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;
# import ipdb
from neuron import h, gui
import numpy as np
from glob import glob as listdir
import os
from branch_setup import *
from protocol import *
import h5py as h5
from GUI import Panels


CVOde = h.cvode
CVOde.active(1)

p = {}
exec(compile(open('parameters.py').read(), 'parameters.py', 'exec'),p)

# Change initialization time
p['time_on_initialization'] = 100
p['time_to_begin_induction'] = 20 # ms

branch = Spiny_branch(p)

protocol = stim_protocol(branch,p)

h.tstop = 2e4


# Load gui
h.nrncontrolmenu() # Run menu
# Color management
# 0 white
# 1 black
# 2 red
# 3 blue
# 4 green
# 5 orange
# 6 brown
# 7 violet
# 8 yellow
# 9 gray
colors = list(range(1,9))
restricted_colors = list(range(2,9))
# Brushes
brush = list(range(1,20)) # 0 thinnest, 1-4 other thicknesses,
                    # higher values cycle through these line thicknesses with different brush patterns.


plots = {}
plots['v'] = branch.plot_soma('v', label='soma',show = 0, color=restricted_colors[0],line=2, position=[0.8, 0.9])
plots['v'].size(110,200,-80,30) 
plots['v'].view(110, -80, 90, 120, 411, 52, 634.56, 407.68)
for s_i,s in enumerate(branch.spines[1:12]):
    plots['v'].addvar('%s[%i]'%('spine_head',s_i),'v(0.5)',
                      restricted_colors[(s_i)%7],1,
                      sec=s.head)
for s_i,s in enumerate(branch.spines[12:]):
    plots['v'].addvar('%s[%i]'%('spine_head',s_i),'v(0.5)',
                      9,1,
                      sec=s.head)
# plot soma Vm
plots['v'].addvar('branch[38](0.1)',
                  'v(0.1)',4,1,
                  sec=branch.cell.branch_base)
plots['v'].flush()

plots['cai'] = branch.spines[9].head.plot('cai', show=0, color=restricted_colors[0])
# Plot thresholds
time_stamps = h.Vector([110,200])
# Plot theta1
theta1 = h.Vector([p['spine_point_processes'][0]['parameters']['theta_cai_RM'], p['spine_point_processes'][0]['parameters']['theta_cai_RM']])
theta1.plot(plots['cai'], time_stamps, 1, 2)
# Plot theta2
theta2 = h.Vector([p['spine_point_processes'][2]['parameters']['theta_cai_BDNF'], p['spine_point_processes'][2]['parameters']['theta_cai_BDNF']])
theta2.plot(plots['cai'], time_stamps, 1, 7)
# Plot theta3
theta3 = h.Vector([p['spine_point_processes'][0]['parameters']['theta_cai_RMBLK'], p['spine_point_processes'][0]['parameters']['theta_cai_RMBLK']])
theta3.plot(plots['cai'], time_stamps, 2, 7)

plots['cai'].size(110,200,-0.01,0.19)
plots['cai'].view(110, -0.01, 90, 0.2, 1085, 52, 559.68, 407.68)
for s_i,s in enumerate(branch.spines[1:12]):
    plots['cai'].addvar('cai '+s.name,'cai(0.5)',restricted_colors[s_i%7],1,sec = s.head)
for s_i,s in enumerate(branch.spines[12:]):
    plots['cai'].addvar('cai '+s.name,'cai(0.5)',9,1,sec = s.head)

plots['cai'].flush()

########### Plot mBDNF of all spines in one graph #######
# plots['BDNF'] = branch.spines[0].head.plot(['BDNF','mBDNF'],
#                             type_mec='pp',
#                             label = 'mBDNF_'+s.name,
#                             color=restricted_colors[0],
#                             line=2,
#                             show=0)
# for s_i,s in enumerate(branch.spines[1:]):
#     s.head.plot(['BDNF','mBDNF'],
#                 type_mec='pp',
#                 label = 'mBDNF',
#                 color=restricted_colors[s_i%7],
#                 line=2,
#                 show=0,
#                 graph=plots['BDNF'])
# plots['BDNF'].size(0,110,0,1)
# plots['BDNF'].view(0, 0, 110, 1, 350, 486, 345.6, 563.2)

########### Plot mintracell_signaling of all spines in one graph ########
# plots['intracell_signaling'] = branch.spines[0].head.plot(['BDNF','intracell_signaling'],
#                             type_mec='pp',
#                             label = 'intracell_signaling_'+s.name,
#                             color=restricted_colors[0],
#                             line=2,
#                             show=0)
# for s_i,s in enumerate(branch.spines[1:]):
#     s.head.plot(['BDNF','intracell_signaling'],
#                 type_mec='pp',
#                 label = 'intracell_signaling',
#                 color=restricted_colors[s_i%7],
#                 line=2,
#                 show=0,
#                 graph=plots['intracell_signaling'])
# plots['intracell_signaling'].size(0,110,0,1)
# plots['intracell_signaling'].view(0, 0, 110, 1, 350, 486, 345.6, 563.2)
# # Plot thresholds
# long_time_stamps = h.Vector([110,h.tstop])
# # Plot theta1
# theta_gAMPA = h.Vector([p['spine_point_processes'][2]['parameters']['theta_gAMPA'], p['spine_point_processes'][2]['parameters']['theta_gAMPA']])
# theta_gAMPA.plot(plots['intracell_signaling'], long_time_stamps, 2, 2)

########### Plot g_factor of all spines in one graph ######
plots['g_factor'] = branch.spines[0].head.plot(['AMPA','g_factor'],
                                               type_mec='pp',
                                               label = 'gAMPA_' + branch.spines[0].name,
                                               color=restricted_colors[0],
                                               line=2,
                                               show=0)
for s_i,s in enumerate(branch.spines[1:12]):
    s.head.plot(['AMPA','g_factor'],
                 type_mec='pp',
                 label = 'gAMPA_'+s.name,
                 color=restricted_colors[(s_i+1)%7],
                 line=2,
                 show=0,
                 graph=plots['g_factor'])
for s_i,s in enumerate(branch.spines[12:18]):
    s.head.plot(['AMPA','g_factor'],
                 type_mec='pp',
                 label = 'gAMPA_'+s.name,
                 color=9,
                 line=2,
                 show=0,
                 graph=plots['g_factor'])
plots['g_factor'].size(110,200,-80,30)
plots['g_factor'].view(110, -80, 90, 120, 1085, 530, 559.68, 407.68)
plots['g_factor'].flush()

########### Plot post_intra of all spines in one graph #########
# plots['post_intra'] = branch.spines[0].head.plot(['RMECB','post_intra'],
#                                                  type_mec='pp',
#                                                  label = 'post_intra_'+s.name,
#                                                  color=restricted_colors[0],
#                                                  line=2,
#                                                  show=0)
# for s_i,s in enumerate(branch.spines[1:]):
#     s.head.plot(['RMECB','post_intra'],
#                 type_mec='pp',
#                 label = 'post_intra',
#                 color=restricted_colors[s_i%7],
#                 line=2,
#                 show=0,
#                 graph=plots['post_intra'])
# plots['post_intra'].size(0,110,0,1)
# plots['post_intra'].view(0, 0, 110, 1, 350, 486, 345.6, 563.2)
# # Plot thresholds
# long_time_stamps = h.Vector([110,h.tstop])
# # Plot theta1
# theta_RMru = h.Vector([p['spine_point_processes'][0]['parameters']['theta_RMru'], p['spine_point_processes'][0]['parameters']['theta_RMru']])
# theta_RMru.plot(plots['post_intra'], long_time_stamps, 2, 2)

########### Plot U_SE_factor of all spines in one graph ##################
plots['U_SE_factor'] = branch.spines[0].head.plot(['AMPA','U_SE_factor'],
                                                  type_mec='pp',
                                                  label = 'AMPA_prel_' + branch.spines[0].name,
                                                  color=restricted_colors[0],
                                                  line=2,
                                                  show=0)
for s_i,s in enumerate(branch.spines[1:12]):
    s.head.plot(['AMPA','U_SE_factor'],
                type_mec='pp',
                label = 'AMPA_prel_'+s.name,
                color=restricted_colors[(s_i+1)%7],
                line=2,
                show=0,
                graph=plots['U_SE_factor'])
for s_i,s in enumerate(branch.spines[12:18]):
    s.head.plot(['AMPA','U_SE_factor'],
                type_mec='pp',
                label = 'AMPA_prel_'+s.name,
                color=9,
                line=2,
                show=0,
                graph=plots['U_SE_factor'])
plots['U_SE_factor'].size(110,200,-0.01,0.19)
plots['U_SE_factor'].view(110, -0.01, 90, 0.2, 409, 531, 634.56, 407.68)
plots['U_SE_factor'].flush()

########### Run GUI ############
# if p['override_tstop'] is not None:
#     h.tstop = p['override_tstop']
# else:
#     h.tstop = protocol.p['tstop']#p['time_on_initialization']+p['time_to_begin_induction'] + 1000

panel = Panels(branch,protocol,plots)
panel.set_stim_start()
panel.set_pulse()



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