Robbe L. T. Goris

Partitioning neuronal variability

Responses of sensory neurons differ across repeated measurements. This variability is usually treated as stochasticity arising within neurons or neural circuits. However, some portion of the variability arises from fluctuations in excitability due to factors that are not purely sensory, such as arousal, attention and adaptation. To isolate these fluctuations, we developed a model in which spikes are generated by a Poisson process whose rate is the product of a drive that is sensory in origin and a gain summarizing stimulus-independent modulatory influences on excitability. This model provides an accurate account of response distributions of visual neurons in macaque lateral geniculate nucleus and cortical areas V1, V2 and MT, revealing that variability originates in large part from excitability fluctuations that are correlated over time and between neurons, and that increase in strength along the visual pathway. The model provides a parsimonious explanation for observed systematic dependencies of response variability and covariability on firing rate.

Attention stabilizes the shared gain of V4 populations

Responses of sensory neurons represent stimulus information, but are also influenced by internal state. For example, when monkeys direct their attention to a visual stimulus, the response gain of specific subsets of neurons in visual cortex changes. Here, we develop a functional model of population activity to investigate the structure of this effect. We fit the model to the spiking activity of bilateral neural populations in area V4, recorded while the animal performed a stimulus discrimination task under spatial attention. The model reveals four separate time-varying shared modulatory signals, the dominant two of which each target task-relevant neurons in one hemisphere. In attention-directed conditions, the associated shared modulatory signal decreases in variance. This finding provides an interpretable and parsimonious explanation for previous observations that attention reduces variability and noise correlations of sensory neurons. Finally, the recovered modulatory signals reflect previous reward, and are predictive of subsequent choice behavior. DOI: http://dx.doi.org/10.7554/eLife.08998.001

Origin and Function of Tuning Diversity in Macaque Visual Cortex

Neurons in visual cortex vary in their orientation selectivity. We measured responses of V1 and V2 cells to orientation mixtures and fit them with a model whose stimulus selectivity arises from the combined effects of filtering, suppression, and response nonlinearity. The model explains the diversity of orientation selectivity with neuron-to-neuron variability in all three mechanisms, of which variability in the orientation bandwidth of linear filtering is the most important. The model also accounts for the cells diversity of spatial frequency selectivity. Tuning diversity is matched to the needs of visual encoding. The orientation content found in natural scenes is diverse, and neurons with different selectivities are adapted to different stimulus configurations. Single orientations are better encoded by highly selective neurons, while orientation mixtures are better encoded by less selective neurons. A diverse population of neurons therefore provides better overall discrimination capabilities for natural images than any homogeneous population.

Dissociation of Choice Formation and Choice-Correlated Activity in Macaque Visual Cortex

Responses of individual task-relevant sensory neurons can predict monkeys trial-by-trial choices in perceptual decision-making tasks. Choice-correlated activity has been interpreted as evidence that the responses of these neurons are causally linked to perceptual judgments. To further test this hypothesis, we studied responses of orientation-selective neurons in V1 and V2 while two macaque monkeys performed a fine orientation discrimination task. Although both animals exhibited a high level of neuronal and behavioral sensitivity, only one exhibited choice-correlated activity. Surprisingly, this correlation was negative: when a neuron fired more vigorously, the animal was less likely to choose the orientation preferred by that neuron. Moreover, choice-correlated activity emerged late in the trial, earlier in V2 than in V1, and was correlated with anticipatory signals. Together, these results suggest that choice-correlated activity in task-relevant sensory neurons can reflect postdecision modulatory signals. SIGNIFICANCE STATEMENT When observers perform a difficult sensory discrimination, repeated presentations of the same stimulus can elicit different perceptual judgments. This behavioral variability often correlates with variability in the activity of sensory neurons driven by the stimulus. Traditionally, this correlation has been interpreted as suggesting a causal link between the activity of sensory neurons and perceptual judgments. More recently, it has been argued that the correlation instead may originate in recurrent input from other brain areas involved in the interpretation of sensory signals. Here, we call both hypotheses into question. We show that choice-related activity in sensory neurons can be highly variable across observers and can reflect modulatory processes that are dissociated from perceptual decision-making.

Slow gain fluctuations limit benefits of temporal integration in visual cortex

Sensory neurons represent stimulus information with sequences of action potentials that differ across repeated measurements. This variability limits the information that can be extracted from momentary observations of a neurons response. It is often assumed that integrating responses over time mitigates this limitation. However, temporal response correlations can reduce the benefits of temporal integration. We examined responses of individual orientation-selective neurons in the primary visual cortex of two macaque monkeys performing an orientation-discrimination task. The signal-to-noise ratio of temporally integrated responses increased for durations up to a few hundred milliseconds but saturated for longer durations. This was true even when cells exhibited little or no adaptation in their response levels. These observations are well explained by a statistical response model in which spikes arise from a Poisson process whose stimulus-dependent rate is modulated by slow, stimulus-independent fluctuations in gain. The response variability arising from the Poisson process is reduced by temporal integration, but the slow modulatory nature of variability due to gain fluctuations is not. Slow gain fluctuations therefore impose a fundamental limit on the benefits of temporal integration.