Math 3375, Fall 2010
Brent Doiron
MATH 1370 - Assignment 1
1. Synaptic integration and coincidence detection
Consider a leaky integrate-and-re neuron driven by a synaptic train. The membrane
dynamics of the neuron obeys:
dV
+ gCL (V VCL ) + gsyn y (t)(V V
Math 3375, Fall 2010
Brent Doiron
MATH 1370 - Assignment 3
1. Synaptic balance and uctuation driven activity
In this question you will explore how the variability of mixed excitatory and inhibitory
synaptic inputs translates to the variability of the neur
Math 1370, Spring 2013
Brent Doiron
MATH 1370 - Assignment 4
1. Firing rate adaptation and competition models. In this problem you will consider the eects of spike rate adaptation on ring dynamics. Particular attention will
be given to two population mode
Math 3375, Fall 2010
Brent Doiron
MATH 1370 - Assignment 2
1. Class I and Class II excitability
Consider the planar neural model
dV
+ gN a m (V )(V VK ) + gK n(V VK ) + gCL (V VCL ) = I (t),
dt
dn
= n (V ) n.
n (V )
dt
C
Parameters and functions can be fo
MATH 1370 Project
Computational neuroscience is a eld that applies mathematical analysis to problems motivated by experimental ndings in neuroscience. However, there are many experimental
sub-communities within neuroscience, and naturally computational ne