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Neural Modeling: The NEF Approach

  • Bernd J. Kröger
  • Trevor Bekolay
Chapter

Abstract

This chapter presents the “neural engineering framework” (NEF), which is a well-documented and easy-to-use framework from the computer programming point of view. In particular, we show how to use this framework to build a neural model for word generation and apply that model to simulate a picture naming test. The NEF can use neuron models that closely emulate neurophysiology in that they produce action potentials at specific points in time. Sensory, motor, and cognitive states are implemented at the neural level by distributed representations (complex neural activation patterns), occurring in ensembles and buffers. The common representation used to communicate between modalities is the mathematical construct of semantic pointers. The temporal control of neural processing steps is realized in the NEF by an action selection system implemented by basal ganglia and thalamus models. Words, semantic relations between words, phonological representations of syllables, and phonological relations between syllables and words are modeled in this approach through semantic pointer networks. These semantic pointer networks can be seen as the end point of an acquisition process that builds long-term declarative memories.

Keywords

LIF-neurons Neural engineering framework Semantic pointer architecture Action selection Action sequencing 

References

Sections 7.1 to 7.4

  1. Eliasmith C (2013) How to build a brain. Oxford University Press, OxfordCrossRefGoogle Scholar
  2. Eliasmith C, Anderson CH (2004) Neural engineering: computation, representation, and dynamics in neurobiological systems. MIT Press, CambridgeGoogle Scholar
  3. Eliasmith C, Stewart TC, Choo X, Bekolay T, DeWolf T, Tan Y (2012) A large-scale model of the functioning brain. Science 338:1202–1205CrossRefGoogle Scholar
  4. Kröger BJ, Crawford E, Bekolay T, Eliasmith C (2016) Modeling interactions between speech production and perception: speech error detection at semantic and phonological levels and the inner speech loop. Front Comput Neurosci 10:51CrossRefGoogle Scholar
  5. Stewart TC, Eliasmith C (2014) Large-scale synthesis of functional spiking neural circuits. Proc IEEE 102:881–898CrossRefGoogle Scholar

Section 7.5

  1. Senft V, Stewart TC, Bekolay T, Eliasmith C, Kröger BJ (2016) Reduction of dopamine in basal ganglia and its effects on syllable sequencing in speech: a computer simulation study. Basal Ganglia 6:7–17CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Bernd J. Kröger
    • 1
  • Trevor Bekolay
    • 2
  1. 1.Department of Phoniatrics, Pedaudiology and Communications DisordersRWTH Aachen UniversityAachenGermany
  2. 2.Applied Brain ResearchWaterlooCanada

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