STDP and BDNF in CA1 spines (Solinas et al. 2019)

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Storing memory traces in the brain is essential for learning and memory formation. Memory traces are created by joint electrical activity in neurons that are interconnected by synapses and allow transferring electrical activity from a sending (presynaptic) to a receiving (postsynaptic) neuron. During learning, neurons that are co-active can tune synapses to become more effective. This process is called synaptic plasticity or long-term potentiation (LTP). Timing-dependent LTP (t-LTP) is a physiologically relevant type of synaptic plasticity that results from repeated sequential firing of action potentials (APs) in pre- and postsynaptic neurons. T-LTP is observed during learning in vivo and is a cellular correlate of memory formation. T-LTP can be elicited by different rhythms of synaptic activity that recruit distinct synaptic growth processes underlying t-LTP. The protein brain-derived neurotrophic factor (BDNF) is released at synapses and mediates synaptic growth in response to specific rhythms of t-LTP stimulation, while other rhythms mediate BDNF-independent t-LTP. Here, we developed a realistic computational model that accounts for our previously published experimental results of BDNF-independent 1:1 t-LTP (pairing of 1 presynaptic with 1 postsynaptic AP) and BDNF-dependent 1:4 t-LTP (pairing of 1 presynaptic with 4 postsynaptic APs). The model explains the magnitude and time course of both t-LTP forms and allows predicting t-LTP properties that result from altered BDNF turnover. Since BDNF levels are decreased in demented patients, understanding the function of BDNF in memory processes is of utmost importance to counteract Alzheimer’s disease.
1 . Solinas SMG, Edelmann E, Leßmann V, Migliore M (2019) A kinetic model for Brain-Derived Neurotrophic Factor mediated spike timing-dependent LTP. PLoS Comput Biol 15:e1006975 [PubMed]
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Model Information (Click on a link to find other models with that property)
Model Type: Neuron or other electrically excitable cell; Synapse; Dendrite;
Brain Region(s)/Organism: Hippocampus;
Cell Type(s): Hippocampus CA1 pyramidal GLU cell;
Channel(s): I Na,t; I_KD; I K; I h; I A; I Calcium;
Gap Junctions:
Receptor(s): AMPA; NMDA;
Transmitter(s): Glutamate;
Simulation Environment: NEURON;
Model Concept(s): Facilitation; Long-term Synaptic Plasticity; Short-term Synaptic Plasticity; STDP;
Implementer(s): Solinas, Sergio [solinas at]; Migliore, Michele [Michele.Migliore at];
Search NeuronDB for information about:  Hippocampus CA1 pyramidal GLU cell; AMPA; NMDA; I Na,t; I A; I K; I h; I Calcium; I_KD; Glutamate;
import random as rnd
from neuron import h
from neuron import gui
import copy as cp
import numpy as np

class Panels(object):
    def __init__(self,branch,protocol,plots):
        self.spines = branch.spines
        self.cell = branch.cell
        self.plots = plots
        self.tau_rm = self.spines[0].head.RMECB.tau_RM
        self.alpha_RMru = self.spines[0].head.RMECB.alpha_RMru
        self.blk_RMBLK = protocol.p['Block_RMBLK']
        self.protocol = protocol
        self.dur = protocol.stimulators['IC_dep'][0][0].dur
        self.dep = protocol.stimulators['IC_dep']
        self.hyp = protocol.stimulators['IC_hyp']
        self.stim = protocol.stim
        self.start_base = protocol.p['time_start_induction_stimuli']
        self.delta_t = 5 #-(self.stim.start - self.start_base)
        self.neck_diam = self.spines[0].neck.diam
        self.AMPA_Pmax = self.spines[0].head.AMPA.Pmax
        self.AMPA_Prel = self.spines[0].head.AMPA.U_SE_init
        self.AMPA_NMDA_ratio = self.spines[0].head.NMDA.Pmax/self.spines[0].head.AMPA.Pmax
        self.NMDA_Pmax = self.spines[0].head.NMDA.Pmax
        self.NMDA_mg = self.spines[0]
        self.NMDA_mgbk = self.spines[0].head.NMDA.mgb_k
        self.KMULT = h.KMULT
        self.KMULTP = h.KMULTP
        self.nBPAP = len(protocol.stimulators['IC_dep'][0])
        self.nstim = 25 #len(protocol.stimulators['IC_dep'])
        self.sp_delay_env = protocol.p['sp_delay_env']
        self.old_sp_delay_env = cp.deepcopy(protocol.p['sp_delay_env'])
        self.gbar_cat = protocol.p['gcatbar']
        self.clamp_dur = 120
        self.clamp_V = -20

    def build(self): = h.HBox()
        h.xbutton("View = Plot", self.set_view) 
        h.xbutton("Set to run short sim", self.run_short) 
        h.xbutton("Set to run long sim", self.run_long) 
        h.xbutton("LTP11", self.set_LTP11) 
        h.xbutton("LTP12", self.set_LTP12) 
        h.xbutton("LTP14", self.set_LTP14)
        h.xbutton("SEClamp del",self.del_seclamp)
        h.xstatebutton("Block RMBLK",(self,'blk_RMBLK'),self.set_spines)
        h.xvalue("Pulse duration (ms)", (self, "dur"), 1, self.set_pulse)
        h.xvalue("Delta_t = t_post - t_pre (ms)", (self, "delta_t"), 1, self.set_stim_start)
        h.xvalue("Spine neck diam (um)", (self, "neck_diam"), 1, self.set_spines)
        h.xvalue("AMPA permeability (cm/s)", (self, "AMPA_Pmax"), 1, self.set_spines)
        h.xvalue("Glutammate Prel", (self, "AMPA_Prel"), 1, self.set_spines)
        h.xvalue("AMPA NMDA ratio", (self, "AMPA_NMDA_ratio"), 1, self.set_spines)
        h.xvalue("NMDA permeability (cm/s)", (self, "NMDA_Pmax"), 1, self.set_spines)
        h.xvalue("NMDA Mg out (mM)", (self, "NMDA_mg"), 1, self.set_spines)
        h.xvalue("NMDA Mg Block slope (/mV)", (self, "NMDA_mgbk"), 1, self.set_spines)
        h.xvalue("BPAB number", (self, "nBPAP"), 1, self.set_pulse)
        h.xvalue("Number of stimuli", (self, "nstim"), 1, self.set_pulse)        
        h.xvalue("KMULT", (self, "KMULT"), 1, self.set_cell)
        h.xvalue("KMULTP", (self, "KMULTP"), 1, self.set_cell)
        h.xvalue("CaT gbar", (self, "gbar_cat"), 1, self.set_spines)
        h.xvalue("Spike envelop delay", (self, "sp_delay_env"), 1, self.set_pulse)
        h.xvalue("RM decay tau (ms)", (self, "tau_rm"), 1, self.set_spines)
        h.xvalue("alpha_RMru (1)", (self, "alpha_RMru"), 1, self.set_spines)
        h.xvalue("Clamp dur", (self, "clamp_dur"), 1, self.set_seclamp)
        h.xvalue("Clamp V", (self, "clamp_V"), 1, self.set_seclamp)
    def map(self):"Stimulus control", 0, 378, 329.28, 642.24)

    def set_seclamp(self):
        self.clamp = h.SEClamp(0.5,sec=self.cell.soma)
        self.clamp.dur1 = self.clamp_dur
        self.clamp.amp1 = self.clamp_V
        self.clamp_graph = h.Graph()
        self.clamp_graph.addvar('I_seclamp',self.clamp._ref_i, 1,1,sec=self.cell.soma)

    def del_seclamp(self):
        del self.clamp
    def set_LTP11(self):
        h.stoprun = 1
        self.nBPAP = 1
        self.nstim = 70
    def set_LTP12(self):
        h.stoprun = 1
        self.nBPAP = 2
        self.nstim = 50
    def set_LTP14(self):
        h.stoprun = 1
        self.nBPAP = 4
        self.nstim = 25
    def set_spines(self):
        for spine in self.spines:
            spine.head.RMECB.tau_RM = self.tau_rm
            spine.head.RMECB.alpha_RMru = self.alpha_RMru
            spine.neck.diam = self.neck_diam
            spine.head.AMPA.Pmax = self.AMPA_Pmax
            spine.head.AMPA.U_SE_init = self.AMPA_Prel
            if self.AMPA_NMDA_ratio > 0:
                spine.head.NMDA.Pmax = self.AMPA_Pmax * self.AMPA_NMDA_ratio
                self.NMDA_Pmax = spine.head.NMDA.Pmax
                spine.head.NMDA.Pmax = self.NMDA_Pmax

            spine.head.gcatbar_cat = self.gbar_cat
   = self.NMDA_mg
            spine.head.NMDA.mgb_k = self.NMDA_mgbk

            if self.blk_RMBLK:
                self.alpha_cai_RMBLK_tmp = spine.head.RMECB.alpha_cai_RMBLK
                spine.head.RMECB.alpha_cai_RMBLK = 0
                spine.head.RMECB.alpha_cai_RMBLK = self.alpha_cai_RMBLK_tmp
    def set_stim_start(self):
        self.stim.start = self.start_base - self.delta_t
        print(("Delta t =",self.stim.start))
    def set_pulse(self):
        # Set curr inj duration
        for dc,hc in zip(self.dep, self.hyp):
            for dc2 in dc:
                dc2.dur = self.dur
        # Set nunmber of curr injections for each induction stim (nBPAPs)
        for dc,hc in zip(self.dep, self.hyp):
            for dc2 in dc[int(self.nBPAP):len(dc)]:
                dc2.dur = 0
            # for hc2 in hc:
            #     hc2.dur = 0
            #     hc2.delay = dc.delay + dc.dur # ms

        # Set nunmber of trains of curr injections, i.e. n induction stimuli
        for nst,(dc,hc) in enumerate(zip(self.dep, self.hyp)):
            if nst > self.nstim:
                for dc2 in dc:
                    dc2.dur = 0
        self.stim.number = self.nstim
        synaptic_delays = np.linspace(0,self.sp_delay_env,len(self.spines))
        for delay,nc,nc_nmda in zip(synaptic_delays,self.protocol.nc_stim,self.protocol.nc_stim_nmda):
            nc.delay = delay
            nc_nmda.delay = delay
        print(("new dur =", self.dep[0][0].dur))

    def set_cell(self):
        command = 'forall '
        for sec in self.cell.apical_sl:
            for seg in sec:
                xdist = h.distance(seg.x)
                if xdist > 100:
                    prev_gkabar_kad = seg.gkabar_kad
                    seg.gkabar_kad = self.KMULT*(1+xdist/100)
                    # print((seg.gkabar_kad -  prev_gkabar_kad) / prev_gkabar_kad)
                    prev_gkabar_kap = seg.gkabar_kap
                    seg.gkabar_kap = self.KMULTP*(1+xdist/100)

    def set_view(self):
        for plot in list(self.plots.values()):
            plot.exec_menu("View = plot")

    def erase(self):
        for plot in list(self.plots.values()):

    def run_short(self):
        h.stoprun = 1
        h.tstop = 250

    def run_long(self):
        h.stoprun = 1
        h.tstop = 1800e3 # runs for 30 min = 1000 ms x 60 s x 30 min = 1.800.000 ms