-wake regulatory network with particular debate over components involved in rapid-eye movement (REM) sleep regulation. We have developed a mathematical modeling framework that is uniquely suited for investigating the structure and dynamics of proposed sleep-wake regulatory networks. Using this framework, we are analyzing the competing proposed network structures for the regulation of REM sleep to determine how the structure of the sleep-wake regulatory network determines sleep-wake behavior and the dynamics of behavioral state transitions.
Date: Tues., Mar. 18, 2014
Location: PAS 2464
Time: 3:30 p.m.
Speaker: Richard Zemel (University of Toronto)
Title: Neural Representations in a Dynamic Uncertain World
As animals interact with their environments, they must constantly update estimates about relevant states of the world. For example, a batter must rapidly re-estimate the velocity of a baseball as he decides whether and when to swing at a pitch. Probabilistic models provide a description of optimal updating based on prior probabilities, a dynamical model, and sensory evidence, and have proved to be consistent with the results of many diverse psychophysical studies. In this talk I will consider three basic questions. First, how can populations of neurons represent the uncertainty that underlies this probabilistic formulation? Second, how can neural spikes be used to represent adaptive computation? And finally, how can the population responses be learned from interaction with the environment? I will describe some recent progress in machine learning that suggests that predictions of behavior can be improved by incorporating particular constraints in a learning model.
Date: Mon., Apr. 7, 2014
Location: EV3 1408 (Environment 3 building, room 1408)
Time: 8:30 a.m. - 5:00 p.m.
Title: 8th Annual Waterloo Brain Day
