Limited Evidence for Sensory Prediction Error Responses in Visual Cortex of Macaques and Humans

Solomon, Selina S.; Tang, Hua; Sussman, Elyse; Kohn, Adam

doi:10.1093/cercor/bhab014

Cited by 31 publications

(50 citation statements)

References 69 publications

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“…These observations are consistent with findings from human EEG that demonstrate a prominent role of sound intensity level transition in modulating ERP component amplitudes during the time-window of MMN and P3a (Barry et al, 2022;O'Reilly, 2021a). This adds to evidence suggesting that neural responses to oddball stimulation depend on interactions between physiological adaptation and the physical properties of stimuli, which confound any potential role of prediction-error signalling (Fishman and Steinschneider, 2012;Lazar and Metherate, 2003;May, 2021;May and Tiitinen, 2010;O'Reilly, 2021b;O'Reilly and Conway, 2021;Solomon et al, 2021;.…”

Section: Tone Frequency Changes and Rising Sound-level Transitions Tr...supporting

confidence: 89%

Decoding violated sensory expectations from the auditory cortex of anaesthetized mice: Hierarchical recurrent neural network depicts separate ‘danger’ and ‘safety’ units

O’Reilly¹,

Angsuwatanakul²,

Wehrman

2022

Preprint

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The ability to respond appropriately to sensory information received from the external environment is among the most fundamental capabilities of central nervous systems. In the auditory domain, processes underlying this behaviour are studied by measuring auditory-evoked electrophysiology during sequences of sounds with predetermined regularities. Identifying neural correlates of ensuing auditory novelty responses is supported by research in experimental animals. In the present study, we reanalysed epidural field potential recordings from the auditory cortex of anaesthetised mice during frequency and intensity oddball stimulation. Multivariate pattern analysis (MVPA) and hierarchical recurrent neural network (RNN) modelling were adopted to explore these data with greater resolution than previously considered using conventional methods. Time-wise and generalised temporal decoding MVPA approaches revealed previously underestimated asymmetry between responses to sound-level transitions in the intensity oddball paradigm, in contrast with tone frequency changes. After training, the cross-validated RNN model architecture with four hidden layers produced output waveforms in response to simulated auditory inputs that were strongly correlated with grand-average auditory-evoked potential waveforms (r2 > 0.9). Units in hidden layers were classified based on their temporal response properties and characterised using principal component analysis and sample entropy. These demonstrated spontaneous alpha rhythms, sound onset and offset responses, and putative 'safety' and 'danger' units activated by relatively inconspicuous and salient changes in auditory inputs, respectively. The hypothesised existence of corresponding biological neural sources is naturally derived from this model. If proven, this would have significant implications for prevailing theories of auditory processing.

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Section: Tone Frequency Changes and Rising Sound-level Transitions Tr...supporting

confidence: 89%

Decoding violated sensory expectations from the auditory cortex of anaesthetized mice: Hierarchical recurrent neural network depicts separate ‘danger’ and ‘safety’ units

O’Reilly¹,

Angsuwatanakul²,

Wehrman

2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Thus, the temporal context in which the gratings were displayed was irrelevant. The authors therefore concluded “spatial and temporal codes in area V1 operate largely independently.” A more recent study by Solomon et al looked at neural responses in awake macaque and human visual cortex in response to rapidly flashed sequences of sinusoidal gratings ( Solomon et al, 2021 ). They showed standard sequences on 80% of trials and deviant sequences on 20%, expecting to observe prediction errors in response to deviant stimuli.…”

Section: Criticismsmentioning

confidence: 99%

Efficient Temporal Coding in the Early Visual System: Existing Evidence and Future Directions

Price

Gavornik

2022

Front. Comput. Neurosci.

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While it is universally accepted that the brain makes predictions, there is little agreement about how this is accomplished and under which conditions. Accurate prediction requires neural circuits to learn and store spatiotemporal patterns observed in the natural environment, but it is not obvious how such information should be stored, or encoded. Information theory provides a mathematical formalism that can be used to measure the efficiency and utility of different coding schemes for data transfer and storage. This theory shows that codes become efficient when they remove predictable, redundant spatial and temporal information. Efficient coding has been used to understand retinal computations and may also be relevant to understanding more complicated temporal processing in visual cortex. However, the literature on efficient coding in cortex is varied and can be confusing since the same terms are used to mean different things in different experimental and theoretical contexts. In this work, we attempt to provide a clear summary of the theoretical relationship between efficient coding and temporal prediction, and review evidence that efficient coding principles explain computations in the retina. We then apply the same framework to computations occurring in early visuocortical areas, arguing that data from rodents is largely consistent with the predictions of this model. Finally, we review and respond to criticisms of efficient coding and suggest ways that this theory might be used to design future experiments, with particular focus on understanding the extent to which neural circuits make predictions from efficient representations of environmental statistics.

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“…Theories of predictive coding in the neocortex have typically focused on the idea that feedback from higher cortical areas encodes a prediction about lower-level sensory data ( 29 ) that is subtracted from the lower-level representation, so that the signals traveling up the cortical hierarchy represent surprise or novelty ( 30 , 31 ). However, a recent study failed to find these signatures of predictive coding ( 32 ).…”

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confidence: 99%

Novel stimuli evoke excess activity in the mouse primary visual cortex

Homann

Koay

Chen

et al. 2022

Proc. Natl. Acad. Sci. U.S.A.

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To explore how neural circuits represent novel versus familiar inputs, we presented mice with repeated sets of images with novel images sparsely substituted. Using two-photon calcium imaging to record from layer 2/3 neurons in the mouse primary visual cortex, we found that novel images evoked excess activity in the majority of neurons. This novelty response rapidly emerged, arising with a time constant of 2.6 ± 0.9 s. When a new image set was repeatedly presented, a majority of neurons had similarly elevated activity for the first few presentations, which decayed to steady state with a time constant of 1.4 ± 0.4 s. When we increased the number of images in the set, the novelty response’s amplitude decreased, defining a capacity to store ∼15 familiar images under our conditions. These results could be explained quantitatively using an adaptive subunit model in which presynaptic neurons have individual tuning and gain control. This result shows that local neural circuits can create different representations for novel versus familiar inputs using generic, widely available mechanisms.

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Limited Evidence for Sensory Prediction Error Responses in Visual Cortex of Macaques and Humans

Cited by 31 publications

References 69 publications

Decoding violated sensory expectations from the auditory cortex of anaesthetized mice: Hierarchical recurrent neural network depicts separate ‘danger’ and ‘safety’ units

Decoding violated sensory expectations from the auditory cortex of anaesthetized mice: Hierarchical recurrent neural network depicts separate ‘danger’ and ‘safety’ units

Efficient Temporal Coding in the Early Visual System: Existing Evidence and Future Directions

Novel stimuli evoke excess activity in the mouse primary visual cortex

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