CNS*2010 - Tutorials

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Neural Signal Processing Algorithms for Neural Spike Trains (3 hours)
Emery N. Brown (MIT, USA)

One of the principal ways through which neurons represent and transmit information is in their spiking activity. Methods to analyze neural spike trains therefore play an important role in helping to understand function in the brain and central nervous system. In this tutorial we will review methods for single and multiple neural spike train data analysis. Lecture 1 will review of the theory of point processes and the use of the generalized linear model to relate spiking activity from single neurons to implicit and explicit stimuli. Lecture 2 will review likelihood methods for simultaneous analysis of multiple single neurons. Lecture 3 will present methods for dynamic analyses of neural spike trains including point process adaptive filters and neural spike train decoding algorithms. All methods will be illustrated using actual experimental examples.

Dynamical systems approaches to understanding neural models (3 hours).
Bard Ermentrout (Pittsburg, USA.)

I will use an open source software package, XPPAUT, to show how dynamical systems methods can be used to investigate a number of problems in computational neuroscience. These range from simulations of single channels, through neurons, and networks. I will briefly touch on a number of numerical and mathematical methods that can be used to understand synchrony, spatial patterns, and the role of noise.

Network models of short-term memory, persistent neural activity, and neural integration (3 hours).
Mark Goldman (University of California Davis, USA)

Neural activity that persists following the offset of a stimulus has been identified as a neural correlate of memory in a wide variety of systems. This tutorial will provide a mathematical foundation for building models of the neural activity observed in memory-storing networks. Current challenges to the field will be addressed and discussed. Topics to be covered include:
Linear network theory: positive and negative feedback; eigenvector and eigenvalue characterization of memory states; feedforward memory networks and failures of eigenvector analysis Robustness: How can networks built of neurons and synapses with brief decay time constants give rise to networks that can maintain memories for tens of seconds? What “tricks” might biology play that neural network modelers have failed to capture? Nonlinear networks: How can we construct memory networks with nonlinearities that cause linear systems techniques to break down?

Brute force exploration of high-dimensional neuronal parameter spaces (3 hours).
Astrid Prinz (Atlanta, USA)

The electrical activity generated by neurons and neuronal networks depends on cellular and synaptic parameters in a complex, often non-intuitive manner. This tutorial will cover a computational method that examines this activity-to-parameter relationship by systematically exploring the high-dimensional parameter spaces of neuron and network models. We will discuss advantages and disadvantages of parameter space exploration as compared to other methods of neuron and network analysis, technical issues regarding the implementation and execution of computational brute-force parameter exploration, available software tools and model databases generated with the method, and analysis and visualization techniques related to parameter space exploration. The tutorial will include hands-on exploration and visualization of an example neuronal parameter space by participants.

Neural Control Engineering - The Emerging Intersection of Control Theory and Neuroscience (6 hours)
Steven Schiff (Penn State Univ, USA)

Abstract: With the advent of model based ensemble techniques to track and control nonlinear systems in real time, the intersection between formal control theory and computational neuroscience is emerging as a powerful new area for exploration. This tutorial will explore how common models from computational neuroscience can be placed within a control theoretic framework, using a variety of cellular and network modeling frameworks. The route to real time feedback control systems will be explained with algorithm and code examples. A detailed discussion of formalizing model inadequacy will be covered. Applications to rhythmic hippocampal oscillations, seizures, Parkinson’s disease, and cortical wave formation will be discussed.

• Linear Kalman Filtering
• Nonlinear Kalman Filtering
• The Hodgkin Huxley Equations
• The Fitzhugh-Nagumo Equations
• The Bridge from Kalman Filtering to Neuronal Dynamics
• Spatiotemporal Neural Dynamics
• Empirical Spatiotemporal Models
• All Models are Wrong – Formalizing Model Inadequacy
• Parkinson’s Disease
• Controlling Neuronal Dynamics with Electrical Stimulation
• Brain Machine Interfaces
• Assimilating Real Data: Seizures and oscillations

Introduction to Computational Motor Control (3 hours).
Reza Shadmehr (Johns Hopkins, USA),

This lecture introduces the problem of motor control from a computational perspective. The act of making a movement involves solving four kinds of problems:
1) We need to learn the costs that are associated with our actions as well as the rewards that we may experience upon completion of that action.
2) We need to learn how our motor commands produce changes in state of our body and our environment.
3) Given the cost structure of the task and the expected outcome of motor commands, we need to find those motor commands that minimize the costs and maximize the rewards.
4) Finally, as we execute the motor commands, we need to integrate our predictions about sensory outcomes with the actual feedback from our sensors to update our belief about our state.

In this framework, the function of basal ganglia appears related to learning costs and rewards associated with our sensory states. The function of the cerebellum appears to be related to predicting sensory outcome of motor commands and correcting motor commands through internal feedback. Together, reward driven optimal feedback control theory appears as a consistent framework to explain a number of disorders in human motor control.