Key concepts Flashcards
(34 cards)
current
I
the rate of flow of electrons-inverse (positive flow)
amperes
flow of charge It=Q
voltage
V
volts
energy required to move charge through an electrical field
resistance
R
Ohms
inverse to current
resists flow of current
capacitance
C
farads (F)
conductive materials with insulator between charge builds on either side
charges up until charge= that of battery
Ohms law
V=IR
R circuit
resistor, battery circuit
resistors in series
Req= R1 + R2 + R3
Batteries in series
sum of batteries in same direction
resistors in parallel
1/Req= 1/R1 +1/R2 + 1/R3
Kirschoff’s current law
sum of all currents at a node is zero
Kirschoff’s voltage law
around loop net change in voltage is zero
RC-circuit
resistor capacitor circuits
capacitor equation
Q=CV
which varies with time and which is constant?
Q=CV
Q and V vary
C is a contant
relationship between I, C and V?
I(t) = C dV/dt
voltage across battery
fixed with time
voltage across capacitor and resistor
varies as a function of time
LIF model
description of neuronal behaviour
2 components: leaky-integrator and firing threshold
leaky integrator- the neuron is modelled on RC circuit. Capacitor=membrane and resistor= ion channels
Tm dV/dt= -(Vm-Vrest)+ R*I(t)
basically saying that time constant (CmRm) and rate of change of membrane with respect to time = leak + input
balance between bleak and input determines dynamics
firing threshold- the neuron fires an AP when membrane potential reaches a threshold value then resets
if LIF neuron with 0Vm and no Iinj has initial V(0). What is membrane potential after time 2T
Vm=V(0) e ^-t/tau
t=2T
Vm=V(0) e^-2= 0.135 volts
LIF model firing rates
the rate at which the neuron generates APs in response to incoming stimuli- number of spikes generated per period of time (Hz/s). influenced by dynamics of membrane potential and threshold
LIF input current
external stimuli/synaptic inputs received by neuron over time - effect firing behaviour
F-I curves LIF model
the firing rate as a function of input- LIF model gives a linear relationship- which isn’t real due to refractory period
refractroy period
after-hyperpolarisation
where neuron is less responsive to additional inputs
LIF synaptic input
input from other neurons
excitatory and inhibitory
spatial summation- input from several neurons summates
temporal summation- input over time summates
synaptic weight- strength of connection