Location: South Ken Campus, Royal School of Mines 2.28, followed by a wine reception in RSM 3.01
Usual time: 5pm - 6pm for talk.
Organiser: Aldo Faisal
Past events
Prof. Andrew Schwartz (McGowan Institute of Regenerative Medicine, University of Pittsburgh)
'Recent progress with high-performance cortical interfaces'
Monday, 16 May 2011
The emphasis on neural populations as the substrate for information processing is the most important recent advance in systems neuroscience. The change in emphasis from the single neuron to the neural ensemble has made it possible to extract high-fidelity information about movements that will occur in the near future. This ability is due to the distributed nature of information processing in the brain. Neurons encode many parameters simultaneously, but the fidelity of encoding at the level of individual neurons is weak. Because encoding is redundant— parameter representation in individual neurons is weak but consistent across the population-- extraction methods based on multiple neurons are capable of generating a faithful representation of intended movement. The realization that useful information is embedded in the population has spawned the current success of brain-controlled interfaces. Since multiple movement parameters are encoded simultaneously in the same population of neurons, we have been gradually increasing the degrees of freedom (DOF) that a subject can control through the interface. Our early work showed that 3-dimensions could be controlled in a virtual reality task. We then demonstrated control of an anthropomorphic physical device with 4 DOF in a self-feeding task. Currently, monkeys in our laboratory are using this interface to control a 7-DOF arm, wrist and hand to grasp objects in different locations and orientations. Our recent data show that we can extract 11-DOF to add hand shape and dexterity to our control set.
Prof. Reza Shadmehr (Johns Hopkins School of Medicine)
'Reward, impulsivity, and control of movements'
Monday, 4th October 2010
We do not know why our voluntary movements have certain kinematic characteristics, and why certain neurological diseases alter these characteristics. For example, we do not know why saccades of healthy people have certain peak speeds, and why in Parkinson’s disease saccades are slower, whereas in schizophrenia saccades are faster. We do not know why saccades of non-human primates can be twice as fast as humans, despite the fact that eyes of the two species have nearly identical biomechanics. We do not know why as we age, saccade speeds drop with every decade of life. I have chosen to highlight saccades because this simple movement has been studied in a large number of conditions. Yet, the problem is ubiquitous to control of movements in general: we do not know the factors that affect how the brain chooses the motor commands that control our movements. As a result, we do not know why with development, disease, or evolution, the brain alters control of our movements. Here, I propose a new theory of motor control. I suggest that the purpose of any movement is to position our body in a more rewarding state. People and other animals discount future reward as a function of time. I demonstrate a correlation between changes in this reward temporal discount function and changes in saccadic velocity and duration. I suggest that there is a link between two large bodies of science: decision making, and motor control. My results suggest that the value that the brain assigns to a stimulus, and the rate at which it discounts this value in time, form a cost that influence the motor commands that move our body. As a result, the theory predicts that motor commands reflect an economic decision regarding reward, time, and effort.
Prof Michael Häusser (UCL)
Dendritic Computation
Tuesday, 27th April 2010
The computational power of dendrites has long been predicted using modelling approaches, but actual experimental examples of how dendrites solve computational problems are rare. I will discuss results from experiments combining patch-clamp recordings with two photon imaging and glutamate uncaging demonstrating that cortical pyramidal neurons can discriminate spatiotemporal sequences of synaptic inputs along single dendrites. This provides a dendritic mechanism for pyramidal neurons to compute direction and velocity, and shows how dendrites can be used to decode spatiotemporal patterns of input.