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Increases or decreases in their activity is extremely important in biological neural systems as it is generally accepted that learning in the brain and formation of memories arise from synaptic modifications 9 The ability of biological synapses to prominent STP and LTP is one of their essential features Concern and Possible Solutions in Neuromorphic Synaptic Models The main issue about neuromorphic synaptic models with learning capability is the storage of the synaptic weights referred as power of communication between two nodes which are confined as well as limited accuracy As a result when introducing new memories it makes memory recovery impossible 10 To overcome this problem various synaptic storage implementations have been suggested below 1 A vital solution is to introduce digital memory cells Meanwhile if the processing of the synapse is to be analog additional analog to digital and vice versa is required to interaction between the analog and digital system in this scenario needed more space on the chip die and enhances complexity and power budget 2 For digital memory cells the alternative of this is a capacitive storage That provides a better solution However if the storage capacitance is connected to a transmission gate the unavoidable leakage would call for mechanisms to lessen the issue by placing a using a big capacitor but have to suffers the occupying the space and power consumption 3 Floating gate transistors provides comprehensive analog conditions long term without support of external clock approach and continuous preserve storage while the break in the power supplies 11
