Linear-Nonlinear-Synapse model for synaptic depressions and temporal frequency tuning

This is repository accompanies the paper:

Liuyuan He, Yutao He, Lei Ma, and Tiejun Huang (2022)

A theoretical model reveals specialized synaptic depressions and temporal frequency tuning in retinal parallel channels

codes

• lib: library for RAB module, LN model, and LNS models.
• Grabner.py: Experiments on (Grabner et al., 2016)
• Schroeder.py: Experiments on (Schroeder et al., 2021)

requirements

• Python3
• numpy
• matplotlib
• scipy

experiments

for (Grabner et al., 2016)

python3 Grabner.py

Experiments on (Grabner et al., 2016)

Load Optimized Models

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The performance of LN models # Figure 1

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1. Response Traces # Figure 1A
2. Peak Responses # Figure 1B
3. Tuning ranges # Figure 1D
4. Parameters in LN models # Figure 1C

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The performance of LNS models # Figure 2

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1. Response Traces # Figure 2B
2. Peak Responses # Figure 2C
3. Tuning ranges # Figure 2D

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The synaptic depression experiment on LNS models # Figure 3

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1. Parameters of LNS models # Figure 3B
2. The PPD experment on two LNS models # Figure 3C&D

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Kinetics inside LNS models # Figure 4

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1. The inner variables of the cb2 LNS model # Figure 4A
2. The depression of inner variables of the cb2 LNS model # Figure 4B
3. The inner variables of the cb2 LNS model with time constants # Figure 4C
4. The inner variables of the cb3a LNS model with time constants # Figure 4D

for (Schroeder et al., 2021)

python3 Schroeder.py

Experiments on (Schroeder et al., 2016)

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Data fitting and parameters of LNS models # Figure 5

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1. Response Traces # Figure 5D
2. Parameters # Figure 5E

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Prediction of temporal tuning # Figure 6

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1. Filtering responses # Figure 6A
2. Peak responses # Figure 6B