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Plan Submitted By : Rahul Jaitly

Plan Submitted By : Rahul Jaitly. Current State – As from last semester. Modeled a single layer of neurons each for the Cortex , Striatum and SNr. Decrease in number of neuron

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Plan Submitted By : Rahul Jaitly

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  1. Plan Submitted By : Rahul Jaitly

  2. Current State – As from last semester • Modeled a single layer of neurons each for the Cortex , Striatum and SNr. • Decrease in number of neuron from one layer to the another • Full forward and lateral connectivity • Learning Rule: Multi Hebbian • Reinforcement from SNc • Results not accurately achieved

  3. Steps to follow… • Introduce Nonlinearity • Introduce Sparse connectivity - Segregated channels Vs Intermediate structure

  4. Cont… • Constrain weights to either positive or negative while they adjust through Hebbian Learning. • Introduce the Thalamus layer Transfer function : Sigmoid Number of neurons > In SNr layer

  5. Integration with Rob Model • Complete the loop by adding the Pre Frontal cortex and Test manager developed in the Rob’s model. • Instead of Test manager ( Targets ) the input comes from the cortex layer • Signal coming from Pre Frontal cortex is like Bias at Striatum • High Dimensional signal to be created from Targets • WCST task assigned to the RDDR model

  6. Observations in past models… • Massive Stepwise Funneling Architecture • Lateral Inhibitory connections devoid of functional significance : Active only during the learning phase. • Absence of Intra nuclear correlation in Striatum and SNr firing • Extraction becomes discriminative , performing better for Reward related inputs • Dimension reduction result of Statistical properties of Input and Behavioral significance. • Using loops Recurrent processing takes place which can be used for planning tasks e.g. Frontal lobe task ( WCST )

  7. Behavior that may be expected… • Reinforcement from SNc leads to dimensionally reducing high dimension input from cortex. • Striatum Codes a set of reward - driven actions relevant to task at hand • Disinhibition selects the appropriate winner among that set of reduced actions. • Striatum Lateral connections through competitive Hebbian learning allow each Striatum neuron to encode unique aspect of the input space. • SNc reinforcement signal may not have role in selecting an action. • Action Selection may be achieved by having Bias through Learned behavior ( Here occurring in Pre Frontal Cortex)

  8. Key Questions • How does Information Integration ( Reduction + Saliency coding ) takes place at Striatum? • What are the Internal states of the model ? • How to simulate High Dimensional input to the Model at the Striatum layer? • Increase in the number of neurons from the SNr to Thalamus to Frontal cortex - Anatomical evidence ? • How to Explain contradictions between the RDDR and the Action selection models: Weak lateral connections Correlation

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