1. 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

2. 100  m GP neuron surface area:17,700  m 2 number of synapses (ex/in):1,200 / 6,800 number of inputs / s12,000 / 6,800 Ca3 pyramidal neuron surface area:38,800  m 2 number of synapses (ex/in):17,000 / 2,000 number of inputs / s170,000 / 20,000 Numerical estimates of in vivo input levels

3. 5,000 AMPA and 500 GABA A Synapses at 10 Hz E in = -70 mV E ex = 0 mV I syn = G in * (V m - E in ) + G ex * (V m - E ex ) E syn = [(G in *E in ) + (G ex *E ex )] / (G in + G ex ) I syn = (G in + G ex ) * (V m - E syn ) Thousands of synapses add up to a lot of conductance! I syn = (300 nS) * (60-50mV) = 3 nA

4. (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 High conductance state of neurons in vivo (D. Jaeger, unpublished) Neocortical pyramidal neurons Striatal medium spiny neuron

5. 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

6. 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

7. (Edgerton JR, Reinhart PH (2003). J Physiol 548: 53-69.) Small conductance K (Ca 2+ ) channels (SK channels) regulate the firing rate of Purkinje neurons in vitro… …but is this also true in vivo?

8. Effects of blocking SK channels in DCN neurons in vitro (D. Jaeger, unpublished)

9. SK channel block in DCN neurons with in vivo-like background conductance levels (D. Jaeger, unpublished)

10. (Destexhe A, Pare D (1999). J Neurophysiol 81: 1531-47.) Modeled M-current (KCNQ) block with and without simulated background synaptic input

11. Spatial and temporal summation are reduced when the conductance level is high (Destexhe A, Pare D (1999). J Neurophysiol 81: 1531-47)

12. (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. )

13. 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

14. 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, τ rise, τ fall, E rev 5.Compartments: list of those receiving input 6. For every independent synapse (in a loop): 1.Copy the synaptic conductance from a template library to the compt 2.Create a timetable object to determine when the synapse activates 3.Create a spikegen object to communicate with the synapse

15. Element tree structure for the simulation

16. 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

17. 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

18. 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

19. //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…

20. 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

21. Movie: 20 Hz excitation, 2.5 Hz inhibition

22. 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.

23. Non-spiking dendrites Spiking dendrites, Uniform synapses Normalized synaptic efficacy Quantifying synaptic efficacy Normalized conductance average

24. Non-spiking dendritesSpiking dendrites, Weighted synapses Spiking dendrites, Uniform synapses Normalized synaptic efficacy Location-dependence of synaptic efficacy

25. 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

26. 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.

27. Thanks to… Dieter Jaeger Cengiz Gunay Jesse Hanson Chris Rowland Lauren Job Kelly Suter Carson Roberts