scholarly journals Recurrent dynamics of prefrontal cortex during context-dependent decision-making

2020 ◽  
Author(s):  
Zach Cohen ◽  
Brian DePasquale ◽  
Mikio C. Aoi ◽  
Jonathan W. Pillow
Keyword(s):  
Decision Making ◽  
Prefrontal Cortex ◽  
Irrelevant Stimulus ◽  
Neural Responses ◽  
One Dimensional ◽  
Systems Neuroscience ◽  
Neural Populations ◽  
Context Dependent ◽  
Monkey Prefrontal Cortex ◽  
Sensory Noise

AbstractA key problem in systems neuroscience is to understand how neural populations integrate relevant sensory inputs during decision-making. Here, we address this problem by training a structured recurrent neural network to reproduce both psychophysical behavior and neural responses recorded from monkey prefrontal cortex during a context-dependent per-ceptual decision-making task. Our approach yields a one-to-one mapping of model neurons to recorded neurons, and explicitly incorporates sensory noise governing the animal’s performance as a function of stimulus strength. We then analyze the dynamics of the resulting model in order to understand how the network computes context-dependent decisions. We find that network dynamics preserve both relevant and irrelevant stimulus information, and exhibit a grid of fixed points for different stimulus conditions as opposed to a one-dimensional line attractor. Our work provides new insights into context-dependent decision-making and offers a powerful framework for linking cognitive function with neural activity within an artificial model.

scholarly journals A Geometric Framework for Understanding Dynamic Information Integration in Context-Dependent Computation

2021 ◽  
Author(s):  
Xiaohan Zhang ◽  
Shenquan Liu ◽  
Zhe Sage Chen
Keyword(s):  
Decision Making ◽  
Prefrontal Cortex ◽  
Information Integration ◽  
Principal Component ◽  
Population Coding ◽  
Geometric Framework ◽  
Context Dependent ◽  
Activity State ◽  
Low Dimensional ◽  
Context Cues

AbstractPrefrontal cortex plays a prominent role in performing flexible cognitive functions and working memory, yet the underlying computational principle remains poorly understood. Here we trained a rate-based recurrent neural network (RNN) to explore how the context rules are encoded, maintained across seconds-long mnemonic delay, and subsequently used in a context-dependent decision-making task. The trained networks emerged key experimentally observed features in the prefrontal cortex (PFC) of rodent and monkey experiments, such as mixed-selectivity, sparse representations, neuronal sequential activity and rotation dynamics. To uncover the high-dimensional neural dynamical system, we further proposed a geometric framework to quantify and visualize population coding and sensory integration in a temporally-defined manner. We employed dynamic epoch-wise principal component analysis (PCA) to define multiple task-specific subspaces and task-related axes, and computed the angles between task-related axes and these subspaces. In low-dimensional neural representations, the trained RNN first encoded the context cues in a cue-specific subspace, and then maintained the cue information with a stable low-activity state persisting during the delay epoch, and further formed line attractors for sensor integration through low-dimensional neural trajectories to guide decision making. We demonstrated via intensive computer simulations that the geometric manifolds encoding the context information were robust to varying degrees of weight perturbation in both space and time. Overall, our analysis framework provides clear geometric interpretations and quantification of information coding, maintenance and integration, yielding new insight into the computational mechanisms of context-dependent computation.

Stimulus context dependent responses of monkey prefrontal cortex neurons

1985 ◽  
Vol 3 ◽  
pp. S10
Author(s):  
Taketoshi Ono ◽  
Kazumasa Yamatani ◽  
Hitoo Nishino ◽  
Masaji Fukuda ◽  
Hisao Nishijo
Keyword(s):  
Prefrontal Cortex ◽  
Stimulus Context ◽  
Context Dependent ◽  
Monkey Prefrontal Cortex

scholarly journals Distinguishing neural correlates of context-dependent advantageous- and disadvantageous-inequity aversion

2018 ◽  
Vol 115 (33) ◽  
pp. E7680-E7689 ◽  
Author(s):  
Xiaoxue Gao ◽  
Hongbo Yu ◽  
Ignacio Sáez ◽  
Philip R. Blue ◽  
Lusha Zhu ◽  
...  
Keyword(s):  
Decision Making ◽  
Prefrontal Cortex ◽  
Computational Models ◽  
Social Contexts ◽  
Inequity Aversion ◽  
Context Dependency ◽  
Anterior Cingulate ◽  
Dorsal Anterior Cingulate Cortex ◽  
Decision Making Processes ◽  
Context Dependent

Humans can integrate social contextual information into decision-making processes to adjust their responses toward inequity. This context dependency emerges when individuals receive more (i.e., advantageous inequity) or less (i.e., disadvantageous inequity) than others. However, it is not clear whether context-dependent processing of advantageous and disadvantageous inequity involves differential neurocognitive mechanisms. Here, we used fMRI to address this question by combining an interactive game that modulates social contexts (e.g., interpersonal guilt) with computational models that enable us to characterize individual weights on inequity aversion. In each round, the participant played a dot estimation task with an anonymous coplayer. The coplayer would receive pain stimulation with 50% probability when either of them responded incorrectly. At the end of each round, the participant completed a variant of dictator game, which determined payoffs for him/herself and the coplayer. Computational modeling demonstrated the context dependency of inequity aversion: when causing pain to the coplayer (i.e., guilt context), participants cared more about the advantageous inequity and became more tolerant of the disadvantageous inequity, compared with other conditions. Consistently, neuroimaging results suggested the two types of inequity were associated with differential neurocognitive substrates. While the context-dependent processing of advantageous inequity was associated with social- and mentalizing-related processes, involving left anterior insula, right dorsolateral prefrontal cortex, and dorsomedial prefrontal cortex, the context-dependent processing of disadvantageous inequity was primarily associated with emotion- and conflict-related processes, involving left posterior insula, right amygdala, and dorsal anterior cingulate cortex. These results extend our understanding of decision-making processes related to inequity aversion.

scholarly journals Spatial gradient in activity within the insula reflects dissociable neural mechanisms underlying context-dependent advantageous and disadvantageous inequity aversion

2018 ◽  
Author(s):  
Xiaoxue Gao ◽  
Hongbo Yu ◽  
Ignacio Saez ◽  
Philip R. Blue ◽  
Lusha Zhu ◽  
...  
Keyword(s):  
Decision Making ◽  
Prefrontal Cortex ◽  
Inequity Aversion ◽  
Neural Mechanisms ◽  
Anterior Cingulate ◽  
Spatial Gradient ◽  
Model Free ◽  
Interpersonal Guilt ◽  
Decision Making Processes ◽  
Context Dependent

AbstractHumans are capable of integrating social contextual information into decision-making processes to adjust their attitudes towards inequity. This context-dependency emerges both when individual is better off (i.e. advantageous inequity) and worse off (i.e. disadvantageous inequity) than others. It is not clear however, whether the context-dependent processing of advantageous and disadvantageous inequity rely on dissociable or shared neural mechanisms. Here, by combining an interpersonal interactive game that gave rise to interpersonal guilt and different versions of the dictator games that enabled us to characterize individual weights on aversion to advantageous and disadvantageous inequity, we investigated the neural mechanisms underlying the two forms of inequity aversion in the interpersonal guilt context. In each round, participants played a dot-estimation task with an anonymous co-player. The co-players received pain stimulation with 50% probability when anyone responded incorrectly. At the end of each round, participants completed a dictator game, which determined payoffs of him/herself and the co-player. Both computational model-based and model-free analyses demonstrated that when inflicting pain upon co-players (i.e., the guilt context), participants cared more about advantageous inequity and became less sensitive to disadvantageous inequity, compared with other social contexts. The contextual effects on two forms of inequity aversion are uncorrelated with each other at the behavioral level. Neuroimaging results revealed that the context-dependent representation of inequity aversion exhibited a spatial gradient in activity within the insula, with anterior parts predominantly involved in the aversion to advantageous inequity and posterior parts predominantly involved in the aversion to disadvantageous inequity. The dissociable mechanisms underlying the two forms of inequity aversion are further supported by the involvement of right dorsolateral prefrontal cortex and dorsomedial prefrontal cortex in advantageous inequity processing, and the involvement of right amygdala and dorsal anterior cingulate cortex in disadvantageous inequity processing. These results extended our understanding of decision-making processes involving inequity and the social functions of inequity aversion.

scholarly journals Visual Decisions in the Presence of Measurement and Stimulus Correlations

2015 ◽  
Vol 27 (11) ◽  
pp. 2318-2353 ◽  
Author(s):  
Manisha Bhardwaj ◽  
Samuel Carroll ◽  
Wei Ji Ma ◽  
Krešimir Josić
Keyword(s):  
Decision Making ◽  
Sensory Input ◽  
Measurement Noise ◽  
Strong Impact ◽  
Strongly Correlated ◽  
Neural Responses ◽  
Multiple Targets ◽  
Statistical Structure ◽  
Perceptual Judgments ◽  
Sensory Noise

Humans and other animals base their decisions on noisy sensory input. Much work has been devoted to understanding the computations that underlie such decisions. The problem has been studied in a variety of tasks and with stimuli of differing complexity. However, how the statistical structure of stimuli, along with perceptual measurement noise, affects perceptual judgments is not well understood. Here we examine how correlations between the components of a stimulus—stimulus correlations—together with correlations in sensory noise, affect decision making. As an example, we consider the task of detecting the presence of a single or multiple targets among distractors. We assume that both the distractors and the observer’s measurements of the stimuli are correlated. The computations of an optimal observer in this task are nontrivial yet can be analyzed and understood intuitively. We find that when distractors are strongly correlated, measurement correlations can have a strong impact on performance. When distractor correlations are weak, measurement correlations have little impact unless the number of stimuli is large. Correlations in neural responses to structured stimuli can therefore have a strong impact on perceptual judgments.

Stimulus context dependent responses of monkey prefrontal cortex neurons

1985 ◽  
Vol 1 ◽  
pp. S10
Author(s):  
Taketoshi Ono ◽  
Kazumasa Yamatani ◽  
Hitoo Nishino ◽  
Masaji Fukuda ◽  
Hisao Nishijo
Keyword(s):  
Prefrontal Cortex ◽  
Stimulus Context ◽  
Context Dependent ◽  
Monkey Prefrontal Cortex

scholarly journals Spatial and temporal structure of choice representations in primate prefrontal cortex

2019 ◽  
Author(s):  
Sepp Kollmorgen ◽  
William T Newsome ◽  
Valerio Mante
Keyword(s):  
Decision Making ◽  
Prefrontal Cortex ◽  
Spatial Organization ◽  
Temporal Structure ◽  
Cortical Surface ◽  
Neural Populations ◽  
Before And After ◽  
Underlying Mechanisms ◽  
Random Connectivity ◽  
Response Field

Divergent accounts of how choices are represented by neural populations have led to conflicting explanations of the underlying mechanisms of decision-making, ranging from persistent, attractor-based dynamics to transient, sequence-based dynamics. To evaluate these mechanisms, we characterize the spatial and temporal structure of choice representations in large neural populations in prefrontal cortex. We find that the pronounced diversity of choice responses across neurons reflects only a few, mostly persistent population patterns recruited at progressively later times before and after a choice. Brief sequential activity occurs during a saccadic choice, but is entirely absent in a delay preceding it. The diversity of choice responses, which could result from almost-random connectivity in the underlying circuits, instead largely reflects the topographical arrangement of response-field properties across the cortical surface. This spatial organization appears to form a fixed scaffold upon which the context-dependent representations of task-specific variables often observed in prefrontal cortex can be learned.

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