A STDP-based Encoding Algorithm for Associative and Composite Data
Hong-Gyu Yoon, Pilwon Kim

TL;DR
This paper introduces a practical memory model based on STDP that encodes and retrieves high-dimensional associative and composite data, demonstrating effective auto-associative and semantic memory tasks.
Contribution
It presents a novel STDP-based encoding scheme capable of handling multiple composite data types in a continuous, associative memory framework.
Findings
Successful retrieval of streamed images with cues in auto-associative tasks.
Words can recall multiple or single sentences based on grammatical relations.
Model effectively encodes and retrieves semantic memories from sentences.
Abstract
Spike-timing-dependent plasticity(STDP) is a biological process of synaptic modification caused by the difference of firing order and timing between neurons. One of the neurodynamical roles of STDP is to form a macroscopic geometrical structure in the neuronal state space in response to a periodic input. This work proposes a practical memory model based on STDP that can store and retrieve high-dimensional associative data. The model combines STDP dynamics with an encoding scheme for distributed representations and can handle multiple composite data in a continuous manner. In the auto-associative memory task where a group of images is continuously streamed to the model, the images are successfully retrieved from an oscillating neural state whenever a proper cue is given. In the second task that deals with semantic memories embedded from sentences, the results show that words can recall…
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Taxonomy
TopicsAdvanced Memory and Neural Computing · Neural dynamics and brain function · Neuroscience and Neuropharmacology Research
