Neural Signal Processing
ELEC 548 Fall 2015
Tuesday/Thursday 09:25AM - 10:40AM
BRC 284
are you in the right room?
How does the brain represent and process
informaon?
How can we (experimenters) process and
understand th
Synaptic Plasticity
Synaptic Transmission
Chapters 10 and 12 of Principles of Neural Science
(PNS).
The point at which one neuron communications with
another is called a synapse.
The average neuron forms about 103 synaptic
connections and receives > 10
ELEC548 / BIOE548 Fall 2014
Neural Signal Processing
Neural Signal Processing!
Firing rates and spiking statistics
What is Neural Signal Processing?
What we have
Goals
Novel experimental
paradigms
Further our basic
understanding of
brain function
New neur
ELEC548 / BIOE548 Fall
2014!
Neural Signal Processing
Kalman Filter
Joint Gaussian
ELEC 548 Fall 2014!
Neural Signal Processing
2"
Observation Model
ELEC 548 Fall 2014!
Neural Signal Processing
3"
Add dynamics
"
ELEC 548 Fall 2014!
Neural Signal Processin
Modeling Neurons
Eect of the Dendritic Cable
Neurons are not spherical nor just a single RC Compartment
Nature Neurosci 3:895
Modeling Dendrites (Cable Equation)
The Cable Equation
ra
Axial resistance of unit length cable
rm
Membrane resistance of unit le
PNS Chapter 9
Propagated Signaling:
The Action Potential
Plan of Action
Introduction to neuroscience
Chapter 1 The brain and behavior
Chapter 2 Nerve cells and behavior
How are neural signals generated?
Chapter 7 Membrane potential
Chapter 9 Propaga
PNS Chapter 7
Membrane Potential
Plan of Action
Introduction to neuroscience
Chapter 1 The brain and behavior
Chapter 2 Nerve cells and behavior
How are neural signals generated?
Chapter 7 Membrane potential
Chapter 9 Propagated signaling: the actio
Problem Set 1
ELEC 548 Neural Signal Processing
Fall 2013
Problem Set 1
This problem set is due Tuesday Sept. 17th at 5pm. Please turn in your work, including all Matlab code and plots, by
uploading to OwlSpace, or in the boxes outside BRC740 or DH2046.
I
PNS Chapters 10 and 12
Synaptic Transmission and
Integration
18-699 / 42-590
Neural Signal Processing
Spring 2010
Prof. Byron Yu
Roadmap
Introduction to neuroscience
Chapter 1 The brain and behavior
Chapter 2 Nerve cells and behavior
How are neural sign
Pattern Recognition
and Machine Learning
Summary Week 10
Lecturer: Matthias Seeger
Assistants: Nikolaos Arvanitopoulos, Young Jun Ko, Carlos Stein, Friedemann Zenke
Tuesday
Recap of representer theorem
Kernel functions
Derivation as inner product for s
Pattern Recognition
and Machine Learning
Summary Week 11
Lecturer: Matthias Seeger
Assistants: Nikolaos Arvanitopoulos, Young Jun Ko, Carlos Stein, Friedemann Zenke
Tuesday
KKT optimality conditions for SVM dual problem
Support vector (essential and bou
Pattern Recognition
and Machine Learning
Summary Week 14
Lecturer: Matthias Seeger
Assistants: Nikolaos Arvanitopoulos, Young Jun Ko, Carlos Stein, Friedemann Zenke
Tuesday
Convergence proof for K-means algorithm
Energy function (t, )
It is possible fo
Recitation 21
A tour of Fourier representations
After studying this chapter, you should be able to:
explain the connection between Fourier series and transforms in terms of sampling the
frequency representation; and
explain the connection between discre
Recitation 23
Interpolation as reconstruction
After studying this chapter, you should be able to:
explain bandlimited, piece constant, and piecewise linear reconstruction using either the
time or frequency representations; and
write a program that uses
Recitation 22
Sampling and reconstruction
After studying this chapter, you should be able to:
reconstruct bandlimited signals from their samples, if the samples are frequent enough;
and
explain the factor of 2 in the sampling theorem.
Unless you believe
Recitation 19
Fourier transform
After studying this chapter, you should be able to:
derive the analysis and synthesis equations for Fourier transforms from their counterparts for relations for Fourier series;
simplify convolution problems by using the f
Recitation 9
Image processing using operators
The goals of this chapter are:
to analyze spatial signals using functionals (or operators);
to look in slow motion at how to invert blurring operations;
to notice non-idealities, such as quantization error,