# Memory Model for Morphological Semantics of Visual Stimuli Using Sparse Distributed Representation
## 2 Related Researches
### Computing Models for Memory Mechanism  
It is generally known that neocortical region of human brain is responsible for memories and high-level knowledge processing. This paper considers HTM as a simulation model for neuron activation mechanism in neocortical region of human brain. Even though a neuron model in HTM is a simplified version of biological neuron and it is similar to the general artificial neuron model, we can consider it is more realistic than the neuron model of the existing artificial neural networks. Figure 3 shows neuron models of the existing artificial neural networks and HTM with a biological neuron model [13]. Figure 3a illustrates a biological neuron model, in which green lines mean proximal dendrites and blue lines mean distal dendrites. In addition, axons in a biological model have both feed-forward and lateral connections. On the other hand, the existing artificial neuron model shown in Figure 3b has only one kind of dendrite and axon with lateral connection. Figure 1: [PERSON] and [PERSON]’s multi-store memory model which consists of sensory memory, short-term memory, and long-term memory [11]. Figure 2: [PERSON]’s working memory model [12] with [PERSON] and [PERSON]’s multi-store memory model [11]. For HTM neuron model in Figure 3c, it has both proximal dendrite and distal dendrite like a biological neuron and axons with feed-forward connections as well as lateral connections. Visual information is processed through various regions in the neocortex of a biological brain as shown in Figure 4[14]. Visual stimulus from the external environment is transferred through optic nerve into V1 region in the occipital lobe. V1 region processes visual features such as edges and lines from visual stimulus. Next, V2 region processes shape information using the edges and lines from V1 region. After that, shape information is used to find objects in V4 region and conceptual information such as faces in IT region. Thus, we can think low level visual feature is transformed into higher level information through abstraction and conceptual process. It is well known that every region of neocortex in human brain communicates information through the internal information delivery channel as shown in Figure 5a. Neocortex can be considered as thin sheet consisting multi-layered neurons. Each different region of thin neocortex sheet processes different information. Figure 5a illustrates 3 different visual cortex regions such as V1, V2, and V4. Visual sensory information fed into V1 is processed through many different regions with bi-directional connections on neocortex. Figure 5b shows one piece of neocortex sheet illustrating 6 layers of neurons and columnar structure. Each layer has its own number from 1 to 6. Layer 1 is the most outer layer and closer to brain surface. On the other hand, layer 6 is the most internal layer and closer to substantia alba. Figure 4: Visual information processing structure model in brain [14]. Figure 3: Comparison of (**a**) biological neuron, (**b**) artificial neuron model for the existing neural networks, and (**c**) a neuron cell model in HTM [13]. Structure and operation of neurons in neocortex can be modeled using sparse distributed representation. It is because only a small part of neurons among huge number of neurons in neocortex is activated in a specific time. Sparse representation draws a great deal of attention because of its information embedding capacity. It can be used to find the most compact size of representation for the original data with underlying discriminative features. We can consider sparse graph research as a kind of sparse representation researches. Sparse graph can represent similarity weights between features. It can be used to improve the classification accuracy by the optimal feature selection [16; 17; 18]. In addition, sparse coding techniques are used for data compression, noise reduction, and interpolation, and so on. Sparse coding is developed to represent the original data using an overcomplete base vector [19; 20; 21].