Internal signal correlates neural populations and biases perceptual decision reports

Carnevale, Federico; Lafuente, Víctor de; Parga, Néstor

doi:10.1073/pnas.1216799109

Cited by 36 publications

(35 citation statements)

References 35 publications

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“…Such progression may reflect a general property of cortical circuits regardless of sensory modality, because similar findings have also been reported in the auditory system, at least for the first relay stations (17). Additionally, the information encoded within early areas does not entirely determine behavioral output in the detection task; on the contrary, the perceptual decision about whether a stimulus is present or absent seems to build up across brain areas (6,12,24).…”

Section: Discussionmentioning

confidence: 77%

Transformation of the neural code for tactile detection from thalamus to cortex

Vázquez

Salinas

Romo

2013

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

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To understand how sensory-driven neural activity gives rise to perception, it is essential to characterize how various relay stations in the brain encode stimulus presence. Neurons in the ventral posterior lateral (VPL) nucleus of the somatosensory thalamus and in primary somatosensory cortex (S1) respond to vibrotactile stimulation with relatively slow modulations (∼100 ms) of their firing rate. In addition, faster modulations (∼10 ms) time-locked to the stimulus waveform are observed in both areas, but their contribution to stimulus detection is unknown. Furthermore, it is unclear whether VPL and S1 neurons encode stimulus presence with similar accuracy and via the same response features. To address these questions, we recorded single neurons while trained monkeys judged the presence or absence of a vibrotactile stimulus of variable amplitude, and their activity was analyzed with a unique decoding method that is sensitive to the time scale of the firing rate fluctuations. We found that the maximum detection accuracy of single neurons is similar in VPL and S1. However, VPL relies more heavily on fast rate modulations than S1, and as a consequence, the neural code in S1 is more tolerant: its performance degrades less when the readout method or the time scale of integration is suboptimal. Therefore, S1 neurons implement a more robust code, one less sensitive to the temporal integration window used to infer stimulus presence downstream. The differences between VPL and S1 responses signaling the appearance of a stimulus suggest a transformation of the neural code from thalamus to cortex. (1-4), and as the signal is relayed from one layer to the next, its representation in spatiotemporal patterns of activity may change (1). Previous studies have shown that, when a tactile vibratory stimulus is presented, neurons in the ventral posterior lateral (VPL) nucleus in the somatosensory thalamus and primary somatosensory cortex (S1, areas 3b and 1) respond by modulating their firing rates computed over a time scale of hundreds of milliseconds (5-9). Because these slow firing rate modulations occurring within relatively long time windows depend on amplitude and vibration frequency, they encode these key stimulus features. In addition, to varying degrees, evoked spikes in both areas also tend to be synchronized to the waveform of the applied mechanical stimulus, which, for frequencies <40 Hz, varies on a time scale of tens of milliseconds. This entrainment, which corresponds to fast firing rate modulations, also depends on stimulus amplitude (9), and so it too carries information about stimulus presence. However, it is unknown whether both the slow (∼100 ms) and fast (∼10 ms) modulations in firing rate contribute to perceptual performance during stimulus detection. Moreover, it is not clear whether the two time scales are equally important in VPL and S1, nor whether the capacity to signal stimulus presence changes from one area to the next.To investigate these issues, we recorded activity from single neurons in the VPL nucle...

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Section: Discussionmentioning

confidence: 77%

Transformation of the neural code for tactile detection from thalamus to cortex

Vázquez

Salinas

Romo

2013

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

Self Cite

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“…Comparable ''internal'' [42] or ''bias'' [43] signals have been observed in mammalian neocortical areas [44] and are thought to reflect choice target values based on local reward history [42,43,45]. The internal signals observed in the latter studies were all potentially motor and/or value related.…”

Section: Internal Signalmentioning

confidence: 90%

Cross-Modal Associative Mnemonic Signals in Crow Endbrain Neurons

Moll

Nieder

2015

Current Biology

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The ability to associate stimuli across time and sensory modalities endows animals and humans with many of the complex, learned behaviors. For successful performance, associations need to be retrieved from long-term memory and maintained active in working memory. We investigated how this is accomplished in the avian brain. We trained carrion crows (Corvus corone) to perform a bimodal delayed paired associate task in which the crows had to match auditory stimuli to delayed visual items. Single-unit recordings from the association area nidopallium caudolaterale (NCL) revealed sustained memory signals that selectively correlated with the learned audio-visual associations across time and modality, and sustained activity prospectively encoded the crows' choices. NCL neurons carried an internal, stimulus-independent signal that was predictive of error and type of error. These results underscore the role of corvid NCL in synthesizing external multisensory information and internal mnemonic data needed for executive control of behavior.

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“…The effect of the trial-to-trial variability in the trial duration on the DA activity was not considered in the previous work (27). However, it is known to have important consequences over prefrontal neurons (29,31) and it is reasonable to believe that it will also affect the midbrain DA system. In fact, effects of temporal variability on DA neurons have been reported several times in tasks without stimulus uncertainty (32)(33)(34) or with it (25).…”

mentioning

confidence: 99%

Dopamine reward prediction error signal codes the temporal evaluation of a perceptual decision report

Sarno

Lafuente

Parga

2017

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

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Learning to associate unambiguous sensory cues with rewarded choices is known to be mediated by dopamine (DA) neurons. However, little is known about how these neurons behave when choices rely on uncertain reward-predicting stimuli. To study this issue we reanalyzed DA recordings from monkeys engaged in the detection of weak tactile stimuli delivered at random times and formulated a reinforcement learning model based on belief states. Specifically, we investigated how the firing activity of DA neurons should behave if they were coding the error in the prediction of the total future reward when animals made decisions relying on uncertain sensory and temporal information. Our results show that the same signal that codes for reward prediction errors also codes the animal's certainty about the presence of the stimulus and the temporal expectation of sensory cues.dopamine activity | perception | temporal expectation | decision making | reinforcement learning W hen an inexperienced animal hears a soft rustle in the nearby foliage, it does not associate this cue with the escaping prey that it observes immediately after. How does the animal get to learn that the correct action to take is to approach it and try to get it? In perceptual decision-making experiments, animals learn how to make decisions based on their perception of weak sensory stimuli, receiving a reward for their correct choices, which they are taught to communicate by means of a specific motor action (1-7). The learning of these tasks is presumably mediated by the activity of midbrain dopamine (DA) neurons (8). Although DA recordings made while animals are engaged in making such difficult decisions are scarce, experiments on Pavlovian and instrumental conditioning have shown that under a novel stimulus-reward association, DA neurons respond to the unexpected reward with an activity burst. Remarkably, after training this phasic response is shifted to the conditioned stimulus where it works as a signal predicting the future reward (8-12). From a computational standpoint, reinforcement learning (RL) methods (13) have been successfully applied to explain this and many other observations (ref. 14 and for reviews see refs. 15-17). According to the reward prediction error (RPE) hypothesis (18,19), the DA phasic activity signals an error in the prediction of the expected total reward (20)(21)(22) and it is used to learn associations between rewards and task events.In classical and instrumental conditioning the reward acts as a reinforcement, strengthening the association with the stimulus, provided the animal follows the task instructions. In some experiments the reward was delivered only after the animal made a choice between alternative options (20,23,24). However, in those studies the task events were unambiguous: The animals' reports were mostly correct and there was a well-defined temporal relationship between the perceived stimulus and reward delivery. However, this is very different from the real-world situation described above in which the reward is annou...

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Internal signal correlates neural populations and biases perceptual decision reports

Cited by 36 publications

References 35 publications

Transformation of the neural code for tactile detection from thalamus to cortex

Transformation of the neural code for tactile detection from thalamus to cortex

Cross-Modal Associative Mnemonic Signals in Crow Endbrain Neurons

Dopamine reward prediction error signal codes the temporal evaluation of a perceptual decision report

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