Week 4: Summary of Firing Rate Model Flashcards
Rate model is a non-spiking relative of the
IF (integrate and fire) model
The rate model models the
average firing of a neuron/cell over time
The rate model does not model
synaptic action
The rate model is just the summation of
incoming inputs
Diagram of the rate model
In rate model. a transfer function is added to activation ‘a’ variable
For instance, a sigmoid such that (2)
negative ‘a’ values gets mapped to 0 (since firing rate can’t be 0)
and positive values saturate (since a neuron can only emit so many spikes per second)
Rate model equation in words is change in activation =
activation + weighted firing rates of inputs
Diagram of rate model differential equation
In rate model, spilt the change in da/dt to calculate
a2 from values of a1 and other inputs
Rate model equation to calculate a2
Rate model equation: What does derivative da/dt mean? (2)
differences in ‘a’ between two time points
(a2 - a1)/(t2-t1)
Firing rate model equation is when
putting ‘a’ through transfer function
In rate model before ‘a’ put through transfer function is interpreted as
activation of a neuron
The transfer function in rate model turns ‘a’ into
firing rate
The ‘a’ in the rate model will
decay like MP but in this model speak of only firing rate
The rate model will capture but not..
dynamics but does not produce spike times (i.e., non-spiking model)
The firing rate model will assume that the average firing rate of neurons (in response to incoming inputs)
will adequately capture the fundamental properties of a neuron in a neuronal network
Any phenomena that is reliant on exact and accurate spike times can not be modelled with… but instead modelled with (2)
firing rate model
lisman-idiart model
The firing rate model is a simpler model but still captures
dynamic changes in activity (i.e., averaging AP spikes of a neuron)
Level of simple to complex models - rate-coded neurons diagram
