Analyzing second order stochasticity of neural spiking under stimuli-bundle exposure

Glynn, Chris; Tokdar, Surya T.; Zaman, Azeem; Caruso, Valeria C.; Mohl, Jeff T.; Willett, Shawn M.

doi:10.1214/20-aoas1383

Cited by 7 publications

(7 citation statements)

References 19 publications

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“…Importantly, these reports have generally concerned responses pooled across trials. Trial-wise spike count distribution models such as those used here and/or faster subtrial analyses such as those we have introduced in previous work ( Caruso et al, 2018 ; Glynn et al, 2021 ) might indicate that such apparently averaging responses actually indicate fluctuations occurring on either the stimulus presentation or sub-stimulus presentation timescales, and not a true stable average (e.g., Figure 9a vs. Figure 9e ).…”

Section: Discussionmentioning

confidence: 83%

“…While it is visually evident that the spiking responses of these three V1 example units on combined AB stimulus presentations appear drawn from a mixture of the A-like and B-like response distributions, evaluating this systematically across the population requires a formal statistical assessment. We developed such an assessment in our previous work concerning fluctuating activity in the context of encoding of multiple simultaneous stimuli ( Caruso et al, 2018 ; Mohl et al, 2020 ; Glynn et al, 2021 ). In particular, we can model the firing rate behavior of neurons when two simultaneous grating stimuli A and B are presented in relation to the firing rates that occur when stimuli A and B are presented individually.…”

Section: Resultsmentioning

confidence: 99%

“…First, we identified fluctuating activity patterns in V1, evoked only by combinations of stimuli that are parsed as separate objects. These fluctuations were formally identified using a statistical analysis method benchmarked to the response patterns evoked by each of the stimuli independently ( Caruso et al, 2018 ; Mohl et al, 2020 ; Glynn et al, 2021 ). This finding suggests not only that multiplexing of information may be a general characteristic of sensory signals in the brain, but also implicates it in the process of separating vs. grouping of stimuli into objects.…”

Section: Discussionmentioning

confidence: 99%

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Coordinated multiplexing of information about separate objects in visual cortex

Jun

Ruff

Kramer

et al. 2022

eLife

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Sensory receptive fields are large enough that they can contain more than one perceptible stimulus. How, then, can the brain encode information about each of the stimuli that may be present at a given moment? We recently showed that when more than one stimulus is present, single neurons can fluctuate between coding one vs. the other(s) across some time period, suggesting a form of neural multiplexing of different stimuli (Caruso et al., 2018). Here, we investigate (a) whether such coding fluctuations occur in early visual cortical areas; (b) how coding fluctuations are coordinated across the neural population; and (c) how coordinated coding fluctuations depend on the parsing of stimuli into separate vs. fused objects. We found coding fluctuations do occur in macaque V1 but only when the two stimuli form separate objects. Such separate objects evoked a novel pattern of V1 spike count (‘noise’) correlations involving distinct distributions of positive and negative values. This bimodal correlation pattern was most pronounced among pairs of neurons showing the strongest evidence for coding fluctuations or multiplexing. Whether a given pair of neurons exhibited positive or negative correlations depended on whether the two neurons both responded better to the same object or had different object preferences. Distinct distributions of spike count correlations based on stimulus preferences were also seen in V4 for separate objects but not when two stimuli fused to form one object. These findings suggest multiple objects evoke different response dynamics than those evoked by single stimuli, lending support to the multiplexing hypothesis and suggesting a means by which information about multiple objects can be preserved despite the apparent coarseness of sensory coding.

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Section: Discussionmentioning

confidence: 83%

Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Coordinated multiplexing of information about separate objects in visual cortex

Jun

Ruff

Kramer

et al. 2022

eLife

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show abstract

“…Importantly, these reports have concerned responses pooled across trials. Trial-wise spike count distribution models such as those used here and/or faster sub-trial analyses such as those we have introduced in previous work (Caruso et al, 2018;Glynn et al, 2021) might indicate that such apparently averaging responses actually indicate fluctuations occurring on either the stimulus presentation or sub-stimulus presentation time scales, and not a true stable average (e.g.…”

Section: Discussionmentioning

confidence: 93%

Coordinated multiplexing of information about separate objects in visual cortex

Jun

Ruff

Kramer

et al. 2019

Preprint

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How the brain preserves information about more than one stimulus at a time remains poorly understood. We recently showed that when more than one stimulus is present, single neurons may fluctuate between coding one vs. the other(s) across some time period. A critical unanswered question is whether and how any such fluctuations are coordinated across the neural population. Here, we analyzed the spike count ("noise") correlations observed between pairs of primary visual cortex (V1) neurons under a variety of conditions. We report that when two separate grating stimuli are presented simultaneously, distinct distributions of positive and negative correlations emerge, depending on whether the two neurons in the pair both respond more strongly to the same vs. different individual stimuli. Neural pairs that shared the same stimulus preference were more likely to show positively correlated spike count variability whereas those with different preferences were more likely to show negative correlations, suggesting that the population response to one particular stimulus may be enhanced over the other on any given trial. This pattern of results was not present when the two gratings were superimposed and formed a single plaid, supporting the interpretation that the pattern of correlated fluctuations is related to the segregation of individual objects in the visual scene.2

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“…Importantly, these results refute the primary hypothesis we set out to test: the notion that the changes in frequency response functions could be used to overcome the multiplicity problem and suggest that alternative coding possibilities should be explored. In particular, we recently proposed a novel theory of neural representations in which response functions could stay unchanged during presentation of multiple stimuli, but neurons might instead alternate between encoding one stimulus and the other, allowing both stimuli to be represented successfully across time (Caruso et al, 2018;Glynn et al, 2020;Mohl et al, 2020). We developed novel statistical methods to test this possibility and found evidence in support of it in both IC neurons and neurons in a visual cortical area.…”

Section: Discussionmentioning

confidence: 99%

Multiple sounds degrade the frequency representation in monkey inferior colliculus

Willett

2021

Eur J of Neuroscience

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How we distinguish multiple simultaneous stimuli is uncertain, particularly given that such stimuli sometimes recruit largely overlapping populations of neurons. One commonly proposed hypothesis is that the sharpness of tuning curves might change to limit the number of stimuli driving any given neuron when multiple stimuli are present. To test this hypothesis, we recorded the activity of neurons in the inferior colliculus while monkeys made saccades to either one or two simultaneous sounds differing in frequency and spatial location. Although monkeys easily distinguished simultaneous sounds ($90% correct performance), the frequency selectivity of inferior colliculus neurons on dual-sound trials did not improve in any obvious way. Frequency selectivity was degraded on dual-sound trials compared to single-sound trials: neural response functions broadened and frequency accounted for less of the variance in firing rate. These changes in neural firing led a maximum-likelihood decoder to perform worse on dual-sound trials than on single-sound trials. These results fail to support the hypothesis that changes in frequency response functions serve to reduce the overlap in the representation of simultaneous sounds. Instead, these results suggest that alternative possibilities, such as recent evidence of alternations in firing rate between the rates corresponding to each of the two stimuli, offer a more promising approach.

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Analyzing second order stochasticity of neural spiking under stimuli-bundle exposure

Cited by 7 publications

References 19 publications

Coordinated multiplexing of information about separate objects in visual cortex

Coordinated multiplexing of information about separate objects in visual cortex

Coordinated multiplexing of information about separate objects in visual cortex

Multiple sounds degrade the frequency representation in monkey inferior colliculus

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