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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]
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;
from neuron import h
import nrnutils_ss as nu

class Spine():

    def __init__(self,
                 neck_mechanisms = [],
                 head_mechanisms = [],
                 parent = None,
                 connection_point = 0,
                 noiseRandObj = None,
                 highindex=None): = name
        self.parent_sec = parent[0]        
        self.parent_seg = parent[1]
        self.parent_x = parent[1].x
        # Spine morphology from Kater & Harris 1994
        # neck: 0.2 < L < 2 um, 0.04 < diam < 0.5 um, 2.512e-4 < volume < 0.3925 um3, 0.00112 < surface < 3.14 um2
        # total: 0.004 < volume < 0.6 um3, 0.1 < surface < 4 um2
        # spine: 3.7488e-3 < volume <  0.2075 um3, 0.09888 < surface < 0.86 um2
        # spine: diam = 1 um -> L = v/((diam/2)^2*3.14) = 0.2075/0.785 = 0.264 um -> surface = 0.82896 um2
        # we use here:
        # neck: L = 1 um, diam = 0.1 um
        # head: L = 0.264 um, diam = 1 um, v = 0.2075 um3
        # the head volume could be reduced
        self.neck = nu.Section(
            cm = p['CmDend'],
            Ra = p['RaAll'],
            parent = self.parent_sec,
            connection_point = self.parent_x)

        self.head = nu.Section(
            cm = p['CmDend'],
            Ra = p['RaAll'],
            parent = self.neck,

        # self.parent_sec.push()
        # for m,attr in [['na3','gbar_na3'],
        #                ['kdr','gkdrbar_kdr'],
        #                ['kad','gkabar_kad']]:
        #     if h.ismembrane(m):
        #         setattr(self.head,attr, getattr(self.parent_sec,attr))
        #         print getattr(self.head,attr)
        # h.pop_section()
        # Access head section
        # print self.head.BDNF.alpha_gAMPA, self.head.BDNF.theta_gAMPA, self.head.BDNF.sigma_gAMPA
        if hasattr(self.head, 'BDNF'):
            self.highindex = noiseRandObj.MCellRan4(highindex)
            if h.ismembrane('ca_ion'):
                setattr(self.head,'internal_nc',h.NetCon(self.head(0.5)._ref_cai, self.head.BDNF, sec = self.head))
                self.head.internal_nc.threshold = self.head.BDNF.theta_cai_BDNF
                # print 'theta_cai_BDNF', self.head.internal_nc.threshold
                self.head.internal_nc.delay = 0.1e-3
                raise Exception("BDNF mechanism requires also cad for Ca dynamics")
            if hasattr(self.head, 'AMPA'):
                if p['check']:
                    print("Setting pointer AMPA.g_factor->BDNF.gAMPA")
                h.setpointer(self.head.AMPA._ref_g_factor, 'gAMPA', self.head.BDNF)
                raise Exception("BDNF mechanism require also AMPA")

        if hasattr(self.head, 'RMECB'):
            if hasattr(self.head, 'AMPA'):
                if p['check']:
                    print("Setting pointer AMPA.U_SE_factor->RMECB.delta_U")
                h.setpointer(self.head.AMPA._ref_U_SE_factor, 'delta_U', self.head.RMECB)
                raise Exception("RM_eCB mechanism require also AMPA")

        if h.ismembrane('na_ion'):
            self.head.ena = 55

        if h.ismembrane('k_ion'):
            self.head.ek = -90

        # Access neck section
        if h.ismembrane('na_ion'):
            self.neck.ena = 55

        if h.ismembrane('k_ion'):
            self.neck.ek = -90

        # Return to the origina section
        if balance_currents:

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