http://hdl.handle.net/2451/33722 Sat, 11 Apr 2026 16:04:24 GMT 2026-04-11T16:04:24Z http://hdl.handle.net/2451/63809 Title: Pupillometry software toolbox (MATLAB) Authors: Burlingham, Charlie; Mirbagheri, Saghar; Heeger, David Abstract: The pupil dilates and re-constricts following task events. It is popular to model this task-evoked pupil response as a linear transformation of event-locked impulses, whose amplitudes are used as estimates of arousal. We show that this model is incorrect and propose an alternative model based on the physiological finding that a common neural input drives saccades and pupil size. The estimates of arousal from our model agreed with key predictions: arousal scaled with task difficulty and behavioral performance but was invariant to small differences in trial duration. Moreover, the model offers a unified explanation for a wide range of phenomena: entrainment of pupil size and saccades to task timing, modulation of pupil response amplitude and noise with task difficulty, reaction-time-dependent modulation of pupil response timing and amplitude, a constrictory pupil response time-locked to saccades, and task-dependent distortion of this saccade-locked pupil response. Thu, 03 Mar 2022 00:00:00 GMT http://hdl.handle.net/2451/63809 2022-03-03T00:00:00Z http://hdl.handle.net/2451/61543 Title: Supplemental Material for "Heading Perception Depends on Time-Varying Evolution of Optic Flow" Authors: Burlingham, Charles S.; Heeger, David J. Abstract: There is considerable support for the hypothesis that perception of heading in the presence of rotation is mediated by instantaneous optic flow. This hypothesis, however, has never been tested. We introduce a novel method, termed “non-varying phase motion," for generating a stimulus that conveys a single instantaneous optic flow field, even though the stimulus is presented for an extended period of time. In this experiment, observers viewed stimulus videos and performed a forced choice heading discrimination task. For non-varying phase motion, observers made large errors in heading judgments. This suggests that instantaneous optic flow is insufficient for heading perception in the presence of rotation. These errors were mostly eliminated when the velocity of phase motion was varied over time to convey the evolving sequence of optic flow fields corresponding to a particular heading. This demonstrates that heading perception in the presence of rotation relies on the time-varying evolution of optic flow. We hypothesize that the visual system accurately computes heading, despite rotation, based on optic acceleration, the temporal derivative of optic flow. Description: Data and Matlab code to reproduce the results published in: Burlingham CS, Heeger DJ, Heading Perception Depends on Time-Varying Evolution of Optic Flow, Proceedings of the National Academy of Sciences, 2020. Mon, 30 Nov 2020 00:00:00 GMT http://hdl.handle.net/2451/61543 2020-11-30T00:00:00Z http://hdl.handle.net/2451/61045 Title: Supplemental Material for "Dynamic Normalization" Authors: Heeger, David J.; Zemlianova, Klavdia O. Abstract: The normalization model has been applied to explain neural activity in diverse neural systems including primary visual cortex (V1). The model’s defining characteristic is that the response of each neuron is divided by a factor that includes a weighted sum of activity of a pool of neurons. In spite of the success of the normalization model, there are 3 unresolved issues. 1) Experimental evidence suggests that normalization in V1 operates via recurrent amplification, i.e., amplifying weak inputs more than strong inputs. It is unknown how normalization arises from recurrent amplification. 2) Experiments have demonstrated that normalization is weighted such each weight specifies how one neuron contributes to another’s normalization pool. It is unknown how weighted normalization arises from a recurrent circuit. 3) Neural activity in V1 exhibits complex dynamics, including gamma oscillations, linked to normalization. It is unknown how these dynamics emerge from normalization. Here, a new family of recurrent circuit models is reported, each of which comprises coupled neural integrators to implement normalization via recurrent amplification with arbitrary normalization weights, some of which can recapitulate key experimental observations of the dynamics of neural activity in V1. Description: Matlab code in this directory computes the results published in: Heeger DJ, Zemlianova KO, A recurrent circuit implements normalization, simulating the dynamics of V1 activity, Proceedings of the National Academy of Sciences, 2020. Preprint: Heeger DJ, Zemlianova KO, Dynamic Normalization, bioRxiv 10.1101/2020.03.22.002634, 2020. Sun, 22 Mar 2020 00:00:00 GMT http://hdl.handle.net/2451/61045 2020-03-22T00:00:00Z http://hdl.handle.net/2451/60439 Title: Supplementary material for "Oscillatory recurrent gated neural integrator circuits (ORGaNICs), a unifying theoretical framework for neural dynamics" Authors: Heeger, David J.; Mackey, Wayne E. Abstract: Working memory is an example of a cognitive and neural process that is not static but evolves dy-namically with changing sensory inputs; another example is motor preparation and execution. We intro-duce a theoretical framework for neural dynamics, based on oscillatory recurrent gated neural integrator circuits (ORGaNICs), and apply it to simulate key phenomena of working memory and motor control. The model circuits simulate neural activity with complex dynamics, including sequential activity and trav-eling waves of activity, that manipulate (as well as maintain) information during working memory. The same circuits convert spatial patterns of premotor activity to temporal profiles of motor control activity, and manipulate (e.g., time warp) the dynamics. Derivative-like recurrent connectivity, in particular, serves to manipulate and update internal models, an essential feature of working memory and motor ex-ecution. In addition, these circuits incorporate recurrent normalization, to ensure stability over time and robustness with respect to perturbations of synaptic weights. Description: Matlab code in this zip archive computes the results published in: Heeger DJ, Mackey WE, Oscillatory Recurrent Gated Neural Integrator Circuits (ORGaNICs): A Unifying Theoretical Framework for Neural Dynamics, Proceedings of the National Academy of Sciences, 2019. Sun, 01 Sep 2019 00:00:00 GMT http://hdl.handle.net/2451/60439 2019-09-01T00:00:00Z