Memoria is a general memory network that applies Hebbian theory which is a major theory explaining human memory formulation to enhance long-term dependencies in neural networks. Memoria stores and retrieves information called engram at multiple memory levels of working memory, short-term memory, and long-term memory, using connection weights that change according to Hebb's rule.
Memoria is an independant module which can be applied to neural network models in various ways and the experiment code of the paper is in the experiment directory.
Please refer to Memoria: Hebbian Memory Architecture for Human-Like Sequential Processing for more details about Memoria.
$ pip install memoria-pytorchYou can install memoria by pip command above.
This is a tutorial to help to understand the concept and mechanism of Memoria.
import torch
from memoria import Memoria, EngramType
torch.manual_seed(42)
# Memoria Parameters
num_reminded_stm = 4
stm_capacity = 16
ltm_search_depth = 5
initial_lifespan = 3
num_final_ltms = 4
# Data Parameters
batch_size = 2
sequence_length = 8
hidden_dim = 64- Fake random data and lifespan delta are used for simplification.
memoria = Memoria(
num_reminded_stm=num_reminded_stm,
stm_capacity=stm_capacity,
ltm_search_depth=ltm_search_depth,
initial_lifespan=initial_lifespan,
num_final_ltms=num_final_ltms,
)
data = torch.rand(batch_size, sequence_length, hidden_dim)# Add data as working memory
memoria.add_working_memory(data)# Expected values
>>> len(memoria.engrams)
16
>>> memoria.engrams.data.shape
torch.Size([2, 8, 64])
>>> memoria.engrams.lifespan
tensor([[3., 3., 3., 3., 3., 3., 3., 3.],
[3., 3., 3., 3., 3., 3., 3., 3.]])- Empty memories are reminded because there is no engrams in STM/LTM yet
reminded_memories, reminded_indices = memoria.remind()# No reminded memories because there is no STM/LTM engrams yet
>>> reminded_memories
tensor([], size=(2, 0, 64))
>>> reminded_indices
tensor([], size=(2, 0), dtype=torch.int64)- In this step, no engrams earn lifespan because there is no reminded memories
memoria.adjust_lifespan_and_memories(reminded_indices, torch.zeros_like(reminded_indices))# Decreases lifespan for all engrams & working memories have changed into shortterm memory
>>> memoria.engrams.lifespan
tensor([[2., 2., 2., 2., 2., 2., 2., 2.],
[2., 2., 2., 2., 2., 2., 2., 2.]])
>>> memoria.engrams.engrams_types
tensor([[2, 2, 2, 2, 2, 2, 2, 2],
[2, 2, 2, 2, 2, 2, 2, 2]], dtype=torch.uint8)
>>> EngramType.SHORTTERM
<EngramType.SHORTTERM: 2>- Now, there are some engrams in STM, remind and adjustment from STM will work
data2 = torch.rand(batch_size, sequence_length, hidden_dim)
memoria.add_working_memory(data2)>>> len(memoria.engrams)
32
>>> memoria.engrams.lifespan
tensor([[2., 2., 2., 2., 2., 2., 2., 2., 3., 3., 3., 3., 3., 3., 3., 3.],
[2., 2., 2., 2., 2., 2., 2., 2., 3., 3., 3., 3., 3., 3., 3., 3.]])reminded_memories, reminded_indices = memoria.remind()# Remind memories from STM
>>> reminded_memories.shape
torch.Size([2, 6, 64])
>>> reminded_indices.shape
torch.Size([2, 6])
>>> reminded_indices
tensor([[ 0, 6, 4, 3, 2, -1],
[ 0, 7, 6, 5, 4, -1]])# Increase lifespan of all the reminded engrams by 5
memoria.adjust_lifespan_and_memories(reminded_indices, torch.full_like(reminded_indices, 5))# Reminded engrams got lifespan by 5, other engrams have got older
>>> memoria.engrams.lifespan
>>> memoria.engrams.lifespan
tensor([[6., 1., 6., 6., 6., 1., 6., 1., 2., 2., 2., 2., 2., 2., 2., 2.],
[6., 1., 1., 1., 6., 6., 6., 6., 2., 2., 2., 2., 2., 2., 2., 2.]])- Repeat 10 times to see the dynamics of LTM
# This is default process to utilize Memoria
for _ in range(10):
data = torch.rand(batch_size, sequence_length, hidden_dim)
memoria.add_working_memory(data)
reminded_memories, reminded_indices = memoria.remind()
lifespan_delta = torch.randint_like(reminded_indices, 0, 6).float()
memoria.adjust_lifespan_and_memories(reminded_indices, lifespan_delta)# After 10 iteration, some engrams have changed into longterm memory and got large lifespan
# Engram type zero means those engrams are deleted
>>> len(memoria.engrams)
72
>>> memoria.engrams.engrams_types
tensor([[3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],
[0, 0, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]], dtype=torch.uint8)
>>> EngramType.LONGTERM
<EngramType.LONGTERM: 3>
>>> EngramType.NULL
<EngramType.NULL: 0>
>>> memoria.engrams.lifespan
tensor([[ 9., 1., 8., 2., 16., 5., 13., 7., 7., 3., 3., 4., 3., 3.,
4., 2., 2., 1., 1., 1., 1., 1., 1., 1., 2., 6., 1., 1.,
2., 2., 2., 2., 2., 2., 2., 2.],
[-1., -1., 3., 2., 19., 21., 11., 6., 14., 1., 5., 1., 5., 1.,
5., 1., 1., 8., 2., 1., 1., 1., 2., 1., 1., 1., 1., 1.,
2., 2., 2., 2., 2., 2., 2., 2.]])@misc{park2023memoria,
title = {Memoria: Hebbian Memory Architecture for Human-Like Sequential Processing},
author = {Sangjun Park and JinYeong Bak},
year = {2023},
eprint = {2310.03052},
archiveprefix = {arXiv},
primaryclass = {cs.LG}
}
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