Predictive_coding

Implementation of several predictive coding models

Gagnepain 2012

Gagnepain, P., Henson, R. N., & Davis, M. H. (2012). Temporal predictive codes for spoken words in auditory cortex. Current Biology, 22(7), 615-621.

Method

Go to Gagnepain2012 and run 'ipython'
```
cd Gagnepain2012
ipython
```
Import model
```
from Gagnepain2012 import Model
```
Generate model
```
new_Model = Model(
    lexicon_File= <path>,
    additional_Lexicon_File= <path>
    )
```
- Parameters
  - lexicon_File
    - The lexicon that the model uses by default
    - 'ELP_groupData.csv' is an example
  - additional_Lexicon_File
    - Lexicon assuming further learning
    - 'Novel_Lexicon.csv' is an example
Test
```
new_Model.Test(phoneme_String= <str>)
```
- Result example

SRN

We made a jupyter notebook example to use SRN model. Please check Train.ipynb and Test.ipynb

Method

Go to SRN and run 'ipython'
```
cd SRN
ipython
```

Import model

from SRN import Model, Sigmoid, Softmax, List_Test

Generate model
```
new_Model = Model(
    hidden_Unit= <int>,
    output_Function= <Sigmoid or Softmax>,
    lexicon_File= <path>,
    additional_Lexicon_File= <path>,
    weight_File= <path>,
    use_Frequency= <bool>
    )
```
- Parameters
  - hidden_Unit
    - The size of hidden units
  - output_Function
    - Determine output function
    - Softmax or Sigmoid
  - lexicon_File
    - The lexicon that the model uses by default
    - ELP_groupData.csv is an example
  - additional_Lexicon_File
    - Lexicon assuming further learning
    - Novel_Lexicon_1.csv and Novel_Lexicon_2.csv are examples.
  - weight_File
    - If you want load a pre-trained weight file, set the weight path
    - If not, set None
  - use_Frequency
    - If you want to use frequency information of lexicon, set True
    - Default is False
Train basic lexicon
```
new_Model.Train(
    learning_Rate= <float>,
    max_Epoch= <int>,
    epoch_Batch_Size= <int>,
    mode='Normal',
    test_Pronunciation = <str>
    )
```
- Parameters
  - learning_Rate
    - The learning rate while training.
  - max_Epoch
    - Determine the maximum training epoch.
  - epoch_Batch_Size
    - Determine the batch size of training.
    - After doing batch training, the weight will be saved.
  - mode
    - In basic lexicon training, this parameter is fixed 'Normal'
  - test_Pronunciation
    - Determine one phoneme string will be tested While training.
Train additional lexicon
```
new_Model.Train(
    learning_Rate= <float>,
    max_Epoch= <int>,
    epoch_Batch_Size= <int>,
    mode='Addition',
    test_Pronunciation = <str>,
    initial_Epoch= <int>,
    tag= <str>,
    )
```
- Parameters
  - learning_Rate
    - The learning rate while training.
    - Using lower value than basic lexicon training's is recommended.
  - max_Epoch
    - Determine the maximum training epoch.
  - epoch_Batch_Size
    - Determine the batch size of training.
    - After doing batch training, the weight will be saved.
  - mode
    - In basic lexicon training, this parameter is fixed 'Addition'
  - test_Pronunciation
    - Determine a phoneme string will be tested While training.
  - initial_Epoch
    - Set number the initial epoch.
    - This value does not affect the model's performance.
    - Using the last epoch of Pre training is recommended to manage easily.

Single phoneme string test

Load both of two pre and post addition models.

pre_Model = Model(
    hidden_Unit= <int>,
    output_Function= <Sigmoid or Softmax>,
    lexicon_File= <path>,
    additional_Lexicon_File= <path>,
    weight_File= <path>
    )    

post_Model = Model(
    hidden_Unit= <int>,
    output_Function= <Sigmoid or Softmax>,
    lexicon_File= <path>,
    additional_Lexicon_File= <path>,
    weight_File= <path>
    )

weight_File is located in './Results/<Use_Frequency>/Weight'

Test plotting
```
pre_Model.Test_Plot(pronunciation= <str>, file_Suffix=<str>)
post_Model.Test_Plot(pronunciation= <str>, file_Suffix=<str>)
```
- Parameters
  - test_Pronunciation
    - Determine a phoneme string will be tested.
  - file_Suffix
    - Determine the suffix of exported file name

Result example

Phase test

Load both of two pre and post addition models.

pre_Model = Model(
    hidden_Unit= <int>,
    output_Function= <Sigmoid or Softmax>,
    lexicon_File= <path>,
    additional_Lexicon_File= <path>,
    weight_File= <path>,
    use_Frequency= <bool>
    )    

post_Novel1_Day1_Model = Model(
    hidden_Unit= <int>,
    output_Function= <Sigmoid or Softmax>,
    lexicon_File= <path>,
    additional_Lexicon_File= <path>,
    weight_File= <path>,
    use_Frequency= <bool>
    )

post_Novel1_Day2_Model = Model(
    hidden_Unit= <int>,
    output_Function= <Sigmoid or Softmax>,
    lexicon_File= <path>,
    additional_Lexicon_File= <path>,
    weight_File= <path>,
    use_Frequency= <bool>
    )

post_Novel2_Day1_Model = Model(
    hidden_Unit= <int>,
    output_Function= <Sigmoid or Softmax>,
    lexicon_File= <path>,
    additional_Lexicon_File= <path>,
    weight_File= <path>,
    use_Frequency= <bool>
    )

post_Novel2_Day2_Model = Model(
    hidden_Unit= <int>,
    output_Function= <Sigmoid or Softmax>,
    lexicon_File= <path>,
    additional_Lexicon_File= <path>,
    weight_File= <path>,
    use_Frequency= <bool>
    )

weight_File is located in './Results/<Use_Frequency>/Weight'

Type following command

Phase_Test(
    pre_Model= pre_Model,
    post_Novel1_Day1_Model= post_Novel2_Day2_Model,
    post_Novel1_Day2_Model= post_Novel2_Day2_Model,
    post_Novel2_Day1_Model= post_Novel2_Day2_Model,
    post_Novel2_Day2_Model= post_Novel2_Day2_Model,
    tag= <str>,
    export_Path= <path>
    )

Result file is <export_Path>/Result_Data<tag>.txt.

TRACE

Trace simulations were conducted in C (available at http://magnuson.psy.uconn.edu/wp-content/uploads/sites/1140/2015/01/ctrace.zip). Code used for simulations is forthcoming.

christianbrodbeck / predictive_coding

Predictive_coding

Gagnepain 2012

Method

SRN

Method

TRACE

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