Biology subjects

Maunsell, J. H.

Publications and source records attributed to Maunsell, J. H..

7 recordsLinked to original sources

bioRxiv2026

Transient noradrenergic signals act as online controllers of perceptual computation

Neuromodulatory signals influence perception, arousal and learning, but their effects on behavioral performance can arise from fundamentally different computations. A transient signal could alter a persistent operating state, control an ongoing computation, or provide a teaching signal that modifies future behavior. These mechanisms make distinct predictions about the time course of neuromodulatory influence on behavior within and across trials. Here we applied temporally randomized white-noise optogenetic activation of locus coeruleus (LC) noradrenergic neurons while monkeys performed a visual match-to-sample discrimination task. Computational simulations established that reverse correlation could distinguish the temporal signatures of the modeled persistent-state, controller and outcome-dependent teaching mechanisms based on behavioral choices. LC activation selectively increased perceptual sensitivity for contralateral stimuli during a narrow time window preceding sensory input, without a detectable change in decision criterion. Cross-trial effects were smaller than predicted by the persistent-state and teaching models, despite sufficient sensitivity to detect the predicted effects. After accounting for the measured LC spiking response to optical stimulation, we estimated that behaviorally effective LC activity occurred approximately 50 ms before sensory input. The temporal weighting of optical stimulation predicted contralateral perceptual outcomes on held-out individual trials. Together, these findings support a temporally and spatially selective controller-like contribution of transient LC activation to ongoing perception among the mechanisms tested. More broadly, they demonstrate how the temporal structure of causal effects can distinguish computational roles of neuromodulatory signals that are difficult to resolve from average behavioral performance.

neuroscience↗

bioRxiv2025

Neuronal normalization in monkey MT is an intensity-weighted average

Normalization is a ubiquitous neuronal computation that is important for safeguarding stimulus selectivity. However, normalization strength has been found to vary greatly across neurons. Here, we show that the normalization of responses by neurons in the macaque middle temporal visual area (MT) is profoundly affected by the receptive field responsivity at each stimulus location. An intensity-weighted normalization model, in which intensity is defined as the product of stimulus contrast and a location-specific receptive field weight, explains most of the previously observed variability in normalization across neurons. It furthermore explains systematic changes in the semi-saturation contrast of contrast response functions at different receptive field locations. Finally, intensity-weighted normalization reveals that spontaneous activity can be viewed as unknown excitatory drive that has measurable intensity and contributes to normalization equivalently to experimental stimuli.

neuroscience↗

bioRxiv2025

Neuronal modulation of the superior colliculus associated with visual spatial attention represents perceptual sensitivity, independent of perceptual decision and motor biases

Neurons in the superior colliculus (SC), like those in the cerebral cortex, are strongly modulated in response to shifts in attention, but make a contribution that is distinct from attention-related modulations in visual cortex. It has been a point of contention whether attention-related enhancement of neuronal activity in SC is associated with selective increase in behavioral sensitivity (d) in neurons response fields or with animals decision bias, which is closely linked with motor planning. By independently controlling monkeys perceptual decision and motor criterion, we show that SC activity is strongly correlated with perceptual sensitivity at the neurons response field. Responses of the same SC neurons were unchanged in the face of correspondingly large changes in the perceptual decision criterion. Furthermore, the SC activity did not convey information about perceptual detection on individual trials. These results suggest that the SC contributes to the component of attentional states related to heightened perceptual sensitivity.

neuroscience↗

bioRxiv2023

Temporal weighting of cortical and subcortical spikes reveals stimulus dependent differences in their contributions to behavior.

The primary visual cortex (V1) and the superior colliculus (SC) both occupy stations early in the processing of visual information. They have long been thought to perform distinct functions, with V1 supporting perception of visual features and the SC regulating orienting to visual inputs. However, growing evidence suggests that the SC supports perception of many of the same visual features traditionally associated with V1. To distinguish V1 and SC contributions to visual processing, it is critical to determine whether both areas causally contribute to perception of specific visual stimuli. Here, mice reported changes in visual contrast or luminance near perceptual threshold while we presented white noise patterns of optogenetic stimulation to V1 or SC inhibitory neurons. We then performed a reverse correlation analysis on the optogenetic stimuli to estimate a neuronal-behavioral kernel (NBK), a moment-to-moment estimate of the impact of V1 or SC inhibition on stimulus detection. We show that the earliest moments of stimulus-evoked activity in SC are critical for detection of both luminance or contrast changes. Strikingly, there was a robust stimulus-aligned modulation in the V1 contrast-detection NBK, but no sign of a comparable modulation for luminance detection. The data suggest that perception of visual contrast depends on both V1 and SC spiking, whereas mice preferentially use SC activity to detect changes in luminance. Electrophysiological recordings showed that neurons in both SC and V1 responded strongly to both visual stimulus types, while the reverse correlation analysis reveals when these neuronal signals actually contribute to visually-guided behaviors.

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bioRxiv2023

Normalization in mouse primary visual cortex

When multiple stimuli appear together in the receptive field of a visual cortical neuron, the response is typically close to the average of that neurons response to each individual stimulus. The departure from a linear sum of each individual response is referred to as normalization. In mammals, normalization has been best characterized in the visual cortex of macaques and cats. Here we study visually evoked normalization in the visual cortex of awake mice using optical imaging of calcium indicators in large populations of layer 2/3 (L2/3) V1 excitatory neurons and electrophysiological recordings across layers in V1. Regardless of recording method, mouse visual cortical neurons exhibit normalization to varying degrees. The distributions of normalization strength are similar to those described in cats and macaques, albeit slightly weaker on average.

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bioRxiv2021

Perceptual Weighting of V1 Spikes Revealed by Optogenetic White Noise Stimulation

During visually guided behaviors, mere hundreds of milliseconds can elapse between a sensory input and its associated behavioral response. How spikes occurring at different times are integrated to drive perception and action remains poorly understood. We delivered random trains of optogenetic stimulation (white noise) to excite inhibitory interneurons in V1 of mice while they performed a visual detection task. We then performed a reverse correlation analysis on the optogenetic stimuli to generate a neuronal-behavioral kernel: an unbiased, temporally-precise estimate of how suppression of V1 spiking at different moments around the onset of a visual stimulus affects detection of that stimulus. Electrophysiological recordings enabled us to capture the effects of optogenetic stimuli on V1 responsivity and revealed that the earliest stimulus-evoked spikes are preferentially weighted for guiding behavior. These data demonstrate that white noise optogenetic stimulation is a powerful tool for understanding how patterns of spiking in neuronal populations are decoded in generating perception and action. Significance StatementHow the brain decodes dynamic neuronal responses to generate perception and behavior remains uncertain. A critical challenge is determining the relative contribution of spikes that occur at different times on the timescale of brain computations (tens of ms). Optogenetic tools permit causal investigations into neuronal-behavioral relationships, but are generally impractical for obtaining millisecond resolution. We circumvented this by delivering random (white noise) patterns of optogenetic inhibition to the primary visual cortex of behaving mice during visual tasks. Aligning optogenetic stimuli to task outcomes (hit, miss) yielded a neuronal-behavioral kernel - a temporal weighting that describes how inhibition at different moments impacts perception of visual stimuli. Thus, this method is a powerful tool for linking neuronal spiking, perception, and behavior.

neuroscience↗

bioRxiv2019

Mice preferentially use increases in cerebral cortex spike rates to detect changes in visual stimuli

Whenever the retinal image changes some neurons in visual cortex increase their rate of firing, while others decrease their rate of firing. Linking specific sets of neuronal responses with perception and behavior is essential for understanding mechanisms of neural circuit computation. We trained mice to perform visual detection tasks and used optogenetic perturbations to increase or decrease neuronal spiking primary visual cortex (V1). Perceptual reports were always enhanced by increments in V1 spike counts and impaired by decrements, even when increments and decrements were delivered to the same neuronal populations. Moreover, detecting changes in cortical activity depended on spike count integration rather than instantaneous changes in spiking. Recurrent neural networks trained in the task similarly relied on increments in neuronal activity when activity was costly. This work clarifies neuronal decoding strategies employed by cerebral cortex to translate cortical spiking into percepts that can be used to guide behavior.

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