Improved system identification using artificial neural networks and analysis of individual differences in responses of an identified neuron

@article{Meruelo2016ImprovedSI,
  title={Improved system identification using artificial neural networks and analysis of individual differences in responses of an identified neuron},
  author={Alicia Costalago Meruelo and David M. Simpson and Sandor M. Veres and Philip L. Newland},
  journal={Neural networks : the official journal of the International Neural Network Society},
  year={2016},
  volume={75},
  pages={
          56-65
        }
}
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14 Citations
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Methods Citations
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References

SHOWING 1-10 OF 58 REFERENCES
A Natural Algorithmic Approach to the Structural Optimisation of Neural Networks
  • N. Suraweera, D. Ranasinghe
  • Computer Science, Business
    2008 4th International Conference on Information and Automation for Sustainability
  • 2008
TLDR
The obtained results and comparisons done with past research work has clearly shown that this method of optimisation is by far, the best approach for adaptive structural optimisation of artificial neural networks.
Optimizing feedforward artificial neural network architecture
A system identification analysis of neural adaptation dynamics and nonlinear responses in the local reflex control of locust hind limbs
TLDR
Key system dynamics remain relatively unchanged during repetitive stimulation while output amplitude adaptation is occurring, and through the use of a parsimonious model structure and Monte Carlo simulations the effect was small and probably of little physiological relevance.
The dynamics of analogue signalling in local networks controlling limb movement
TLDR
Improved data analysis methods for describing neuronal function that are more robust and allow statistical analysis are developed and tested, finding that nonlinear models provided an improved fit in describing the response properties of interneurons that were then classified with statistical clustering methods.
Deriving neural network controllers from neuro-biological data: implementation of a single-leg stick insect controller
TLDR
The results suggest that the single-leg controllers of a biomimetic six-legged robot equipped with standard DC motors are suitable as modules for hexapod controllers, and they might therefore bridge morphological- and behavioral-based approaches to stick insect locomotion control.
Neuromuscular reflex control of limb movement - validating models of the locusts hind leg control system using physiological input signals
TLDR
The results show that the performance of the Wiener/Volterra models at predicting the response of the system to physiologically realistic inputs is poor and investigation into this failure has allowed a simpler and more accurate model to be proposed.
Dynamics of neurons controlling movements of a locust hind leg: Wiener kernel analysis of the responses of proprioceptive afferents.
TLDR
The Wiener kernel method has demonstrated that responses in the position-sensitive afferents are representative of a constant gain low-pass filter with a cutoff frequency of approximately 80 Hz, whereas those in the velocity- and acceleration-sensitive affirmations are band passed.
Neural networks for control systems
  • P. Antsaklis
  • Computer Science
    IEEE Trans. Neural Networks
  • 1990
TLDR
A description is given of 11 papers from the April 1990 special issue on neural networks in control systems of IEEE Control Systems Magazine, on the design of associative memories using feedback neural networks and the modeling of nonlinear chemical systems using neural networks.
Dynamics of neurons controlling movements of a locust hind leg. III. Extensor tibiae motor neurons.
TLDR
Model predictions of the responses of the motor neurons showed that the first-order characterization poorly predicted the actual responses of FETi and SETi to FeCO stimulation, whereas the addition of the second-order (nonlinear) term markedly improved the performance of the model.
Global Structure, Robustness, and Modulation of Neuronal Models
TLDR
A global analysis of the structure of a conductance-based model neuron finds correlates of this dual robustness and sensitivity in this model, which implies that neuromodulators that alter a sensitive set of conductances will have powerful, and possibly state-dependent, effects.
...
1
2
3
4
5
...