BDNF Function in Adult Synaptic Plasticity_the Synaptic Consolidation Hypotesis
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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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
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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
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(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(D. Jaeger, unpublished)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
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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
![Page 6: Simulating in vivo -like synaptic input patterns in multicompartmental models](https://reader036.fdocuments.us/reader036/viewer/2022062411/56814080550346895dac05a2/html5/thumbnails/6.jpg)
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
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(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?
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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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(Destexhe A, Pare D (1999). J Neurophysiol 81: 1531-47.)
Modeled M-current (KCNQ) block with and without simulated background synaptic input
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Spatial and temporal summation are reduced whenthe conductance level is high
(Destexhe A, Pare D (1999). J Neurophysiol 81: 1531-47)
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(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.)
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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
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
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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+
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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
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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 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
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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 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
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//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…
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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 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
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Movie: 20 Hz excitation, 2.5 Hz inhibition
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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.
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Non-spiking dendritesSpiking dendrites,Uniform synapses
Nor
mal
ized
syn
aptic
eff
icac
yQuantifying synaptic efficacy
Nor
mal
ized
con
duct
ance
ave
rage
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Non-spiking dendrites Spiking dendrites,Weighted synapses
Spiking dendrites,Uniform synapses
Nor
mal
ized
syn
aptic
eff
icac
y
Location-dependence of synaptic efficacy
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Analyses of model spiking output1. 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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