cerebral-lstm 1.1.1

A PyTorch implementation of Cerebral LSTM: A Better Alternative for Single- and Multi-Stacked LSTM Cell-Based RNNs.

Cerebral LSTM - implementation in Pytorch

This repository provides python package for pytorch implementation of Cerebral LSTM, presented in the paper "Cerebral LSTM: A Better Alternative for Single- and Multi-Stacked LSTM Cell-Based RNNs". Research paper is published in SN Computer Science Springer Nature Journal.

Paper Title: Cerebral LSTM: A Better Alternative for Single- and Multi-Stacked LSTM Cell-Based RNNs

Author: Ravin Kumar

Publication: 14th March 2020

Published Paper: click here

Doi: DOI Link of Paper

Other Sources:

• Research Gate, Research Gate - Preprint
• Osf.io
• SSRN
• Internet Archive, Internet Archive - Preprint

Github Repositories:

• Github Repository (Python Package- Pytorch Implementation): Python Package
• Github Repository (Sentiment Analysis LSTM vs Cerebral LSTM): ML Experiments

Cite Paper as:

Kumar, R. Cerebral LSTM: A Better Alternative for Single- and Multi-Stacked LSTM Cell-Based RNNs. SN COMPUT. SCI. 1, 85 (2020). https://doi.org/10.1007/s42979-020-0101-1

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Cerebral LSTM Architecture:

Uf(t) = σ(Wuf ⋅ [h(t − 1), x(t)] + buf)
Ui(t) = σ(Wui ⋅ [h(t − 1), x(t)] + bui)
UCtmp(t) = tanh (Wuc ⋅ [h(t − 1), x(t)] + buc)
UC(t) = Uf(t) ∗ UC(t − 1) + Ui(t) ∗ UCtmp(t)
Uo(t) = σ(Wuo ⋅ [h(t − 1), x(t)] + buo)
Lf(t) = σ(Wlf ⋅ [h(t − 1), x(t)] + blf)
Li(t) = σ(Wli ⋅ [h(t − 1), x(t)] + bli)
LCtmp(t) = tanh (Wlc ⋅ [h(t − 1), x(t)] + blc)
LC(t) = Lf(t) ∗ LC(t − 1) + Li(t) ∗ LCtmp(t)
Lo(t) = σ(Wlo ⋅ [h(t − 1), x(t)] + blo)
h(t) = Uo(t) ∗ tanh(UC(t)) + Lo(t) ∗ tanh(LC(t))

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Python Package: Pytorch Implementation

📥 Installation

pip install cerebral_lstm

Or,

pip install git+https://github.com/mr-ravin/cerebral_lstm.git

🚀 Usage

import torch
from cerebral_lstm import CerebralLSTM

# Create a Cerebral LSTM model, i.e. RNN  model with Cerebral LSTM cell unit
model = CerebralLSTM(input_size=64, hidden_size=128, num_layers=2, use_xavier=True, dropout=0.5) # Default: use_xavier=True

# Input: (seq_len, batch_size, input_size)
x = torch.randn(10, 32, 64)  # Example input
output, hidden = model(x)
print(output.shape)  # (10, 32, 128)

How to access only Cerebral LSTM cell unit ?

from cerebral_lstm import CerebralLSTMCell

# Get only a single Cerebral LSTM cell unit
lstm_cell_unit = CerebralLSTMCell(input_size=64, hidden_size=128, use_xavier=True) # Default: use_xavier=True

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Impact of Initialisation of Trainable Parameters in Cerebral LSTM

The initial value of trainable parameters of upper and lower parts have impact onnumber of epochs required to train Cerebral LSTM cell. Ideally, upper and lowerparts should not have same initial values for their trainable parameters.

Identical initial trainable parameter values for upper and lower parts ❌

Initial Symmetry: Upper and lower parts of the Cerebral LSTM process inputs identically, leading to similar cell states Uc(t) and Lc(t).

Redundancy: Initial representations of upper and lower parts are redundant, potentially under-utilizing the model’s capacity.

Gradients: Early training updates are similar, but divergence may occur over time,leading to different feature extraction.

Different initial trainable parameter values for upper and lower parts ✔️

Diverse Learning: Upper and lower parts of Cerebral LSTM immediately capture different aspects of the data, enhancing representation diversity.

Specialization: Faster convergence and better utilization of the dual-path architec-ture, as each path can specialize in different features.

Performance: Improved performance due to richer, non-redundant representationsfrom the start.

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Experimentation Repository in https://github.com/mr-ravin/cerebral-rnn-experimental-results

• Comparative Study - Cerebral LSTM vs LSTM:

  Pytorch Implementation of Cerebral LSTM is available in Cerebral_LSTM/Cerebral_LSTM_Implementation_in_Pytorch.ipynb file.

• Comparative Study Cerebral LSTM vs Stacked-LSTM vs LSTM (Logs only)

  For the training loss graphs present in the research paper, see the below structure:

  |
  |-data/                             # This directory contains dataset used for comparison.
  |
  |-loss_values/                      # This directory contains record of training loss for each model to perform comparative analysis.
        |
        |- 2stack_lstm.txt 
        |- proposed_model.txt
        |- single_lstm.txt
  

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Conclusion

Our proposed recurrent cell ‘Cerebral LSTM’ showed the ability to better understand data and has easily outperformed both single LSTM and two-stacked LSTM based recurrent neural networks. Many variants of Cerebral LSTM can be designed using available varieties of LSTM cells such as peephole LSTM. Further research work can be conducted on designing Cerebral LSTM based stacked recurrent neural networks for designing deep learning architectures for understanding time-series data. Other recurrent cells including gated recurrent units can also be analyzed after modifying itsinternal connections similar to our cerebral structure.

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Copyright License

Copyright (c) 2025 Ravin Kumar
Website: https://mr-ravin.github.io

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation 
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