From Neuroelectrodynamics to Thinking Machines

@article{Aur2011FromNT,
  title={From Neuroelectrodynamics to Thinking Machines},
  author={D. Aur},
  journal={Cognitive Computation},
  year={2011},
  volume={4},
  pages={4-12}
}
  • D. Aur
  • Published 2011
  • Computer Science
  • Cognitive Computation
  • Natural systems can provide excellent solutions to build artificial intelligent systems. The brain represents the best model of computation that leads to general intelligent action. However, current mainstream models reflect a weak understanding of computations performed in the brain that is translated in a failure of building powerful thinking machines. Specifically, temporal reductionist neural models elude the complexity of information processing since spike timing models reinforce the idea… CONTINUE READING
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    References

    SHOWING 1-10 OF 72 REFERENCES
    Towards an artificial brain.
    • 43
    Resonate-and-fire neurons
    • 366
    • PDF
    A Spiking Neuron as Information Bottleneck
    • 20
    • PDF
    Towards an integrative theory of cognition.
    • R. Poznanski
    • Medicine, Computer Science
    • Journal of integrative neuroscience
    • 2002
    • 23
    Neuronal spatial learning
    • 9
    Spike timing-dependent plasticity: a Hebbian learning rule.
    • 1,083
    • PDF
    Reading the Neural Code: What do Spikes Mean for Behavior?
    • 8
    • PDF
    Where is the ‘Jennifer Aniston neuron’?
    • 1
    • PDF
    Population Encoding With Hodgkin–Huxley Neurons
    • A. Lazar
    • Mathematics, Medicine
    • IEEE Transactions on Information Theory
    • 2010
    • 44
    • PDF
    A carbon nanotube implementation of temporal and spatial dendritic computations
    • 31
    • PDF