Nicoladie Tam, Ph.D. Nicoladie Tam, Ph.D. brain brain
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Project Overview

  • Neural simulator is a useful research tool to model brain functions by a computer so that we can explore the parameters much easier than the real brain.
  • Neural simulator is an implementation of the mathematical equations that control the functions of neurons, neural networks and the brain.


  • Since we acknowledge the limitations of the human brain in processing interactions of complex functions, and its inefficiency in computing mathematical functions, we delegate these complex, tedious tasks to the job of the computer, and let it crunch out the number for us.
  • On the contrary, the human brain is very good at generalizing results and abstracting concepts, we can accelerate the process of discovery by letting the computer simulates the results for us to analysis.
  • Computational neuroscience essentially is a science of using the computer's processing power to solve complex mathematical equations of the brain such that results can be simplified by human.
  • Computational neuroscience also involves using the same computer to analyze complex data crunched out by the computer model, using statistical analysis of these complex interactions, which then allows human to digest the volume of data created from the simulations.
  • This allows us to explore different parameter space of the mathematical model, and test the hypotheses of how the brain works numerically.
  • It also allows us to study brain functions and behavior, from cellular level to physiological level to cognitive and emotional level without sacrificing any details.
  • Most importantly, it provides a tool to experiment with a computer model without using animals.

Research Objectives

  • Explore the parameter space of the neural model
  • Validate the mathematical model with real biological data
  • Refine the mathematical model iteratively with experimentations
  • Abstract the complex interactions among neurons into simpler conceptual frameworks
  • Construct theories and principles of neural processing in brain functions from the simulation results

Specific Goals

  • Develop a generalizeable neural simulator that uses plug-and-play modular designs to create an extensible model of the brain built with detailed neural circuitry
  • Implement an integrated simulator-analysis computer program to analyze the simulated data statistically
  • Extract the simulation and analysis results into abstract model of intellectual function of the brain that is based on known neurophysiological equations

The Challenge

  • How to create a generalizeable neural simulator that spans the level of complexity from cellular level to physiological level to cognitive/behavioral level
  • How the reduce the complexity of the nervous sytem into a simpler, more intuitive model
  • How to abstract complex data into a conceptual model
  • How to represent neural information from neural spike code level into cognitive intellectual knowledge
  • How to take advantage of the statistical properties of the brain for efficient, robust computation and processing
  • How does parallel processing of the brain overcome the intrinsic slow and limiting power of neuron computing

The Solutions

  • For implementation of neural simulator:
  • See publication: Tam, D. C. and Hutson, R. K. (1993) An object-oriented paradigm for the design of realistic neural simulators. In: Computation Neural Systems. (F. H. Eeckman and J. M. Bower, eds.) Kluwer Academic Publishers, Norwell, MA. pp. 115-119.
  • See publication: Tam, D. C. (1992) A generalizable object-oriented neural simulator for reconstructing functional properties of biological neuronal networks. Proceedings of the Simulation Technology Conference. Nov., 1992, pp. 551-555.
  • See publication: Tam, D. C. (1992) Object-oriented programming techniques for implementing generalizable models. Proceedings of the Simulation Technology Conference. Nov., 1992, pp. 300-303.

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