Final published version
Licence: CC BY: Creative Commons Attribution 4.0 International License
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Describing complex cells in primary visual cortex : a comparison of context and multi-filter LN models. / Westö, Johan; May, Patrick.
In: Journal of Neurophysiology, Vol. 120, No. 2, 08.2018, p. 703-719.Research output: Contribution to Journal/Magazine › Journal article › peer-review
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TY - JOUR
T1 - Describing complex cells in primary visual cortex
T2 - a comparison of context and multi-filter LN models
AU - Westö, Johan
AU - May, Patrick
PY - 2018/8
Y1 - 2018/8
N2 - Receptive field (RF) models are an important tool for deciphering neural responses to sensory stimuli. The two currently popular RF models are multi-filter linear-nonlinear (LN) models and context models. Models are, however, never correct and they rely on assumptions to keep them simple enough to be interpretable. As a consequence, different models describe different stimulus-response mappings, which may or may not be good approximations of real neural behavior. In the current study, we take up two tasks: First, we introduce new ways to estimate context models with realistic nonlinearities, that is, with logistic and exponential functions. Second, we evaluate context models and multi-filter LN models in terms of how well they describe recorded data from complex cells in cat primary visual cortex. Our results, based on single-spike information and correlation coefficients, indicate that context models outperform corresponding multi-filter LN models of equal complexity (measured in terms of number of parameters), with the best increase in performance being achieved by the novel context models. Consequently, our results suggest that the multi-filter LN-model framework is suboptimal for describing the behavior of complex cells: the context-model framework is clearly superior while still providing interpretable quantizations of neural behavior.
AB - Receptive field (RF) models are an important tool for deciphering neural responses to sensory stimuli. The two currently popular RF models are multi-filter linear-nonlinear (LN) models and context models. Models are, however, never correct and they rely on assumptions to keep them simple enough to be interpretable. As a consequence, different models describe different stimulus-response mappings, which may or may not be good approximations of real neural behavior. In the current study, we take up two tasks: First, we introduce new ways to estimate context models with realistic nonlinearities, that is, with logistic and exponential functions. Second, we evaluate context models and multi-filter LN models in terms of how well they describe recorded data from complex cells in cat primary visual cortex. Our results, based on single-spike information and correlation coefficients, indicate that context models outperform corresponding multi-filter LN models of equal complexity (measured in terms of number of parameters), with the best increase in performance being achieved by the novel context models. Consequently, our results suggest that the multi-filter LN-model framework is suboptimal for describing the behavior of complex cells: the context-model framework is clearly superior while still providing interpretable quantizations of neural behavior.
KW - Receptive field
KW - Complex cell
KW - Context model
KW - LN model
KW - Stimulus-response mapping
U2 - 10.1152/jn.00916.2017
DO - 10.1152/jn.00916.2017
M3 - Journal article
VL - 120
SP - 703
EP - 719
JO - Journal of Neurophysiology
JF - Journal of Neurophysiology
SN - 0022-3077
IS - 2
ER -