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Principles Underlying the Construction of Brain- Based Devices Jeff Krichmar The Neurosciences Institute San Diego, California, USA test behavior in real.

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Presentation on theme: "Principles Underlying the Construction of Brain- Based Devices Jeff Krichmar The Neurosciences Institute San Diego, California, USA test behavior in real."— Presentation transcript:

1 Principles Underlying the Construction of Brain- Based Devices Jeff Krichmar The Neurosciences Institute San Diego, California, USA test behavior in real world compare with empirical data develop theory create simulation

2 Construction of an Intelligent Machine Following the Brain Based Model Design should be constrained by these principles: Active sensing and autonomous movement in the environment. Organize the signals from the environment into categories without a priori knowledge or instruction. Incorporate a simulated brain with detailed neural dynamics and neuroanatomy. Engage in a behavioral task and adaptation of behavior when an important environmental event occurs. Allow comparisons with experimental data acquired from animal systems.

3 Active Sensing and Autonomous Movement in the Environment Darwin VII-VIII Darwin IX-X present Darwin IV-VI BrainWorks present

4 Organize the Signals from the Environment into Categories without A Priori Knowledge or Instruction Fabre-Thorpe, Phil. Trans. R. Soc. Lond. B (2003) 358, 1215–1223 Seth et al, Cerebral Cortex, November 2004, V 14 N 11

5 Incorporate A Simulated Brain With Detailed Neural Dynamics And Neuroanatomy

6 voltage independent inhibitory plastic value dependent voltage dependent Incorporate A Simulated Brain With Detailed Neural Dynamics And Neuroanatomy Cortex ECin ECout ECin FB ECout FB DG DG FB CA3 CA3 FB CA3 FF CA1 CA1 FB CA1 FF S M HDG V1 Color Camera V1 Width V2/4 Color V2/4 Width ODOMETRY HD IT PrATNM HDG HIPPOCAMPUS SR+ IR Platform IR Wall R- MOTOR BF

7 Engage in a Behavioral Task And Adapt Behavior When An Important Environmental Event Occurs

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9 Allow Comparisons with Experimental Data Acquired from Animal Systems

10 EC out EC in DGCA3 CA1

11 Incorporate A Simulated Brain With Detailed Neural Dynamics And Neuroanatomy reflex response = error signal predictive input Preflex reflex

12 Incorporate A Simulated Brain With Detailed Neural Dynamics And Neuroanatomy inhibitory climbing fiber error signal excitatory Camera IR Turn IR Velocity Motor Velocity Motor Turn Pre-Cerebellar Nuclei Motion Area (MT) Purkinje Cells Turn Deep Cerebellar Nuclei Turn Inferior Olive Turn Purkinje Cells Velocity Deep Cerebellar Nuclei Velocity Inferior Olive Velocity LTD LTP Reflex Error signal Error signal Reflex Preflex

13 Engage in a Behavioral Task and Adapt Behavior when an Important Environmental Event Occurs Un-Trained Trained

14 Allow Comparisons with Experimental Data Acquired from Animal Systems Weight Matrices (initially, all weights were equal) Pre-Cerebellar-Nuclei Purkinje Cells for velocity White = maximum Black = minimum More widespread LTD for sharper courses results in lower velocity LTD Pre-Cerebellar Nuclei Purkinje Cells Velocity LTD

15 Development of Intelligent Machines that follow Neurobiological and Cognitive Principles in their Construction

16 Jason Fleischer Jeff McKinstry Don Hutson Botond Szatmary Anil Seth Jim Snook Jeff Krichmar Brian Cox Alisha Lawson Thomas Allen Donatello Darwin V Darwin X BrainWorks Segway B Build A Brain Team

17 Construction of an Intelligent Machine Following the Brain Based Model Design should be constrained by these principles: Active sensing and autonomous movement in the environment. Organizing the signals from the environment into categories without a priori knowledge or instruction. Incorporating a simulated brain with detailed neural dynamics and neuroanatomy. Engaging in a behavioral task and adaptation of behavior when an important environmental event occurs. Allowing comparisons with experimental data acquired from animal systems.

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