  Making neural networks remember over the long term has been a longstanding
issue. Although several external memory techniques have been introduced, most
focus on retaining recent information in the short term. Regardless of its
importance, information tends to be fatefully forgotten over time. We present
Memoria, a memory system for artificial neural networks, drawing inspiration
from humans and applying various neuroscientific and psychological theories.
The experimental results prove the effectiveness of Memoria in the diverse
tasks of sorting, language modeling, and classification, surpassing
conventional techniques. Engram analysis reveals that Memoria exhibits the
primacy, recency, and temporal contiguity effects which are characteristics of
human memory.
