INTRODUCTION

1. From/To LTM Symbol neurons “A” “N” LTM (“ARRAY”) “R” “Y” P LTM WPN PN STM E x c i t a t i o n l i n k Win 10 I n h i b i t i o n l i n k 5 SN Store 5 R Information Short-term Memory Long-term Memory A R Y Retrieve Link strength 3 10 9 Link strength 2 4 Link strength 1 4 Storage neurons Fig. 3 LTM cell 9 8 3 LTM LTM LTM Playback 3 P Level h neurons WTA P 8 7 STM 2 2 LTM LTM LTM LTM P Level h-1 … P 7 6 R STM 1 W / E 1 Sensor input Fig. 4 Hierarchical LTM 6 A R Y N Read pointer Write/erase pointer Fig. 6 STM architecture W / E LTM (“ARRAY”) From/to STM Layer 6 next level Signal flow … PN 2/3 Lateral association Layer 2 Feedback from higher level 4 5 Lateral inhibition 6 Layer 6 From/to STM From/to STM From/to STM Input activation “A” “Y” “R” Fig. 5 LTM cell with minicolumns Hierarchical spatio-temporal memory for machine learning based on laminar minicolumn structure Janusz A. Starzyk, Yinyin Liu Ohio University, Athens, OH LONG-TERM MEMORY SHORT-TERM MEMORY INTRODUCTION • LTM cell: • One long-term memory (LTM) cell stores one particular sequence whose length determines the number of required PNs (Fig.3). Cells can be combined into hierarchy (Fig. 4). • Symbol neurons (SN) excite primary neurons (PN) through Win. • (1) • PNs are interconnected to induce the temporal association and model dynamics. (2) • LTM cell overall output (3) • LTM cell learning: • Through competition, a winning LTM cell with maximum output signal strength stores the sequence by adjusting the weights. • LTM cell recalling: • Signal strength on the LTM output neuron represents the match between the sequence stored in this LTM cell and the input sequence. • STM cell: • Universal playback machine (Fig. 6) • Size is limited, like in human STM • Storage neurons for writing • Playback neurons for reading • Write/erase pointers & read pointers • Pointers in a closed loop to reused storage • Writing to STM cell: • A signal from level 6 neuron of active minicolumn activates a column in the STM • Storage neurons in the STM cell fire only when get two activations (from inputs & write pointer) • Reading from STM cell: • Read pointer disinhibits playback neurons • Playback neurons fire when activated from storage neurons & the read pointer is not active • Spatio-temporal memories are fundamental to self-organization and learning in bio-inspired systems. • Short term memory (STM) and long term memory (LTM): two major types of memories in neurobiological research of human brain. • They occupy different regions of the human brain, have different structural organization. • They interact with each other. • Input information go through the STM so that it can be stored in the LTM. • Information from LTM is retrieved to STM where it is updated and new associations are created (Fig.1). Fig. 1 Interaction between LTM and STM • The layered uniform structure of identical processing units, postulated by Mountcastle as a minicolumn organization [1][3], supports the biological intelligence building in human neocortex. • Neurons on different layers of minicolumns are proposed to have specific function in the interaction between STM and LTM. • When retrieving information from LTM to STM, particular layer of neurons receives stimulation from LTM. • When storing information from STM to LTM, stimulations from STM activate the minicolumns corresponding to the elements of a sequence. • The activation from STM or LTM is differentiated from the real environment input by different level of the signal strength. • LTM based on minicolumns: • Sequence is from the real input: • Minicolumns representing symbols “A” “R” “Y” are found through competition. • Signal flow (Fig. 5): • input  layer 6/4 of winning columns  • layer 2 of winning columns  PNs  LTM cell output • Strongly stimulates the PNs in LTM cell • Strong activations of layer 2 neurons of winning minicolumns help their layer 6 neurons win in local competition  PNs are connected with layer 6 using Hebbian learning. • The output of LTM cell enters the layer 6 neuron on the higher LTM level so that “ARRAY” can be combined with other possible sequences to build complex sequence memory. • Sequence is from STM: • Signal flow (Fig. 5): • input layer 6 of winning columns  • PNs  LTM cell output • STM stimulation will not flow up the minicolumn and overlap with the real sensory input • Slightly stimulates the PNs in LTM cell. • By comparing the level of stimulation, LTM is able to differentiate the recalled information from the real sensory input. MINICOLUMN STRUCTURE CONCLUSIONS • In this work, laminar minicolumn structure with multiple layers of neurons, proposed and studied in visual cortex by Grossberg [4] (Fig.2), is used to implement the fundamental learning mechanism of spatio-temporal memory. It has several characteristics: • Lateral inhibition among layer 4 neurons • selective circuit between 6/4 layers  perceptual grouping local winners’ domination • Feedback from layer 2 to layer 6 • Folded feedback 2 6 4 • Feedback from higher-level layer 6  solve ambiguity input selection • Feedback will not propagate forward  network stability • In this work, the laminar minicolumn is used in building the proposed structure of STM and LTM. STM is built as a playback machine which stores and recalls a certain sequence without making any associations. The sequential LTM built in a minicolumn can store and recall the sequence by associating symbols and it is able to differentiate the real environment input from the recalled information. • The proposed memory models have efficient and stable operation, are biologically plausible and have a number of desired properties for building self-organizing, hierarchical hardware structures. BIBLIOGRAPHY [1].Edward G. Jones, Microcolumns in the Cerebral Cortex, Proc. of National Academy of Science of United States of America, vol. 97(10), 2000, pp. 5019-5021 [2].Mountcastle, V. B., Response Properties of Neurons of Cat’s Somatic Sensory Cortex to Peripheral Stimuli, J. Neurophysiol, vol. 20, 1957, pp. 374-407 [3].S. Grossberg, How does the cerebral cortex work? Learning, attention, and grouping by the laminar circuits of visual cortex, Technical report CAS/CNS-97-023, 1998. Fig.2 Laminar Minicolumn