simulating in vivo -like synaptic input patterns in multicompartmental models
DESCRIPTION
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. Numerical estimates of in vivo input levels. 100 m m. - PowerPoint PPT PresentationTRANSCRIPT
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
100 m
100 m
GP neuron• surface area: 17,700 m2
• number of synapses (ex/in): 1,200 / 6,800
• number of inputs / s 12,000 / 6,800
Ca3 pyramidal neuron• surface area: 38,800 m2
• number of synapses (ex/in): 17,000 / 2,000
• number of inputs / s 170,000 / 20,000
Numerical estimates of in vivo input levels
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)
Thousands of synapses add up to a lot of conductance!
Isyn = (300 nS) * (60-50mV) = 3 nA
(Pare D, Shink E, Gaudreau H, Destexhe A,
Lang EJ (1998). J Neurophysiol 79: 1460-70.)
High conductance state of neurons in vivo
(D. Jaeger, unpublished)
Neocortical pyramidal neurons
Striatal medium spiny neuron
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
(Edgerton JR, Reinhart PH (2003). J Physiol 548: 53-69.)
Small conductance K(Ca2+) channels (SK channels) regulate the firing rate of Purkinje neurons in vitro…
…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(D. Jaeger, unpublished)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(D. Jaeger, unpublished)
(Destexhe A, Pare D (1999). J Neurophysiol 81: 1531-47.)
Modeled M-current (KCNQ) block with and without simulated background synaptic input
Spatial and temporal summation are reduced whenthe conductance level is high
(Destexhe A, Pare D (1999). J Neurophysiol 81: 1531-47)
(Fellous J-M et al (2003). Neuroscience 122: 811-29.)
200 msec
100 independent inputs
10 independent inputs
Input synchronization affects rate and precision
(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, Ri3. Active conductances: GENESIS tabchannel objects4. Synapse templates (AMPA, GABA, etc.):
gmax, τrise, τfall, Erev
5. Compartments: list of those receiving input6. For every independent synapse (in a loop):
1. Copy the synaptic conductance from a template library to the compt2. Create a timetable object to determine when the synapse activates3. Create a spikegen object to communicate with the synapse
Element tree structure for the simulation
Root
Cellpath Inputs
Soma
G_Na+
G_K+
AMPA
Dendrite
Dendrite
Library
AMPA synapse
G_Na+
G_K+
AMPA synapse
timetable
AMPA synapse
Soma
timetable
spikegen
spikegen
G_Na+
G_K+
1. Create synaptic conductances using synchan objects
//GENESIS script to define AMPA-type conductancefunction make_AMPA_syn
// make AMPA-type synapseif (!({exists AMPA}))
create synchan AMPAend// assign specific synapse propertiessetfield 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 filesinclude Syns.ginclude Chans.g
//Check if library already existsif (!{exists /library}) create neutral /library disable /libraryend
//Push library element, make conductance elements, pop librarypushe /library
make_AMPA_synmake_G_Namake_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 intervals2 = gamma distribution of intervals3 = regular intervals4 = 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
//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
3. For all compartments receiving input…
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.
Non-spiking dendritesSpiking dendrites,Uniform synapses
Nor
mal
ized
syn
aptic
eff
icac
yQuantifying synaptic efficacy
Nor
mal
ized
con
duct
ance
ave
rage
Non-spiking dendrites Spiking dendrites,Weighted synapses
Spiking dendrites,Uniform synapses
Nor
mal
ized
syn
aptic
eff
icac
y
Location-dependence of synaptic efficacy
Analyses of model spiking output1. 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…
• Dieter Jaeger• Cengiz Gunay• Jesse Hanson• Chris Rowland• Lauren Job• Kelly Suter• Carson Roberts