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Study of Artificial Neurogenesis and its Potential Impact to Form New Computational Model for The Cognition Part of Human Brain

ResearcherAhmed M.Abd El Zaher

About Cognitive PsycologyCognitive psychology : is the science of the mind, and the study of knowledge representation is a cornerstone for cognition modeling and understanding. Declarative KnowledgeThe Knowledge of Facts

Example: -We can define familiar Concepts.Describe typical events.Call up images.What Is ConceptConcepts:Help us organize the multitude of objects, events and relation in our physical & mental world.

Concept embody knowledge about object that is not perceptually apparent4Two Major Approaches For Concept Representation:-Semantic Network.-Prototype Model.What's EventsOur interaction with environment and with other people consists of sequences of events.Representation of Events:-Simple Action & Proposition-Causal Relations.-Schemas & Scripts.Procedural Knowledge:The knowledge underlying our actionsRepresented By: Production rule.Acquisition of Procedural Knowledge: (skill acquisition)Proceduralization: transform from slow declarative knowledge into fast accessed procedural knowledge.Composition: involves combining two production rule into one ( remove redundancy )Symbolic Architectures of Cognition: (Models)

A-Atkinson & Shiffrin, 1968)B-The Act* Model (from Anderson, 1983).C-The Soar Model (from Newell et al.,1989).10A-Atkinson & Shiffrin, 1968)

Sensory RegisterShort-Term StoreLong-Term StoreInput

B-The Act* Model (from Anderson, 1983).

Declarative MemoryProduction MemoryWorking MemoryPerformancesStorageMatchApplicationRetrievalEncodingOutside WorldExecution12C-The Soar Model (from Newell et al.,1989). Outside World

Production MemoryWorking MemoryMusclesExecutionDecisionMatchMotor SystemsPerceptual SystemsSensesChunking13Distributed Representation:Important Attributes:-The Parallel Propagation of Activation Through The Network -The Distributed Representation of Information across sets of weights.-The Learning of New Patterns By Changing Weights According to the Hebb and delta rules.-Mimic Memory Function ,The Same Set of Neural Net Remember Multiple Object Association.

14From Our Point of View: DrawBack of Neural Net-Pure mathematical abstract for knowledge representation-Directed to model neuron morphology rather than its actual functionality and its contribution in the thinking processPhysiological Topics (Our Research Framework) About Neurogenesis :FOR MOST OF ITS 100-YEAR HISTORY, NEUROSCIENCE has embraced a central dogma: a mature adult's brain remains a stable, unchanging, computer like machine with fixed memory and processing power. You can lose brain cells, but you certainly cannot gain new ones. How could it be otherwise?If the brain were capable of structural change, How could we remember anything? How could we maintain a constant self-identity? About Neurogenesis:Although the skin, liver, heart, kidneys, lungs and blood can all generate new cells to replace damaged ones, at least to a limited extent, until recently scientists thought that such regenerative capacity did not extend to the central nervous system, which consists of the brain and spinal cord.

Newborn Nerve Cells:Researchers first demonstrated that the central nervous systems of mammals contain some innate regenerative properties in the 1960s and 1970s, when several groups showed that the axons, or main branches, of neurons in the adult brain and spinal cord can re-grow to some extent after injury. Newborn Nerve Cells:Others subsequently revealed the birth of new neurons, a phenomenon called neurogenesis, in the brains of adult birds, nonhuman primates and humans.

"New Nerve Cells for the Adult Brain," by Gerd Kempermann and Fred H. Gage; SCIENTIFIC AMERICAN, May 1999].New Neuron Trip:NEURAL STEM CELLS are the fount of new cells in the brain. They divide periodically in two main areas: the ventricles (purple, inset), which contain cerebrospinal fluid to nourish the central nervous system, and the hippocampus (light blue, meet), a structure crucial for learning and memory. As the neural stem cells proliferate (cell pathways below),they give rise to other neural stem cells and to neural precursors that can grow up to be either neurons or support cells, which are collectively termed glial cells (astrocytes or otigodendrocytes). But these newborn neural stem cells need to move (red arrows, inset) away from their progenitors before they can differentiate. Only 50 percent, on average, migrate successfully (the others perish). In the adult brain, newborn neurons have been found In the hippocampus and in the olfactory bulbs, which process smells. Researchers hope to be able to induce the adult brain to repair itself by coaxing neural stem cells or neural precursors to divide and develop when and where they are needed. F.H.G.

Source : Brain, Repair Yourself by Fred H. GAGENeurogenesis Phases:F. Neurogenesis in the Adult Rodent Brain(A) Depictions of sagittal and coronal views ofmouse brain in areas where neurogenesis occurs.Red areas indicate the germinal zones in the adult mammalian brain: the subgranular zone (SGZ) of the dentate gyrus in the hippocampus and the subventricular zone (SVZ) of the lateral ventricles.Neurons generated in the SVZ migrate through the rostral migratory stream and are incorporated into the olfactory bulb.(BE) Neurogenesis revealed by BrdU incorporation in the olfactory bulb (B), rostral migratory stream (C), SVZ (D), and dentate gyrus (E). Inset in (C) is a sagittal view of rostral migratory stream before reaching the olfactory bulb, and inset in (E) is a high-magnification view of the area indicated by the arrow in (E). Colors indicate the following:red, BrdU; green, NeuN.(F and G) Newborn neurons in the olfactory bulband dentate gyrus labeled by retrovirus-mediatedexpression of green fluorescent protein (GFP). Insets are high-magnification views of the cells indicated by arrows. Colors indicate the following:red, NeuN; green, GFP; blue, DAPI. Image in (F)is reproduced with permission from Cold SpringHarbor Laboratory Press (Zhao, 2007).

Source : CELL Journal Review Paper.Neurogenesis Phases:The birth of new cells.The migration of newborn cells to the correct spots. The maturation of those cells into neurons.Current unknowns ( Under invistigation)The actual rule govern neo neuron is not understanding until now ,there contribution to existing old hard wired brain circuitry under intense investigation .

Assumption for neurogenesis :New neuron attached to more active & closer neurons.Its attached with initial weight w0 and the weight value proportional to the level of activation of its neighborhoods.The number of attached neuron is direct proportional to growth factor concentration.

Our old suggestions:Procedure for constructing new modelDepends on last investigation of neurogenesis : We consider the following general experimental steps:-Constructing Neural Network. (Input-Hidden-Output Layer)-During different period in Learning Epoch, Add additional Neuron to Hidden Layer depends on: a- arbitrarily random growth factor Concentration ( neuro-chemical substances ) GF, b- more active neurons. ( Represent Demanded Portion for Neurogenesis ).Our old suggestions:Experiment steps:-Repeat last steps for diff Network architecture ,diff activation function and diff learning algorithm.- Finally measure network Performance in term of learning speed and knowledge capacity.

Our old suggestions:Expected Conclusion: In Case of (Positive Result)If The added new neuron will produce increase in network learning performance or its knowledge capacity this may infer us predict that neurogenesis actually affect positively on mammal cognitive power and ,we will recommend this new neural network architecture as a tool for knowledge representation in the hand of the scientists & engineers in multiple discipline.Our old suggestions:After extensive search in previous work this Model observed:(Current Research) - Research on the functional role of adult neurogenesis in the hippocampus [1].

Research on the Learning and development and the important of prior experience [2].

And many papers intense investigate the issue, for this reason we consider modeling neurogenesis served enough by a lot of research article. [1] Radmila Manev, Hari Manev ,.2004. The meaning of mammalian adult neurogenesis and the function of newly added neuron the small world network.[2] Gerry T.M Altmann ,2001,learning and development in neural networks-the important of prior experince.Redirection of research plan for investigating other current unknown.Observed Phenomena:-Research on the functional role of adult neurogenesis in the hippocampus reveal that animal with old skilled neuron and undergoes neurogensis were suffer catastrophic interference if it adapt immature neuron to new environmental change [1].

The Strooping effect [2] describe the interference produced from competition between automatic and controlled executing tasks.

-different meaning according to the observer point of view.[1] Laurenze Wiskott , Malte J. Rasch and Gerd Kempermann ,.2004. What is the functional role of adult neurogenesis in the hippocampus.[2] Stroop , 1935.Our Investigation,Modeland Conclusion Based on The Observed Phenomena

Novel Computational Model Apoptosis/NeuroGenesis For Multi-abstraction Level Perception

ResearcherAhmed M.Abd El Zaher

Our Assumptions:-N feature Vectors Input to NN with Mean () and Distribution Function F(X). There are a constant programmed-Apoptosis/ Neurogenesis orMultiple Strooping effect Contains both rotation and Indentation Change of Meaning A of Z Pattern Reflect Multiple Abstraction Level In The Same Image and Its Associated Interference : according to level of view we have 3 facts all is true : micro we have Z pattern ,or Macro far away We recognize A Pattern , and intermediate (Integrated )Abstraction Level which recognize we have A pattern of ZZ Sub pattern.

Source : saved screen image from my manuscript MS Office file. - There are a blockage neurotransmitter that permit only required functional portion of encoding layer neuron to operate and block other neighbors ,to prevent catastrophic interference or over-learning.

- As we see Simultaneously Event the Temporally Connection Begin Cross Talked between the Correlated sub Neuron Group in the Encoding Layer.

Our Assumptions:-During Training if The Input Statistic is Large Deviated from previously trained Input victor then During training perform Apoptosis for previously Trained hidden and Connect ne unit group Neurogenesis to prevent , during testing ,Suppress all Deviated input from current Group by multiply Its connection Wight to the Output By Suppression factor. To prevent Stored Feature from Catastrophic Interference.

-According to relative view of input (observer ) the cross linked of merged feature suppression factor change which facilitate for subsequent Decision Layer to Recognize and infers higher abstractive inferences ( like we have A pattern of Zs inference)

Consider we have multiple abstractive perceptual events vector E=e1, e2 , e3, .., en consider its corresponding human attention vector is A=a 1, a 2, a 3, .., a n

Assume we Have (Potential of event vector P).assuming A=f(P) , where f(P) is a linear function. Assumption framework:(Model and Analysis)So for each event e n we have a n functionally established from the Event Potential Victor P (model the potential of external event required attention degree -mammals conscious decision - )

The sum of attention at any time instant remains constant ak= c

Source : saved screen image from my manuscript MS Office file. The Network Architecture As follow: (Consider Intended Feature In Visual System (Sensor Field of View) via systematic Neurogensis- Apoptosis operationUsed for Large Deviated Un-Correlated Input feature Victor

Source : saved screen image from my manuscript MS Office file.

Source : saved screen image from my manuscript MS Office file.

Source : saved screen image from my manuscript MS Office file. Assume the mechanism of neurogenesis/apoptosis during learning happened as follow: for fixed no of input and output layer, consider: n = no of neurons in encoding layer. Ken = size of event en.

Encoding Layer Allocation (Learning /Recall Phase):

Source : saved screen image from my manuscript MS Office file. Source : saved screen image from my manuscript MS Office file.

Source : saved screen image from my manuscript MS Office file.

Source : saved screen image from my manuscript MS Office file.

Source : saved screen image from my manuscript MS Office file. Notes 1: an vector may consider as multiple ROI (region of interest) in image processing application.

Notes 2: Level of abstraction in perception of visual pattern may view as locking on the same seen from low to high resolution. Notes:-The model treating of attention degradation. -Treat multi abstractive level perception collected by higher abstractive neurons circuits for global object identification.

-Prevent from catastrophic interference by statistically based or (event abstractive perception based) grouping of encoding layer which valuable information to the higher abstractive inference engine.

Conclusion :-The Minimum cross talk between two different functionally neuron cluster via this isolation structure in learning process observed biologically in cortex boundary between two regions and subjected to special type of neurotransmitter /inhibitor chemical unknown till know.-The construction preserve the parallel distributed nature of learning/perception as will as the serial perception of image component due to attention fixed amount of resource and the focal and movement of human visual system to full recognize external world.

-The Concept of 1 network -> multiple skills/perception implemented here this may interpret why neurogenesis integrated into old neuron circuits without catastrophic interference.Conclusion :-Can deal with multiple simultaneously ROI in Image identification perception applications.

-Perception Sensor Focus/attention Block May Replaced By Vision Ultrasound or other Multiple ROI or Depth Medical sensor.

-Consider of biomedical cases (Cancer and Normal Tissue) .

Some of its expected application in biomedical problem:-We Provide the Model Here As Theoretical Model further Experimental Setup May Be Performed To Prove Its Application Benefit.

-Other work may treat linear neurogenesis allocation neuron clusters in non-linear manner according to expected knowledge

-Study The Use of The model in image identification with multiple abstractive ROI ( Experimentally )Study the use of the model in gene sequencing, cancer detection -Performing t-Test and other Statistical Test for comp with control Normal Model Network Architecture:capacity.Future Work:Thank You All