Linear-nonlinear-Poisson cascade model
The Linear-nonlinear-Poisson (LNP) cascade model [1][2] is used to encode the stimuli of neural ensembles in the visual system. As opposed to traditional approaches, which use stimuli such as drifting or pulsating bars, the LNP cascade model uses white noise as the stimuli.
Advantages and Disadvantages
The LNP cascade model has the following advantages [2]:
- Spans a wide range of inputs.
- Can capture nonlinearities, such as spike threshold and response saturation that are inherit in many neurons.
- Robust to fluctuations in responsivity.
- Avoids adaptation to stimuli, such as can occur with strong or prolonged stimuli.
- Can easily be extended to multiple neuron recording.
The randomness and temporal independence of this method also is disadvantageous since it makes it difficult to assess certain aspects of the neural response and does not give information about the response variability [2].
References
- ^ Simoncelli, E. P., Paninski, L., Pillow, J. & Swartz, O. in The Cognitive Neurosciences 3rd edn (ed. Gazzaniga, M.) 327-338 (MIT, 2004)
- ^ a b c Chichilnisky, E. J., A simple white noise analysis of neuronal light responses. Network: Computation in Neural Systems 12:199-213. (2001)
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