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Distinct relationships of parietal and prefrontal cortices to evidence accumulation.

Hanks TD, Kopec CD, Brunton BW, Duan CA, Erlich JC, Brody CD - Nature (2015)

Bottom Line: Gradual accumulation of evidence is thought to be fundamental for decision-making, and its neural correlates have been found in several brain regions.Classical analyses uncovered correlates of accumulating evidence, similar to previous observations in primates and also similar across the two regions.Our results place important constraints on the circuit logic of brain regions involved in decision-making.

View Article: PubMed Central - PubMed

Affiliation: 1] Princeton Neuroscience Institute, Princeton University, Princeton, New Jersey 08544, USA [2] Department of Molecular Biology, Princeton University, Princeton, New Jersey 08544, USA.

ABSTRACT
Gradual accumulation of evidence is thought to be fundamental for decision-making, and its neural correlates have been found in several brain regions. Here we develop a generalizable method to measure tuning curves that specify the relationship between neural responses and mentally accumulated evidence, and apply it to distinguish the encoding of decision variables in posterior parietal cortex and prefrontal cortex (frontal orienting fields, FOF). We recorded the firing rates of neurons in posterior parietal cortex and FOF from rats performing a perceptual decision-making task. Classical analyses uncovered correlates of accumulating evidence, similar to previous observations in primates and also similar across the two regions. However, tuning curve assays revealed that while the posterior parietal cortex encodes a graded value of the accumulating evidence, the FOF has a more categorical encoding that indicates, throughout the trial, the decision provisionally favoured by the evidence accumulated so far. Contrary to current views, this suggests that premotor activity in the frontal cortex does not have a role in the accumulation process, but instead has a more categorical function, such as transforming accumulated evidence into a discrete choice. To probe causally the role of FOF activity, we optogenetically silenced it during different time points of the trial. Consistent with a role in committing to a categorical choice at the end of the evidence accumulation process, but not consistent with a role during the accumulation itself, a behavioural effect was observed only when FOF silencing occurred at the end of the perceptual stimulus. Our results place important constraints on the circuit logic of brain regions involved in decision-making.

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Computing tuning curves that describe the relationship between neural activity and accumulated evidencea, One trial for an example neuron from PPC. The left side shows the neuron’s firing rate, and the right side shows the behavioral model’s estimate of the evolution of the distribution of a (color represents probability density). Time runs vertically and is aligned to stimulus onset minus neural response lag. ±B correspond to the “sticky” decision-commitment bounds on evidence accumulation. b, Building a map of firing rate versus accumulator value. At a given timepoint (here, t=0.4), we copy the distribution of a (purple box) to a vertical position given by the neuron’s firing rate. c, Continuing with the same timepoint, we add a slice from very recorded trial. This produces the full joint distribution P(r,a / t=0.4), the probability of seeing firing rate r and accumulator value a at time t=0.4. d, The accumulator values are binned, and mean firing rate is computed for each bin to generate a neural tuning curve as a function of a. e, The process is repeated for each timepoint. Each vertical slice corresponds to a tuning curve, with the one from panel (d) shown above the purple arrowhead.
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Figure 2: Computing tuning curves that describe the relationship between neural activity and accumulated evidencea, One trial for an example neuron from PPC. The left side shows the neuron’s firing rate, and the right side shows the behavioral model’s estimate of the evolution of the distribution of a (color represents probability density). Time runs vertically and is aligned to stimulus onset minus neural response lag. ±B correspond to the “sticky” decision-commitment bounds on evidence accumulation. b, Building a map of firing rate versus accumulator value. At a given timepoint (here, t=0.4), we copy the distribution of a (purple box) to a vertical position given by the neuron’s firing rate. c, Continuing with the same timepoint, we add a slice from very recorded trial. This produces the full joint distribution P(r,a / t=0.4), the probability of seeing firing rate r and accumulator value a at time t=0.4. d, The accumulator values are binned, and mean firing rate is computed for each bin to generate a neural tuning curve as a function of a. e, The process is repeated for each timepoint. Each vertical slice corresponds to a tuning curve, with the one from panel (d) shown above the purple arrowhead.

Mentions: We used the behavioral model previously developed for this task10 to obtain trial-by-trial, moment-by-moment estimates of the accumulating evidence, denoted by a(t) (Extended Data Fig. 2, Extended Data Table 1). Together with the simultaneously recorded firing rates r(t), this enabled the estimation of “tuning curves” that specify, for each point in time during the stimulus, how r depends on a (Fig. 2).


Distinct relationships of parietal and prefrontal cortices to evidence accumulation.

Hanks TD, Kopec CD, Brunton BW, Duan CA, Erlich JC, Brody CD - Nature (2015)

Computing tuning curves that describe the relationship between neural activity and accumulated evidencea, One trial for an example neuron from PPC. The left side shows the neuron’s firing rate, and the right side shows the behavioral model’s estimate of the evolution of the distribution of a (color represents probability density). Time runs vertically and is aligned to stimulus onset minus neural response lag. ±B correspond to the “sticky” decision-commitment bounds on evidence accumulation. b, Building a map of firing rate versus accumulator value. At a given timepoint (here, t=0.4), we copy the distribution of a (purple box) to a vertical position given by the neuron’s firing rate. c, Continuing with the same timepoint, we add a slice from very recorded trial. This produces the full joint distribution P(r,a / t=0.4), the probability of seeing firing rate r and accumulator value a at time t=0.4. d, The accumulator values are binned, and mean firing rate is computed for each bin to generate a neural tuning curve as a function of a. e, The process is repeated for each timepoint. Each vertical slice corresponds to a tuning curve, with the one from panel (d) shown above the purple arrowhead.
© Copyright Policy - permissions-link
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC4835184&req=5

Figure 2: Computing tuning curves that describe the relationship between neural activity and accumulated evidencea, One trial for an example neuron from PPC. The left side shows the neuron’s firing rate, and the right side shows the behavioral model’s estimate of the evolution of the distribution of a (color represents probability density). Time runs vertically and is aligned to stimulus onset minus neural response lag. ±B correspond to the “sticky” decision-commitment bounds on evidence accumulation. b, Building a map of firing rate versus accumulator value. At a given timepoint (here, t=0.4), we copy the distribution of a (purple box) to a vertical position given by the neuron’s firing rate. c, Continuing with the same timepoint, we add a slice from very recorded trial. This produces the full joint distribution P(r,a / t=0.4), the probability of seeing firing rate r and accumulator value a at time t=0.4. d, The accumulator values are binned, and mean firing rate is computed for each bin to generate a neural tuning curve as a function of a. e, The process is repeated for each timepoint. Each vertical slice corresponds to a tuning curve, with the one from panel (d) shown above the purple arrowhead.
Mentions: We used the behavioral model previously developed for this task10 to obtain trial-by-trial, moment-by-moment estimates of the accumulating evidence, denoted by a(t) (Extended Data Fig. 2, Extended Data Table 1). Together with the simultaneously recorded firing rates r(t), this enabled the estimation of “tuning curves” that specify, for each point in time during the stimulus, how r depends on a (Fig. 2).

Bottom Line: Gradual accumulation of evidence is thought to be fundamental for decision-making, and its neural correlates have been found in several brain regions.Classical analyses uncovered correlates of accumulating evidence, similar to previous observations in primates and also similar across the two regions.Our results place important constraints on the circuit logic of brain regions involved in decision-making.

View Article: PubMed Central - PubMed

Affiliation: 1] Princeton Neuroscience Institute, Princeton University, Princeton, New Jersey 08544, USA [2] Department of Molecular Biology, Princeton University, Princeton, New Jersey 08544, USA.

ABSTRACT
Gradual accumulation of evidence is thought to be fundamental for decision-making, and its neural correlates have been found in several brain regions. Here we develop a generalizable method to measure tuning curves that specify the relationship between neural responses and mentally accumulated evidence, and apply it to distinguish the encoding of decision variables in posterior parietal cortex and prefrontal cortex (frontal orienting fields, FOF). We recorded the firing rates of neurons in posterior parietal cortex and FOF from rats performing a perceptual decision-making task. Classical analyses uncovered correlates of accumulating evidence, similar to previous observations in primates and also similar across the two regions. However, tuning curve assays revealed that while the posterior parietal cortex encodes a graded value of the accumulating evidence, the FOF has a more categorical encoding that indicates, throughout the trial, the decision provisionally favoured by the evidence accumulated so far. Contrary to current views, this suggests that premotor activity in the frontal cortex does not have a role in the accumulation process, but instead has a more categorical function, such as transforming accumulated evidence into a discrete choice. To probe causally the role of FOF activity, we optogenetically silenced it during different time points of the trial. Consistent with a role in committing to a categorical choice at the end of the evidence accumulation process, but not consistent with a role during the accumulation itself, a behavioural effect was observed only when FOF silencing occurred at the end of the perceptual stimulus. Our results place important constraints on the circuit logic of brain regions involved in decision-making.

Show MeSH
Related in: MedlinePlus