Dynamic Control of Response Criterion in Premotor Cortex during Perceptual Detection under Temporal Uncertainty

Carnevale, Federico; Lafuente, Víctor de; Romo, Ranulfo; Barak, Omri; Parga, Néstor

doi:10.1016/j.neuron.2015.04.014

Cited by 128 publications

(140 citation statements)

References 42 publications

Supporting

Mentioning

134

Contrasting

Order By: Relevance

“…S4 B and C. A similar effect was reported for false alarms during a somatosensory detection task (18). To further quantify this phenomenon, we constructed a response template (18) for each class-selective type, based on hits only and time bins in which each participating neuron coded the preferred class for at least 400 ms (eight time bins of 200 ms displaced every 50 ms, n = 282; ref. 18).…”

Section: S1mentioning

confidence: 62%

“…To further quantify this phenomenon, we constructed a response template (18) for each class-selective type, based on hits only and time bins in which each participating neuron coded the preferred class for at least 400 ms (eight time bins of 200 ms displaced every 50 ms, n = 282; ref. 18). The resulting template represents the typical response profile seen over time during correct discriminations.…”

Section: S1mentioning

confidence: 99%

See 1 more Smart Citation

Emergence of an abstract categorical code enabling the discrimination of temporally structured tactile stimuli

Rossi-Pool

Salinas

Zainos

et al. 2016

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

Self Cite

120

View full text Add to dashboard Cite

The problem of neural coding in perceptual decision making revolves around two fundamental questions: (i) How are the neural representations of sensory stimuli related to perception, and (ii) what attributes of these neural responses are relevant for downstream networks, and how do they influence decision making? We studied these two questions by recording neurons in primary somatosensory (S1) and dorsal premotor (DPC) cortex while trained monkeys reported whether the temporal pattern structure of two sequential vibrotactile stimuli (of equal mean frequency) was the same or different. We found that S1 neurons coded the temporal patterns in a literal way and only during the stimulation periods and did not reflect the monkeys' decisions. In contrast, DPC neurons coded the stimulus patterns as broader categories and signaled them during the working memory, comparison, and decision periods. These results show that the initial sensory representation is transformed into an intermediate, more abstract categorical code that combines past and present information to ultimately generate a perceptually informed choice.behaving monkeys | somatosensory cortex | dorsal premotor cortex | pattern discrimination | categorical code F rom the most stereotyped behavior of invertebrates to the most elaborate behavior of primates, a central issue in neurobiology is elucidating how sensory information is represented in neural circuits and how it is used to generate actions. In principle, this process can be understood as a chain of three basic neuronal operations. The representation of the physical/chemical attributes of the environment and the execution of motor commands can be regarded as the end points of this chain of neuronal operations. In the middle of this chain is a crucial processing step in which the sensory representations are analyzed and transformed in such a manner that the nervous system is able to choose the adequate motor action.We have investigated this chain of processes by analyzing the neuronal activity of parietal and frontal cortices in trained monkeys performing a vibrotactile frequency discrimination task (VFDT; reviewed in refs. 1-3). In this task, monkeys compared the frequencies of two vibratory stimuli applied sequentially to the skin of one fingertip and then used their free hand to push one of two response buttons to indicate whether the second stimulus frequency (f2) was lower or higher than the first stimulus frequency (f1). The VFDT, although apparently simple, is designed so that it can only be executed when a minimum number of neuronal operations or cognitive steps are performed: coding f1, holding f1 in working memory, comparing f2 with the memory trace of f1, and, finally, executing a motor response to indicate whether f2 > f1 or f2 < f1. Thus, the VFDT allowed us to investigate a wide range of essential neural processes during perceptual decision making (1-3). However, the VFDT poses a fundamental problem: What is the neural code(s) that an observer might use to decide whether f2 > f1 or f2 < f1?...

show abstract

Section: S1mentioning

confidence: 62%

Section: S1mentioning

confidence: 99%

Emergence of an abstract categorical code enabling the discrimination of temporally structured tactile stimuli

Rossi-Pool

Salinas

Zainos

et al. 2016

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

Self Cite

120

View full text Add to dashboard Cite

show abstract

“…According to the model, the possible causes are the following: In some hit trials, particularly those with weak stimulus amplitude, the event detected by the Bayesian module was not the stimulus itself but a noisy fluctuation, similar to what happens in FA trials. In these trials, the effective duration of the delay period depends on the time when the fluctuation occurs, which lies within a 2-s temporal window (31). However, these are only a small fraction of the total number of hit trials and this effect is expected to give a small contribution.…”

Section: Resultsmentioning

confidence: 99%

“…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.

Self Cite

View full text Add to dashboard Cite

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...

show abstract

“…In recent years, there has been renewed interest in modeling complex human behaviors such as memory and motor skills using neural networks (Sussillo et al 2015;Rajan, Harvey, and Tank 2016;Hennequin, Vogels, and Gerstner 2014;Carnevale et al 2015;Laje, Buonomano, and Buonomano 2013). However, training these networks to produce meaningful behavior has proven difficult.…”

Section: Discussionmentioning

confidence: 99%

hebbRNN: A Reward-Modulated Hebbian Learning Rule for Recurrent Neural Networks

Michaels¹,

Scherberger²

2016

JOSS

View full text Add to dashboard Cite

Dynamic Control of Response Criterion in Premotor Cortex during Perceptual Detection under Temporal Uncertainty

Cited by 128 publications

References 42 publications

Emergence of an abstract categorical code enabling the discrimination of temporally structured tactile stimuli

Emergence of an abstract categorical code enabling the discrimination of temporally structured tactile stimuli

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

hebbRNN: A Reward-Modulated Hebbian Learning Rule for Recurrent Neural Networks

Contact Info

Product

Resources

About