Characterizing and interpreting the influence of internal variables on sensory activity

Lange, Richard; Haefner, Ralf M.

doi:10.1016/j.conb.2017.07.006

Cited by 30 publications

(25 citation statements)

References 39 publications

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“…First, our primary goal was to assess how simple decoders might approximate optimal performance, a concept that is not applicable to the prediction of choice from neural responses. Second, since the publication of the original reports of choice-correlated fluctuations of MT response, a number of alternative interpretations of this relation have been put forth (Bondy et al 2018;Lange and Haefner 2017;Nienborg and Cumming 2009). Likewise, the mathematical frameworks for relating the activity of neural ensembles to choices have become increasingly sophisticated (Panzeri et al 2017).…”

Section: Discussionmentioning

confidence: 99%

A simple linear readout of MT supports motion direction-discrimination performance

Yates

Katz

Levi

et al. 2020

Journal of Neurophysiology

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Motion discrimination is a well-established model system for investigating how sensory signals are used to form perceptual decisions. Classic studies relating single-neuron activity in the middle temporal area (MT) to perceptual decisions have suggested that a simple linear readout could underlie motion discrimination behavior. A theoretically optimal readout, in contrast, would take into account the correlations between neurons and the sensitivity of individual neurons at each time point. However, it remains unknown how sophisticated the readout needs to be to support actual motion-discrimination behavior or to approach optimal performance. In this study, we evaluated the performance of various neurally plausible decoders, trained to discriminate motion direction from small ensembles of simultaneously recorded MT neurons. We found that decoding the stimulus without knowledge of the interneuronal correlations was sufficient to match an optimal (correlation aware) decoder. Additionally, a decoder could match the psychophysical performance of the animals with flat integration of up to half the stimulus and inherited temporal dynamics from the time-varying MT responses. These results demonstrate that simple, linear decoders operating on small ensembles of neurons can match both psychophysical performance and optimal sensitivity without taking correlations into account and that such simple read-out mechanisms can exhibit complex temporal properties inherited from the sensory dynamics themselves. NEW & NOTEWORTHY Motion perception depends on the ability to decode the activity of neurons in the middle temporal area. Theoretically optimal decoding requires knowledge of the sensitivity of neurons and interneuronal correlations. We report that a simple correlation-blind decoder performs as well as the optimal decoder for coarse motion discrimination. Additionally, the decoder could match the psychophysical performance with moderate temporal integration and dynamics inherited from sensory responses.

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Section: Discussionmentioning

confidence: 99%

A simple linear readout of MT supports motion direction-discrimination performance

Yates

Katz

Levi

et al. 2020

Journal of Neurophysiology

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“…Unlike

which is an effect of the decision-making threshold mechanism and shared by all neurons,

is specific to and generally different for each neuron, reflecting its role in the perceptual decision-making process. A CC stimulus dependence may arise as a result of stimulus-dependent decision feedback ( Haefner et al, 2016 ; Bondy et al, 2018 ; Lange and Haefner, 2017 ), or other sources of stimulus-dependent cross-neuronal correlations ( Ponce-Alvarez et al, 2013 ; Orbán et al, 2016 ) such as shared gain fluctuations ( Goris et al, 2014 ). In fact, we will show below that gain-induced stimulus-dependent cross-neuronal correlations account for observed features in our empirical data.…”

Section: Resultsmentioning

confidence: 99%

“…Experimental ( Nienborg and Cumming, 2009 ; Cohen and Maunsell, 2009a ; Bondy et al, 2018 ), and theoretical ( Lee and Mumford, 2003 ; Maunsell and Treue, 2006 ; Wimmer et al, 2015 ; Haefner et al, 2016 ; Ecker et al, 2016 ) work indicates that top-down modulations of sensory responses play an important role in the perceptual decision-making process. In particular, feedback signals are expected to show cell-specific stimulus dependencies associated with the tuning properties ( Lange and Haefner, 2017 ). Different coding theories attribute different roles to the feedback signals, for example, conveying predictive errors ( Rao and Ballard, 1999 ) or prior information for probabilistic inference ( Lee and Mumford, 2003 ; Fiser et al, 2010 ; Haefner et al, 2016 ; Tajima et al, 2016 ; Bányai and Orbán, 2019 , Bányai et al, 2019 ; Lange and Haefner, 2020 ).…”

Section: Discussionmentioning

confidence: 99%

Stimulus-dependent relationships between behavioral choice and sensory neural responses

Chicharro

Panzeri

Haefner

2021

eLife

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Understanding perceptual decision-making requires linking sensory neural responses to behavioral choices. In two-choice tasks, activity-choice covariations are commonly quantified with a single measure of choice probability (CP), without characterizing their changes across stimulus levels. We provide theoretical conditions for stimulus dependencies of activity-choice covariations. Assuming a general decision-threshold model, which comprises both feedforward and feedback processing and allows for a stimulus-modulated neural population covariance, we analytically predict a very general and previously unreported stimulus dependence of CPs. We develop new tools, including refined analyses of CPs and generalized linear models with stimulus-choice interactions, which accurately assess the stimulus- or choice-driven signals of each neuron, characterizing stimulus-dependent patterns of choice-related signals. With these tools, we analyze CPs of macaque MT neurons during a motion discrimination task. Our analysis provides preliminary empirical evidence for the promise of studying stimulus dependencies of choice-related signals, encouraging further assessment in wider data sets.

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“…This latter process can contextually enable, route, and gate choice information [18], thus greatly affecting how choice signals are distributed in these areas. It is therefore possible that during a task choice signals are simultaneously represented across multiple areas, with amplitudes that, relative to concurrently represented processes, are areas-specific and contextually modulated by the internal state of the animal [19].…”

Section: Introductionmentioning

confidence: 99%

Distributed context-dependent choice information in mouse dorsal-parietal cortex

Orlandi¹,

Abdolrahmani²,

Aoki³

et al. 2021

Preprint

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Choice information appears in the brain as distributed signals with top-down and bottom-up components that together support decision-making computations. In sensory and associative cortical regions, the presence of choice signals, their strength, and area specificity are known to be elusive and changeable, limiting a cohesive understanding of their computational significance. In this study, examining the mesoscale activity in mouse posterior cortex during a complex visual discrimination task, we found that broadly distributed choice signals defined a decision variable in a low-dimensional embedding space of multi-area activations, particularly along the ventral visual stream. The subspace they defined was near-orthogonal to concurrently represented sensory and motor-related activations, and it was modulated by task difficulty and contextually by the attention state of the animals. To mechanistically relate choice representations to decision-making computations, we trained recurrent neural networks with the choices of the animals and found an equivalent decision variable whose context-dependent dynamics agreed with that of the neural data. In conclusion, our results demonstrated an independent decision variable broadly represented in the posterior cortex, controlled by task features and cognitive demands. Its dynamics reflected decision computations, possibly linked to context-dependent feedback signals used for probabilistic-inference computations in variable animal-environment interactions.

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Characterizing and interpreting the influence of internal variables on sensory activity

Cited by 30 publications

References 39 publications

A simple linear readout of MT supports motion direction-discrimination performance

A simple linear readout of MT supports motion direction-discrimination performance

Stimulus-dependent relationships between behavioral choice and sensory neural responses

Distributed context-dependent choice information in mouse dorsal-parietal cortex

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