Data from: Basolateral amygdala oscillations enable fear learning in a biophysical model
Data files
Nov 22, 2024 version files 32 GB
-
Data_Fig3.zip
9.50 GB
-
Data_Fig4.zip
59.74 MB
-
Data_Fig5.zip
8.12 GB
-
Data_Fig6.zip
14.32 GB
-
README.md
6.40 KB
Abstract
The basolateral amygdala (BLA) is a key site where fear learning takes place through synaptic plasticity. Rodent research shows prominent low theta (∼3-6 Hz), high theta (∼6-12 Hz), and gamma (>30 Hz) rhythms in the BLA local field potential recordings. However, it is not understood what role these rhythms play in supporting the plasticity. Here, we create a biophysically detailed model of the BLA circuit to show that several classes of interneurons (PV, SOM, and VIP) in the BLA can be critically involved in producing the rhythms; these rhythms promote the formation of a dedicated fear circuit shaped through spike-timing-dependent plasticity. Each class of interneurons is necessary for the plasticity. We find that the low theta rhythm is a biomarker of successful fear conditioning. The model makes use of interneurons commonly found in the cortex and, hence, may apply to a wide variety of associative learning situations.
This repository contains the simulated data used to generate the figures presented in “Basolateral amygdala oscillations enable fear learning in a biophysical model” by Anna Cattani, Don B Arnold, Michelle McCarthy, Nancy Kopell (https://doi.org/10.7554/eLife.89519.1)
File structure and Data description
Types of neurons in the network:
- ECS: pyramidal cell encoding the CS input
- PN: pyramidal cell encoding the US input
- VIP: vasoactive intestinal peptide-expressing interneuron
- SOM: somatostatin-expressing interneuron
- PV: parvalbumin-expressing interneuron
- PYRCS: auxiliary pyramidal cell receiving CS
- PYRUS: auxiliary pyramidal cell receiving US
SINGLE NEURON NETWORK: “Data_Fig3” and “Data_Fig4”
“Data_Fig3” and “Data_Fig4” contains text files named “singleneuronnet_cond1_stim3_exp...rule1..._numtrial.txt”. More specifically,
- “Singleneuronnet” specifies that the network is made by only one neuron per type
- cond1 refers to simulations performed during fear conditioning
- stim3 refers to the presence of concomitant CS and US
- exp… is followed by 0, 1, 2, 6, and 7. exp0 refers to the full network with all the interneuron subtypes, exp1: network with no SOM, exp2: no PV, 6: no VIP, exp7: only PV.
- rule1 refers to the depression-dominated plasticity rule used to generate these data
- “STDP”, “AMPA_ECS_PN”, and “LFP” contains different information related to the network dynamics (STDP), change in the conductance between ECS and PN (AMPA_ECS_PN), local field potential (LFP), as detailed below.
- numtrial goes from 0 to 39, representing 40 network realizations.
A) “STDP.txt”:
[1] time [ms]
[2] VIP membrane potential [mV]
[3] SOM membrane potential [mV]
[4] PV membrane potential [mV]
[5] ECS membrane potential [mV]
[6] PN (called F in the article) membrane potential [mV]\
[7] auxiliary pyramidal neuron representing CS [mV]
[8] auxiliary pyramidal neuron representing US [mV]
[9] potentiation ECS -> F
[10] depression ECS->F
[11] potentiation F -> VIP (not used for simulations in the article)
[12] depression F-> VIP (not used for simulations in the article)
B) “AMPA_ECS_PN.txt”:
[1] evolution in time of the ECS to F conductance
[2] evolution in time of F to VIP conductance (no actual variation is reported since plasticity from F to VIP has not been considered in the main manuscript)
C) “LFP.txt”:
[1] time
[2] sum of the AMPA currents (coming from ECS to F, F to VIP, and F to PV connections)
[3] H-current in the SOM cell
[4] P-current in the SOM cell
[5] D-current in the VIP cell
D) param contains the value of the following conductances:
[1] GABA from VIP to SOM
[2] GABA from VIP to PV
[3] GABA from SOM to ECS
[4] GABA from SOM to PN
[5] GABA from PV to PN
[6] AMPA from PN to PV
[7] GABA from PV to ECS
[8] AMPA from PN to VIP
[9] AMPA from PYRCS to ECS
[10] AMPA from PYRCS to PV
[11] AMPA from PYRUS to PN
[12] AMPA from PYRUS to VIP
HETEROGENEOUS NETWORK: “Data_Fig5” and “Data_Fig6”
“Data_Fig5” and “Data_Fig6” contains text files named “het_cond..._stim...exp0_rule1..._numtrial.txt”. More specifically,
- “het” refers to the heterogeneous network made by multiple neurons per type
- cond0 refers to simulations performed before fear conditioning, cond1 during fear conditioning, and cond2 after fear conditioning
- stim1 refers to the presence of only CS, stim3: concomitant CS and US
- exp0 refers to the full network with all the interneuron subtypes
- rule1 refers to the depression-dominated plasticity rule used to generate these data
- “STDP”, “AMPA_ECS_PN”, and “LFP” contains different information related to the network dynamics (STDP), change in the conductance between ECS and PN (AMPA_ECS_PN), local field potential (LFP), as detailed below.
- numtrial goes from 0 to 39, representing 40 network realizations.
“Data_Fig5” and “Data_Fig6” also contain three files .txt named “STDP.txt”, “AMPA_ECS_PN.txt”, and “LFP.txt”, which are structured as follows:
A) “STDP.txt”:
[1] time
[2] VIP1 membrane potential [mV]
[3] VIP2 membrane potential [mV]
[4] VIP3 membrane potential [mV]
[5] SOM1 membrane potential [mV]
[6] SOM2 membrane potential [mV]
[7] SOM3 membrane potential [mV]
[8] PV membrane potential (all three PV cells display identical activity) [mV]
[9-18] ECS cells’ membrane potential [mV]
[19-28] PN cells’ membrane potential [mV]
[29] auxiliary pyramidal neuron representing CS [mV]
[30] auxiliary pyramidal neuron representing US [mV]
[31] potentiation ECS1 -> PN1
[32] potentiation PN1 -> VIP (not used for simulations in the article)
[33] depression ECS1 -> PN1
[34] depression PN1 -> VIP (not used for simulations in the article)
B) “AMPA_ECS_PN.txt”:
[1] evolution of ECS1 to PN1 conductance over time
[2] evolution of PN1 to VIP1 conductance over time (no actual variation since plasticity from PN to VIP has not been considered in the main manuscript)
[3] evolution of PN1 to VIP2 conductance over time (no actual variation)
[4] evolution of PN1 to VIP3 conductance over time (no actual variation)
[5] evolution of PN2 to VIP1 conductance over time (no actual variation)
C) “LFP.txt”:
[1] time
[2] sum of the AMPA currents (coming from ECS to F, F to VIP cells, and F to PV connections)
[3] H-current in the SOM cell
[4] P-current in the SOM cell
[5] D-current in the VIP cell
D) “param” contains the value of the following conductances:
[1] GABA from VIPs to SOMs
[2] GABA from VIPs to PVs
[3] GABA from SOMs to ECSs
[4] GABA from SOMs to PNs
[5] GABA from PVs to PNs
[6] AMPA from PNs to PVs
[7] GABA from PVs to ECSs
[8] AMPA from PN(1) to VIP(1)
[9] AMPA from PN(1) to VIP(2)
[10] AMPA from PN(1) to VIP(3)
[11] AMPA from PN(2) to VIP(1)
[12] AMPA from PYRCS to ECS(1)
[13] AMPA from PYRCS to PVs
[14] AMPA from PYRUS to PN(1)
[15] AMPA from PYRUS to VIP
Sharing/access Information
The codes to generate the figures can be found at https://github.com/annacatt/Basolateral_amygdala_oscillations_enable_fear_learning_in_a_biophysical_model
Help is available by contacting the creator, Anna Cattani (acattani@bu.edu)