### Abstract

1. A longstanding view of simple cells is that they sum their inputs linearly. However, the linear model falls short of a complete account of simple-cell direction selectivity. We have developed a nonlinear model of simple-cell responses (hereafter referred to as the normalization model) to explain a larger body of physiological data. 2. The normalization model consists of an underlying linear stage along with two additional nonlinear stages. The first is a half-squaring nonlinearity; half-squaring is half- wave rectification followed by squaring. The second is a divisive normalization nonlinearity in which each model cell is suppressed by the pooled activity of a large number of cells. 3. By comparing responses with counterphase (flickering) gratings and drifting gratings, researchers have demonstrated that there is a nonlinear contribution to simple-cell responses. Specifically they found 1) that the linear prediction from counterphase grating responses underestimates a direction index computed from drifting grating responses, 2) that the linear prediction correctly estimates responses to gratings drifting in the preferred direction, and 3) that the linear prediction overestimates responses to gratings drifting in the nonpreferred direction. 4. We have simulated model cell responses and derived mathematical expressions to demonstrate that the normalization model accounts for this empirical data. Specifically the model behaves as follows. 1) The linear prediction from counterphase data underestimates the direction index computed from drifting grating responses. 2) The linear prediction from counterphase data overestimates the response to gratings drifting in the nonpreferred direction. The discrepancy between the linear prediction and the actual response is greater when using higher contrast stimuli. 3) For an appropriate choice of contrast, the linear prediction from counterphase data correctly estimates the response to gratings drifting in the preferred direction. For higher contrasts the linear prediction overestimates the actual response, and for lower contrasts the linear prediction underestimates the actual response. 5. In addition, the normalization model is qualitatively consistent with data on the dynamics of simple-cell responses. Tolhurst et al. found that simple cells respond with an initial transient burst of activity when a stimulus first appears. The normalization model behaves similarly; it takes some time after a stimulus first appears before the model cells are fully normalized. We derived the dynamics of the model and found that the transient burst of activity in model cells depends in a particular way on stimulus contrast. The burst is short for high-contrast stimuli and longer for low-contrast stimuli. 6. The importance of these results is that the normalization model preserves the essential features of linearity in the face of apparently contradictory behavior. According to the model, a cell's direction selectivity is attributed to the underlying linear stage, and a cell's nonlinear behavior is attributed to half-squaring and normalization.

Original language | English (US) |
---|---|

Pages (from-to) | 1885-1898 |

Number of pages | 14 |

Journal | Journal of Neurophysiology |

Volume | 70 |

Issue number | 5 |

State | Published - 1993 |

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### ASJC Scopus subject areas

- Physiology
- Neuroscience(all)

### Cite this

**Modeling simple-cell direction selectivity with normalized, half-squared, linear operators.** / Heeger, D. J.

Research output: Contribution to journal › Article

*Journal of Neurophysiology*, vol. 70, no. 5, pp. 1885-1898.

}

TY - JOUR

T1 - Modeling simple-cell direction selectivity with normalized, half-squared, linear operators

AU - Heeger, D. J.

PY - 1993

Y1 - 1993

N2 - 1. A longstanding view of simple cells is that they sum their inputs linearly. However, the linear model falls short of a complete account of simple-cell direction selectivity. We have developed a nonlinear model of simple-cell responses (hereafter referred to as the normalization model) to explain a larger body of physiological data. 2. The normalization model consists of an underlying linear stage along with two additional nonlinear stages. The first is a half-squaring nonlinearity; half-squaring is half- wave rectification followed by squaring. The second is a divisive normalization nonlinearity in which each model cell is suppressed by the pooled activity of a large number of cells. 3. By comparing responses with counterphase (flickering) gratings and drifting gratings, researchers have demonstrated that there is a nonlinear contribution to simple-cell responses. Specifically they found 1) that the linear prediction from counterphase grating responses underestimates a direction index computed from drifting grating responses, 2) that the linear prediction correctly estimates responses to gratings drifting in the preferred direction, and 3) that the linear prediction overestimates responses to gratings drifting in the nonpreferred direction. 4. We have simulated model cell responses and derived mathematical expressions to demonstrate that the normalization model accounts for this empirical data. Specifically the model behaves as follows. 1) The linear prediction from counterphase data underestimates the direction index computed from drifting grating responses. 2) The linear prediction from counterphase data overestimates the response to gratings drifting in the nonpreferred direction. The discrepancy between the linear prediction and the actual response is greater when using higher contrast stimuli. 3) For an appropriate choice of contrast, the linear prediction from counterphase data correctly estimates the response to gratings drifting in the preferred direction. For higher contrasts the linear prediction overestimates the actual response, and for lower contrasts the linear prediction underestimates the actual response. 5. In addition, the normalization model is qualitatively consistent with data on the dynamics of simple-cell responses. Tolhurst et al. found that simple cells respond with an initial transient burst of activity when a stimulus first appears. The normalization model behaves similarly; it takes some time after a stimulus first appears before the model cells are fully normalized. We derived the dynamics of the model and found that the transient burst of activity in model cells depends in a particular way on stimulus contrast. The burst is short for high-contrast stimuli and longer for low-contrast stimuli. 6. The importance of these results is that the normalization model preserves the essential features of linearity in the face of apparently contradictory behavior. According to the model, a cell's direction selectivity is attributed to the underlying linear stage, and a cell's nonlinear behavior is attributed to half-squaring and normalization.

AB - 1. A longstanding view of simple cells is that they sum their inputs linearly. However, the linear model falls short of a complete account of simple-cell direction selectivity. We have developed a nonlinear model of simple-cell responses (hereafter referred to as the normalization model) to explain a larger body of physiological data. 2. The normalization model consists of an underlying linear stage along with two additional nonlinear stages. The first is a half-squaring nonlinearity; half-squaring is half- wave rectification followed by squaring. The second is a divisive normalization nonlinearity in which each model cell is suppressed by the pooled activity of a large number of cells. 3. By comparing responses with counterphase (flickering) gratings and drifting gratings, researchers have demonstrated that there is a nonlinear contribution to simple-cell responses. Specifically they found 1) that the linear prediction from counterphase grating responses underestimates a direction index computed from drifting grating responses, 2) that the linear prediction correctly estimates responses to gratings drifting in the preferred direction, and 3) that the linear prediction overestimates responses to gratings drifting in the nonpreferred direction. 4. We have simulated model cell responses and derived mathematical expressions to demonstrate that the normalization model accounts for this empirical data. Specifically the model behaves as follows. 1) The linear prediction from counterphase data underestimates the direction index computed from drifting grating responses. 2) The linear prediction from counterphase data overestimates the response to gratings drifting in the nonpreferred direction. The discrepancy between the linear prediction and the actual response is greater when using higher contrast stimuli. 3) For an appropriate choice of contrast, the linear prediction from counterphase data correctly estimates the response to gratings drifting in the preferred direction. For higher contrasts the linear prediction overestimates the actual response, and for lower contrasts the linear prediction underestimates the actual response. 5. In addition, the normalization model is qualitatively consistent with data on the dynamics of simple-cell responses. Tolhurst et al. found that simple cells respond with an initial transient burst of activity when a stimulus first appears. The normalization model behaves similarly; it takes some time after a stimulus first appears before the model cells are fully normalized. We derived the dynamics of the model and found that the transient burst of activity in model cells depends in a particular way on stimulus contrast. The burst is short for high-contrast stimuli and longer for low-contrast stimuli. 6. The importance of these results is that the normalization model preserves the essential features of linearity in the face of apparently contradictory behavior. According to the model, a cell's direction selectivity is attributed to the underlying linear stage, and a cell's nonlinear behavior is attributed to half-squaring and normalization.

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UR - http://www.scopus.com/inward/citedby.url?scp=0027494491&partnerID=8YFLogxK

M3 - Article

C2 - 8294961

AN - SCOPUS:0027494491

VL - 70

SP - 1885

EP - 1898

JO - Journal of Neurophysiology

JF - Journal of Neurophysiology

SN - 0022-3077

IS - 5

ER -