Simulating in vivo-like synaptic input patterns in multicompartmental models

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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

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Numerical estimates of in vivo input levels
100 mm
100 mm

• GP neuron
• surface area 17,700 mm2
• number of synapses (ex/in) 1,200 / 6,800
• number of inputs / s 12,000 / 6,800

• Ca3 pyramidal neuron
• surface area 38,800 mm2
• number of synapses (ex/in) 17,000 / 2,000
• number of inputs / s 170,000 / 20,000

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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
(GinEin) (GexEex) / (Gin Gex) Isyn (Gin
Gex) (Vm - Esyn)
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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(D. Jaeger, unpublished)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.)
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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

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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 cant be replicated in
  vitro
• Input correlation / synchrony

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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?
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Effects of blocking SK channels in DCN neurons in
vitro
(D. Jaeger, unpublished)
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SK channel block in DCN neurons with in vivo-like
background conductance levels
(D. Jaeger, unpublished)
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Modeled M-current (KCNQ) block with and without
simulated background synaptic input
(Destexhe A, Pare D (1999). J Neurophysiol 81
1531-47.)
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Spatial and temporal summation are reduced
when the conductance level is high
(Destexhe A, Pare D (1999). J Neurophysiol 81
1531-47)
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Input synchronization affects rate and precision
(Fellous J-M et al (2003). Neuroscience 122
811-29.)
(Gauck Jaeger, 2000.)
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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

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Steps involved in setting up the simulations

• Cell morphology reconstruct a filled neuron,
  obtain a morphology file from a colleague or the
  web, or make a simplified morphology model.
• Passive parameters Rm, Cm, Ri
• Active conductances GENESIS tabchannel objects
• Synapse templates (AMPA, GABA, etc.)
• ? gmax, trise, tfall, Erev
• Compartments list of those receiving input
• For every independent synapse (in a loop)
• 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

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Element tree structure for the simulation
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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
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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 /libraryend//Push
library element, make conductance elements, pop
library pushe /library make_AMPA_syn make_G_Na
make_G_K pope
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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 compartments 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
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3. For all compartments receiving input
//set up spikegen create a unique
spikegen object for this compartments 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 compartments
AMPA element, type SPIKE // Next
loop iteration or END
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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 whats 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

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Movie 20 Hz excitation, 2.5 Hz inhibition
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Quantifying synaptic efficacy

• 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 synapses 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.

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Quantifying synaptic efficacy
Spiking dendrites, Uniform synapses
Non-spiking dendrites
Normalized conductance average
Normalized synaptic efficacy
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Location-dependence of synaptic efficacy
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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
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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.

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Thanks to

• Dieter Jaeger
• Cengiz Gunay
• Jesse Hanson
• Chris Rowland
• Lauren Job
• Kelly Suter
• Carson Roberts

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