Here is a list of our speakers for the 2017 and 2018 academic year:
September 26, 2017 - Blake Richards
October 24, 2017 - Jorn Diedrichsen
November 7, 2017 - Mariam Aly
January 23, 2018 - Steve Prescott
March 6, 2018 - Aimee Nelson
March 14, 2018 - Lana Trick
April 6, 2018 - Waterloo Brain Day (12th annual)
Tuesday, September 26, 2017
3:30 p.m. - 5:00 p.m.
Department of Cell & Systems Biology
University of Toronto
Deep Learning with Pyramidal Neurons
Deep learning has led to significant advances in artificial intelligence, in part, by adopting strategies motivated by neurophysiology. However, it is unclear how deep learning could occur in the real brain, due to the difficulty of performing credit assignment without backpropagation. Here, we show that deep learning can be achieved in a biologically feasible simulation by moving away from point neuron models and towards multi-compartment neurons. Like neocortical pyramidal neurons, neurons in our model receive feedforward sensory information and higher-order feedback in electrotonically segregated compartments. Thanks to this segregation, the neurons in different layers of the network can coordinate local synaptic weight updates to achieve global optimization. As a result, the network can take advantage of multilayer architectures---the hallmark of deep learning. This work demonstrates that deep learning can be achieved using segregated dendritic compartments for feedforward and feedback information, which may help to explain the dendritic morphology of neocortical pyramidal neurons.
Brain and Mind Institute
University of Western Ontario
The Brain’s GPU? In Search of the Cerebellum’s Universal Transform
The cerebellum exhibits a highly specialized and uniform neuronal circuitry, which likely evolved to solve very specific problems in sensory-motor control. In the human brain, the cerebellar circuitry has dramatically expanded, and it contributes here to virtually every possible cognitive function. But what is this elusive computation that the cerebellum contributes to cortical processing? I will show some insights from our research on motor control and learning, which shows that the cerebellum is critically involved in prediction and fast error-based learning. I will then talk about the first steps that we have recently taken to test computational theories about the function of the cerebellum across different cognitive domains using functional imaging.
Perception and Attention in Memory Systems
Research in cognitive neuroscience has traditionally progressed by studying different components of cognition largely in isolation. But, ultimately, complex behavior is the result of the interplay between multiple aspects of cognition at the behavioral and the neural levels. With a combination of behavioral, neuroimaging (fMRI) and patient studies, I’ll argue that the computations performed by brain regions allow them to critically and flexibly support many different aspects of cognition, from attention to perception to long-term memory. In the first part of my talk, I’ll show that at a behavioral level, perception shares functional commonalities with long-term memory, and traditional memory systems of the brain play a critical role in perception. In the second part of my talk, I’ll show that attention modulates these “memory systems”, and that this modulation has consequences for attentional and mnemonic behavior. Together, my research points to the utility of understanding the brain and behavior by thinking about the mechanisms that allow any given brain region to flexibly contribute to diverse aspects of cognition.
University of Toronto
Somatosensory Coding Gone Wrong: The Origins of Neuropathic Pain
Pain is the normal sensory response to noxious stimulation. But pathological changes in neural coding can result in innocuous somatosensory input being misperceived as painful. This so-called neuropathic pain is notoriously difficult to treat and is, therefore, a significant clinical problem. In my presentation, I will discuss our ongoing work to uncover the cellular and circuit properties that control how somatosensory information is normally processed, and how pathological changes in those properties contribute to neuropathic pain. The identification that certain pathological states are degenerate (i.e. can be produced via distinct molecular changes) may help explain why neuropathic pain is so difficult to treat.
Changes in the organization of the motor cortex that follow incomplete spinal cord injury
Movement training for improving upper limb control is an essential component of rehabilitation for individuals with spinal cord injury (SCI). Understanding the cortical representation of arm muscles in SCI is fundamental to designing more effective movement training regimes. In uninjured individuals, the primary motor cortex (M1) contains overlapping muscle representations, an organization that reflects muscle synergies. This organizational feature has yet to be studied in SCI yet is considered a key element that defines the coordinated action of multiple muscles during human movement. Using Transcranial magnetic stimulation (TMS), we investigated the bilateral representation and overlapping distribution of muscles of the upper limb in chronic cervical SCI and aged-matched controls (n=9, each group). Muscles studied included the abductor pollicus brevis (APB), flexor carpi radialis (FCR) and biceps brachii (BB) and the cortical territory (cm2), overlapping territory (cm2) of the target muscles, and center of gravity were computed. Results indicate a reduction in the cortical territory dedicated to all three muscles in SCI (i.e. reduced complete overlap) compared to uninjured controls. Further, SCI had greater cortical territory dedicated to a single or dual muscle representation. These data indicate that overlapping organization is preserved in the motor cortex of SCI, however, the overlapping representation does not extend to all three muscles. The implication from these data is that movement training emphasizing synergies that incorporate all three muscles (APB, FCR, BB) may promote greater representational overlap (similar to uninjured controls) and provide functional gains in motor control.
University of Guelph
The long drive home: Secondary tasks, mind-wandering and finding the happy medium between over- and underload
In Canada, many individuals put in long hours on the road driving to and from work. On an undemanding and familiar drive, driving may not use of all of the available cognitive resources, leaving the driver in conditions of underload. Under those circumstances drivers may be tempted to engage in external secondary tasks such as cell phone conversations and texting, which overload the drivers’ attentional resources. However, performance may also deteriorate even without external secondary tasks, as occurs with mind-wandering. When this occurs, drivers withdraw their attention from the external task of driving over time and become absorbed in their own thoughts. A series of studies are presented in which a driving simulator is used to assess changes in driving performance as a function of time, secondary tasks, and individual differences related to working memory and sustained attention.