NSCI 303 - MT1 Lecture 4: Compartmental Modeling

length constant

• indicates distance a voltage signal passively spreads along a dendrite or an axon

• lamda = sqrt(rm/ri) = sqrt(drm/4ri)

• rm needs to be large to precent leaky current

• ri needs to be low for least amount of decay

voltage change of a signal over a distance

• V(x) = V0e^(-x/lamda)

• V0 = when signal is first introduced

rm

decreases with both dia

shunting inhibition

• decreases rm by opening channels = EPSPs leak out

• most effective at soma and proximal dendrites where AP is generated

compartmental modeling

• model behaviour of a single neurons or networks of neurons by considering their geometry and distribution of channels throughout the cell

• computational approach that represents neurons as many small cylindrical compartments and HH equation is applied to each compartment

• each compartments have their own ion channels (ligand-gated/v-gated)

• properties of channels and conductances do not vary within one compartment

compartmental modeling data

• accurate measurements for neuron and dendrite geometry

• values for rm, ri and cm

• distribution of ion channels throughout the cell

• identity of ion channels (ligand/v-gated)

• synapse locations on the cell

• conductance change and time course for each synapse

compartmental modeling goals

determine relationship between synaptic inputs and neuronal spiking activity

compartmental modelling limitations

• many assumptions, estimates and guesses have to be made

• resulting model will have many free parameters

• exponential number of combinations

• lack of objective criteria

Questions compartmental modeling addresses

• How many inputs have to be active within a given time window for the cell to fire?

• Does the location of where inputs are given matter?

• How is neuronal firing related to the neuron geometry?

spine geometry

• long and thin neck: ri of neck is vv high = large EPSP in spine head, small EPSP in the dendrite

• short and wide neck: ri of neck is vv low = smaller EPSP in spine head, large EPSP in the dendrite

Mainen and Sejnowski

David McCormick identified four types of cortical neurons differentiable by their firing activity. These neurons are fast spiking, regular spiking, intrinsic bursting, and chattering. Mainen and Sejnowski sought out to investigate whether or not the geometry of these neurons was responsible for the four unique firing patterns. They used compartmental modelling to accurately reconstruct the geometry of the different neurons and then ran a simulation to see how the models would respond to injected current. Importantly, channel types and conductances were assumed to be constant across the models, so differences in spiking would only be explained by differences in geometry. They were able to accurately recreate the firing activity of the cortical neurons using compartmental modelling.