# Memory Model for Morphological Semantics of Visual Stimuli Using Sparse Distributed Representation
## 1 Introduction  
Recent deep neural networks and learning algorithms show outstanding performance improvements. These results arise more affirmative expectations of future technological development [1, 2, 3]. On the other hand, these promising technologies also have quite a lot of limitations as well. Optimizing structure of deep neural networks and various hyper parameters as well as computing infra for highly complicated training algorithms are well known problems. In addition, the recent deep neural algorithms require huge volume of training data set with tag information and further almost algorithms support offline learning using the preconstructed training data set. Therefore, transition of problem domain or revision of training data set require new learning process from the beginning.  
In general, training schemes for deep neural networks are different from biological learning method. As we all well understand, biological objects selectively receive and remember external stimuli input from sensory organs. We can consider biological objects always perform online learning in everyday life using these memory process.  
As an alternative to overcome limitation of deep neural networks, tries to imitate biological learning are encouraged more and more. This paper considers the most representative 2 of these tries. The one is SPAUN (Semantic Pointer Architecture Unified Model) [4] and the other is HTM (Hierarchical Temporal Memory) [5,6].  
SPAUN is a brain simulation model which consists of 2.5 million spiking neurons. Based on this model, we can simulate various brain operation such as recognition, memory, learning, and reasoning.  
In addition, HTM employs modeling memory mechanism in neocortical region of biological brain system. HTM is a memory model with hierarchical structure, and it usually bases memory-prediction model in brain. Further, HTM is a cognitive model which can analyze and process time series variations of sequential input data. HTM can be applied to detect abnormal state of server or application and abnormal using of system. Basically, HTM converts input data to SDR (Sparse Distributed Representation) and processes SDR for analyzing and prediction. According to HTM model, SDR can be considered as the most representative data format processed by neocortical region of brain.  
This paper proposes a memory and recall model for morphological semantics within visual stimuli using SDR of HTM model. Neocortical region in biological brain takes in charge of high level of information processing including abstraction and conceptualization for stimuli from the external environment. In addition, it supports memory and recall functions when required. In these whole processes, we can model operational mechanism of neurons on neocortical region using SDR operation.  
The morphological semantic memory model proposed in this paper consists of two parts such as memory process for input stimuli and recall process for previous memory. First, memory process can be considered as a process to convert input visual stimuli to SDR type data. We can assume SDR as a neuron activation pattern in neocortical region in the abstraction and conceptualization processes for input visual stimuli. For the next recall process, we consider a detection process to find the most similar SDR pattern in the memory from the SDR pattern of the given visual stimulus.  
This paper consists of 5 sections. In Section 2, we introduce backgrounds of this work and related research results. Section 3 explains structures of the proposed memory and recall model, and Section 4 presents experimental results for MNIST [7] and Fashion-MNIST [8] dataset. Finally, Section 5 discusses conclusions and future works.