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Simulating in vivo-like synaptic input patterns in
multicompartmental models
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What are in vivo-like synaptic input patterns?
When are such simulations useful?
How we do it using GENESIS
Some strategies for analyzing the results
Numerical estimates of in vivo input levels
100 mm
100 mm
GP neuron
Ca3 pyramidal neuron
• surface area:
17,700 mm2
• surface area:
38,800 mm2
• number of synapses (ex/in):
1,200 / 6,800
• number of synapses (ex/in):
17,000 / 2,000
• number of inputs / s
12,000 / 6,800
• number of inputs / s
170,000 / 20,000
Thousands of synapses add up to a lot of conductance!
5,000 AMPA and 500 GABAA
Synapses at 10 Hz
Ein = -70 mV
Eex = 0 mV
Isyn = Gin * (Vm - Ein) + Gex * (Vm - Eex)
Esyn = [(Gin*Ein) + (Gex*Eex)] / (Gin+ Gex)
Isyn = (Gin + Gex) * (Vm - Esyn)
Isyn = (300 nS) * (60-50mV) = 3 nA
High conductance state of neurons in vivo
Neocortical pyramidal neurons
Striatal medium spiny neuron
(D. Jaeger, unpublished)
(Pare D, Shink E, Gaudreau H, Destexhe A,
Lang EJ (1998). J Neurophysiol 79: 1460-70.)
Simulating in vivo-like synaptic input patterns in
multicompartmental models
•
•
•
•
What are in vivo-like synaptic input patterns?
When are such simulations useful?
How we do it using GENESIS
Some strategies for analyzing the results
Simulating in vivo-like synaptic input patterns in
multicompartmental models
• When are such simulations useful?
 When we want to extrapolate from in vitro data to
the in vivo case
– Intrinsic cell properties (ion channels, morphology)
– Synaptic integration
• Temporal and spatial summation
• Interactions between excitation and inhibition
When input complexity can’t be replicated in vitro
– Input correlation / synchrony
Small conductance K(Ca2+) channels (SK channels)
regulate the firing rate of Purkinje neurons in vitro…
(Edgerton JR, Reinhart PH (2003). J Physiol 548: 53-69.)
…but is this also true in vivo?
Effects of blocking SK channels in DCN neurons in vitro
(D. Jaeger, unpublished)
SK channel block in DCN neurons with in vivo-like
background conductance levels
(D. Jaeger, unpublished)
Modeled M-current (KCNQ) block with and without
simulated background synaptic input
(Destexhe A, Pare D (1999). J Neurophysiol 81: 1531-47.)
Spatial and temporal summation are reduced when
the conductance level is high
(Destexhe A, Pare D (1999). J Neurophysiol 81: 1531-47)
Input synchronization affects rate and precision
100 independent inputs
10 independent inputs
(Fellous J-M et al (2003).
Neuroscience 122: 811-29.)
200 msec
(Gauck & Jaeger, 2000.)
Simulating in vivo-like synaptic input patterns in
multicompartmental models
•
•
•
•
What are in vivo-like synaptic input patterns?
When are such simulations useful?
How we do it using GENESIS
Some strategies for analyzing the results
Steps involved in setting up the simulations
1. Cell morphology: reconstruct a filled neuron, obtain
a morphology file from a colleague or the web, or
make a simplified morphology model.
2. Passive parameters: Rm, Cm, Ri
3. Active conductances: GENESIS tabchannel objects
4. Synapse templates (AMPA, GABA, etc.):
 gmax, τrise, τfall, Erev
5. Compartments: list of those receiving input
6. For every independent synapse (in a loop):
1.
2.
3.
Copy the synaptic conductance from a template library to the compt
Create a timetable object to determine when the synapse activates
Create a spikegen object to communicate with the synapse
Element tree structure for the simulation
Root
Cellpath
Dendrite
Soma
Library
Inputs
AMPA
AMPA synapse
G_Na+
G_Na+
G_K+
G_K+
timetable
AMPA synapse
AMPA synapse
spikegen
Dendrite
G_Na+
Soma
timetable
spikegen
G_K+
1. Create synaptic conductances using synchan objects
//GENESIS script to define AMPA-type conductance
function make_AMPA_syn
// make AMPA-type synapse
if (!({exists AMPA}))
create synchan AMPA
end
// assign specific synapse properties
setfield AMPA Ek {E_AMPA}
setfield AMPA tau1 {tauRise_AMPA}
setfield AMPA tau2 {tauFall_AMPA}
setfield AMPA gmax {G_AMPA}
setfield AMPA frequency 0
end
2. Put the synaptic conductances into the library
//GENESIS script to create library template objects
//First, include my synapse and channel function files
include Syns.g
include Chans.g
//Check if library already exists
if (!{exists /library})
create neutral /library
disable /library
end
//Push library element, make conductance elements, pop library
pushe /library
make_AMPA_syn
make_G_Na
make_G_K
pope
3. For all compartments receiving input…
//Using the same random seed means you get the same timetables next time too.
randseed 78923456
//Loop: for each compartment that receives a synapse…
1. copy the AMPA synapse from the library to the compartment
2. addmsg: connect the synaptic conductance to the compartment with
CHANNEL and VOLTAGE messages
//set up the timetable
1. create a unique timetable object for this compartment’s AMPA synapse
2. set timetable fields with setfield:
method: 1 = exponential distribution of intervals
2 = gamma distribution of intervals
3 = regular intervals
4 = read times from ascii file
meth_desc1: mean interval
(= 1/rate)
meth_desc2: refractory period
(we use 0.005)
meth_desc3: order of gamma distribution
(we use 3)
3. call /inputs/Excit/soma/timetable TABFILL
3. For all compartments receiving input…
//set up spikegen
create a unique spikegen object for this compartment’s synapse
set the spikegen fields with setfield
output_amp: 1
thresh 0.5
//the spikegen tells the synapse when to activate based on the timetable
addmsg from timetable to spikegen: type = INPUT, message = activation
addmsg from the spikegen to the compartment’s AMPA element, type = SPIKE
// Next loop iteration or END
Simulating in vivo-like synaptic input patterns in
multicompartmental models
•
•
•
•
What are in vivo-like synaptic input patterns?
When are such simulations useful?
How we do it using GENESIS
Some strategies for analyzing the results
– Matlab provides a flexible platform for customization and
automation of data analysis.
– Movies can help you explain what’s going on in the model
– Compare multiple models, each representing a distinct
alternative case.
– Compare synaptic activity with output spiking for each
synapse. Look at synaptic efficacy as a function of
location.
– Analyze model input-output relations
Movie: 20 Hz excitation, 2.5 Hz inhibition
Quantifying synaptic efficacy
1. Probabilistic method:
Efficacy = P (output spike | synaptic activation) / P (output spike)
Advantage: need only the output spike times and synapse timetables.
Disadvantage: a time window must be chosen (usually arbitrarily),
and the best time window may vary with output spike rate.
2. Average synaptic conductance method:
Efficacy = peak of synapse’s spike-triggered average conductance
Advantage: no arbitrary time window needs to be selected
Disadvantage: must write the full conductance trace for every synapse
during the simulation, then analyze each one individually.
Quantifying synaptic efficacy
Normalized synaptic efficacy
Normalized conductance average
Non-spiking dendrites
Spiking dendrites,
Uniform synapses
Location-dependence of synaptic efficacy
Normalized synaptic efficacy
Non-spiking dendrites
Spiking dendrites,
Uniform synapses
Spiking dendrites,
Weighted synapses
Analyses of model spiking output
1. Synaptic integration mode: interactions between excitation and inhibition
2. Variability of model spiking: synaptic –vs– intrinsic control of timing
Conclusions
• Many independent synapses can easily be added to a
multicompartmental model using the synchan,
timetable and spikegen objects in GENESIS.
• This method is useful for making inferences about
how in vitro results will apply to the in vivo system
and for studying single neuron input-output
functions.
• Matlab provides a convenient platform for
customizing and automating the analysis of the data.
Thanks to…
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Dieter Jaeger
Cengiz Gunay
Jesse Hanson
Chris Rowland
Lauren Job
Kelly Suter
Carson Roberts