Distributed representations of action sequences in anterior cingulate cortex: A recurrent neural network approach
Anterior cingulate cortex (ACC) has been the subject of intense debate over the past 2 decades, but its specific computational function remains controversial. Here we present a simple computational model of ACC that incorporates distributed representations across a network of interconnected processi...
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| Vydané v: | Psychonomic bulletin & review Ročník 25; číslo 1; s. 302 - 321 |
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| Hlavní autori: | , |
| Médium: | Journal Article |
| Jazyk: | English |
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New York
Springer US
01.02.2018
Springer Nature B.V |
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| ISSN: | 1069-9384, 1531-5320, 1531-5320 |
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| Abstract | Anterior cingulate cortex (ACC) has been the subject of intense debate over the past 2 decades, but its specific computational function remains controversial. Here we present a simple computational model of ACC that incorporates distributed representations across a network of interconnected processing units. Based on the proposal that ACC is concerned with the execution of extended, goal-directed action sequences, we trained a recurrent neural network to predict each successive step of several sequences associated with multiple tasks. In keeping with neurophysiological observations from nonhuman animals, the network yields distributed patterns of activity across ACC neurons that track the progression of each sequence, and in keeping with human neuroimaging data, the network produces discrepancy signals when any step of the sequence deviates from the predicted step. These simulations illustrate a novel approach for investigating ACC function. |
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| AbstractList | Anterior cingulate cortex (ACC) has been the subject of intense debate over the past 2 decades, but its specific computational function remains controversial. Here we present a simple computational model of ACC that incorporates distributed representations across a network of interconnected processing units. Based on the proposal that ACC is concerned with the execution of extended, goal-directed action sequences, we trained a recurrent neural network to predict each successive step of several sequences associated with multiple tasks. In keeping with neurophysiological observations from nonhuman animals, the network yields distributed patterns of activity across ACC neurons that track the progression of each sequence, and in keeping with human neuroimaging data, the network produces discrepancy signals when any step of the sequence deviates from the predicted step. These simulations illustrate a novel approach for investigating ACC function. Anterior cingulate cortex (ACC) has been the subject of intense debate over the past 2 decades, but its specific computational function remains controversial. Here we present a simple computational model of ACC that incorporates distributed representations across a network of interconnected processing units. Based on the proposal that ACC is concerned with the execution of extended, goal-directed action sequences, we trained a recurrent neural network to predict each successive step of several sequences associated with multiple tasks. In keeping with neurophysiological observations from nonhuman animals, the network yields distributed patterns of activity across ACC neurons that track the progression of each sequence, and in keeping with human neuroimaging data, the network produces discrepancy signals when any step of the sequence deviates from the predicted step. These simulations illustrate a novel approach for investigating ACC function.Anterior cingulate cortex (ACC) has been the subject of intense debate over the past 2 decades, but its specific computational function remains controversial. Here we present a simple computational model of ACC that incorporates distributed representations across a network of interconnected processing units. Based on the proposal that ACC is concerned with the execution of extended, goal-directed action sequences, we trained a recurrent neural network to predict each successive step of several sequences associated with multiple tasks. In keeping with neurophysiological observations from nonhuman animals, the network yields distributed patterns of activity across ACC neurons that track the progression of each sequence, and in keeping with human neuroimaging data, the network produces discrepancy signals when any step of the sequence deviates from the predicted step. These simulations illustrate a novel approach for investigating ACC function. |
| Author | Holroyd, Clay B. Shahnazian, Danesh |
| Author_xml | – sequence: 1 givenname: Danesh surname: Shahnazian fullname: Shahnazian, Danesh organization: Department of Psychology, University of Victoria – sequence: 2 givenname: Clay B. surname: Holroyd fullname: Holroyd, Clay B. email: holroyd@uvic.ca organization: Department of Psychology, University of Victoria |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/28444633$$D View this record in MEDLINE/PubMed |
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| SubjectTerms | Animals Behavioral Science and Psychology Cognition & reasoning Cognitive Psychology Decision making Error correction & detection Goals Gyrus Cinguli - physiology Humans Medical imaging Memory Models, Neurological Motivation Neural networks Neural Networks (Computer) Neurons Neurons - physiology Neurosciences Psychology Theoretical Review |
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| Title | Distributed representations of action sequences in anterior cingulate cortex: A recurrent neural network approach |
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