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

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Accession:244412
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.
Reference:
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]
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;
Gene(s):
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 unipv.it]; Migliore, Michele [Michele.Migliore at Yale.edu];
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;
from neuron import h
import random as rnd
import numpy as np

class stim_protocol():
    
    def __init__(self,
                 cell,
                 p,
                 rank = 0):

        # Add parameters to protocol
        self.p = p
        self.seed = 123881
        rnd.seed(self.seed)
        # Start protocol
        self.stimulators = {}

        # Initial time lag
        start_next = 0

        # Stabilization of membrane potential
        start_next += 0

        # pre induction EPSP Test 
        self.test_pre = h.NetStim()
        self.test_pre.start = start_next # start_next # ms
        self.test_pre.interval = 1e3/self.p['test_freq'] # ms -> 20 sec
        self.test_pre.number = int(self.p['time_on_initialization'] / self.test_pre.interval) # 200
        self.test_pre.noise = 0
        if p['check']:
            print('Begin sim',start_next)
        
        self.nc_test_pre = []
        self.nc_test_pre_nmda = []
        for spine in cell.spines:
            self.nc_test_pre.append(h.NetCon(self.test_pre, spine.head.AMPA,0,0,1))
            self.nc_test_pre_nmda.append(h.NetCon(self.test_pre, spine.head.NMDA,0,0,1))
        self.stimulators['test_pre_spike_times'] = h.Vector()
        self.nc_test_pre[-1].record(self.stimulators['test_pre_spike_times'])

        start_next += self.p['time_on_initialization']
        if p['check']:
            print('End PRE',start_next)
        

        # Induction protocol
        self.stim = h.NetStim()
        start_next += self.p['time_to_begin_induction']
        self.stim.start = start_next # ms
        self.p['time_start_induction_stimuli'] = self.stim.start
        self.stim.number = self.p['nstim'] # 200
        self.stim.interval = 1e3/self.p['induction_freq'] # ms  0.5 Hz = 2 s
        self.stim.noise = 0
        if p['check']:
            print('Begin induction',start_next)
        

        self.nc_stim = []
        self.nc_stim_nmda = []
        synaptic_delays = np.linspace(0,p['sp_delay_env'],len(cell.spines))
        for spine,delay in zip(cell.spines,synaptic_delays):
            spine.delay = delay #rnd.uniform(0,p['sp_delay_env'])
            self.nc_stim.append(h.NetCon(self.stim, spine.head.AMPA,0,spine.delay,1))
            self.nc_stim_nmda.append(h.NetCon(self.stim, spine.head.NMDA,0,spine.delay,1))
        self.stimulators['induction_spikes'] = h.Vector()
        self.nc_stim[-1].record(self.stimulators['induction_spikes'])

        # during induction EPSP Test 
        self.test_during = h.NetStim()
        self.test_during.start = start_next + 1e3/self.p['induction_freq']/2 # ms
        if p['check']:
            print("DURING",self.test_during.start, 1e3/self.p['induction_freq']/2)
        self.test_during.interval = 1e3/self.p['test_freq'] # ms -> 20 sec
        self.test_during.number = int(self.stim.number*self.stim.interval / self.test_during.interval)
        self.test_during.noise = 0

        self.nc_test_during = []
        self.nc_test_during_nmda = []
        for spine in cell.spines:
            self.nc_test_during.append(h.NetCon(self.test_during, spine.head.AMPA,0,0,1))
            self.nc_test_during_nmda.append(h.NetCon(self.test_during, spine.head.NMDA,0,0,1))
        self.stimulators['test_during_spike_times'] = h.Vector()
        self.nc_test_during[-1].record(self.stimulators['test_during_spike_times'])

        # BPAP self.stimulation    
        IC_dep = self.stimulators['IC_dep'] = []
        IC_hyp = self.stimulators['IC_hyp'] = []
        IC_delays = self.stimulators['IC_delays'] = []
        for s in range(self.p['nstim']):
            IC_dep.append([])
            IC_hyp.append([])
            IC_delays.append(start_next + self.stim.interval * s - p['IC_delay_to_spike'])
            for ap in range(4):
                IC_dep[-1].append(h.IClamp(0.5, sec = cell.cell.soma))
                IC_hyp[-1].append(h.IClamp(0.5, sec = cell.cell.soma))
                IC_dep[-1][ap].amp = p['BPAP_stimulus_amplitude'] # nA
                IC_dep[-1][ap].delay =  IC_delays[-1] + ap*5# ms
                IC_dep[-1][ap].dur = p['BPAP_dep_stimulus_duration'] # ms

                IC_hyp[-1][ap].amp = -0.02 # nA
                IC_hyp[-1][ap].delay = IC_dep[-1][ap].delay + IC_dep[-1][ap].dur # ms
                IC_hyp[-1][ap].dur = p['BPAP_hyp_stimulus_duration'] # ms
            # self.stimulators['IC_dep'].append(h.IClamp(0.5, sec = cell.cell.branch_base))
            # self.stimulators['IC_hyp'].append(h.IClamp(0.5, sec = cell.cell.branch_base))
            # self.stimulators['IC_dep'][-1].amp = self.p['BPAP_stimulus_ampitude'] # nA
            # self.stimulators['IC_dep'][-1].delay = start_next + self.stim.interval * s - self.p['IC_delay_to_spike'] # ms
            # self.stimulators['IC_delays'].append(self.stimulators['IC_dep'][-1].delay)
            # self.stimulators['IC_dep'][-1].dur = self.p['BPAP_dep_stimulus_duration'] # ms

            # self.stimulators['IC_hyp'][-1].amp = -0.02 # nA
            # self.stimulators['IC_hyp'][-1].delay = self.stimulators['IC_dep'][-1].delay + self.stimulators['IC_dep'][-1].dur # ms
            # self.stimulators['IC_hyp'][-1].dur = self.p['BPAP_hyp_stimulus_duration'] # ms


        self.time_of_induction = self.p['nstim'] * self.stim.interval
        start_next += self.time_of_induction
        self.time_end_induction = start_next
        if p['check']:
            print('End induction',start_next)

        # Post induction EPSP test during expression
        self.test_post = h.NetStim()
        start_next += self.p['time_after_induction']
        self.test_post.start = start_next  # ms
        self.test_post.number = 1e4 
        self.test_post.interval = 1e3/self.p['test_freq'] # ms = 0.05 Hz = 20 sec
        self.test_post.noise = 0

        self.nc_test_post = []
        self.nc_test_post_nmda = []
        for spine in cell.spines:
            self.nc_test_post.append(h.NetCon(self.test_post, spine.head.AMPA,0,0,1))
            self.nc_test_post_nmda.append(h.NetCon(self.test_post, spine.head.NMDA,0,0,1))
        start_next += self.p['time_of_expression'] #ms
        self.stimulators['test_post_spike_times'] = h.Vector()
        self.nc_test_post[-1].record(self.stimulators['test_post_spike_times'])

        # cell.spines[0].head.plot('cai')
        # dend.plot('v')
        # # h('load_file("Tests/Test_ampa.ses")')
        # # h('load_file("Tests/Test_ampa_30min.ses")')

        # Remve the induction self.stimulus
        if self.p['activate_LTP_protocol']:
            self.stim.number = self.p['nstim']
        else:
            self.stim.number = 0

        ##### Initialization ########
        h.v_init = self.p['Vrest'] # mV
        self.p['tstop'] = start_next
        h.init()
        if p['check']:
            print('Setup finished on cpu %g tstop %g'%(rank,h.tstop))



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