Heterogeneous value coding in orbitofrontal populations.

We investigated the anatomical and functional organization of the human substantia nigra (SN) using diffusion and functional MRI data from the Human Connectome Project. We identified a tripartite connectivity-based parcellation of SN with a limbic, cognitive, motor arrangement. The medial SN connects with limbic striatal and cortical regions and encodes value (greater response to monetary wins than losses during fMRI), while the ventral SN connects with associative regions of cortex and striatum and encodes salience (equal response to wins and losses). The lateral SN connects with somatomotor regions of striatum and cortex and also encodes salience. Behavioral measures from delay discounting and flanker tasks supported a role for the value-coding medial SN network in decisional impulsivity, while the salience-coding ventral SN network was associated with motor impulsivity. In sum, there is anatomical and functional heterogeneity of human SN, which underpins value versus salience coding, and impulsive choice versus impulsive action.

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A genetic algorithm with real-value coding to optimize multimodal continuous functions

Structural and Multidisciplinary Optimization ◽

10.1007/s00158-001-0166-y ◽

2001 ◽

Vol 23 (1) ◽

pp. 63-74 ◽

Cited By ~ 80

Author(s):

M. Bessaou ◽

P. Siarry

Keyword(s):

Genetic Algorithm ◽

Continuous Functions ◽

Real Value ◽

Value Coding

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Research on the Interaction of Genetic Algorithm in Assisted Composition

Computational Intelligence and Neuroscience ◽

10.1155/2021/3137666 ◽

2021 ◽

Vol 2021 ◽

pp. 1-15

Author(s):

Han Hu

Keyword(s):

Genetic Algorithm ◽

Time Value ◽

Coding Method ◽

Algorithmic Music ◽

Computer Aided ◽

Character Representation ◽

Value Coding ◽

Algorithmic Music Composition ◽

Special Process

Computer-aided composition is an attempt to use a formalized process to minimize human (or composer) involvement in the creation of music using a computer. Exploring the problem of computer-aided composition can enable us to understand and simulate the thinking mode of composers in the special process of music creation, which is an important application of artificial intelligence in the field of art. Feature extraction on the MIDI files has been introduced in this paper. Based on the genetic algorithm in this paper, a platform of the sampling coding method to optimize the character representation has solved the traditional algorithmic music composition study. Music directly from the pitch and duration can be derived from the characteristics, respectively, in the form of a one-hot encoding independently said. Failure to the rhythm of the characterization of the pitch and duration are problems that lead to the inability of compositional networks to learn musical styles better. Rhythm is the combination of pitch and time values according to certain rules. The rhythm of music affects the overall style of music. By associating the pitch and time value coding, the rhythm style of music can be preserved better so that the composition network can learn the style characteristics of music more easily.

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Reward Value Coding Distinct From Risk Attitude-Related Uncertainty Coding in Human Reward Systems

Journal of Neurophysiology ◽

10.1152/jn.00745.2006 ◽

2007 ◽

Vol 97 (2) ◽

pp. 1621-1632 ◽

Cited By ~ 269

Author(s):

Philippe N. Tobler ◽

John P. O'Doherty ◽

Raymond J. Dolan ◽

Wolfram Schultz

Keyword(s):

Risk Aversion ◽

Risk Attitude ◽

Mean Value ◽

Expected Value ◽

Individual Risk ◽

Functional Magnetic Resonance ◽

Coding Regions ◽

Monetary Rewards ◽

Expected Values ◽

Value Coding

When deciding between different options, individuals are guided by the expected (mean) value of the different outcomes and by the associated degrees of uncertainty. We used functional magnetic resonance imaging to identify brain activations coding the key decision parameters of expected value (magnitude and probability) separately from uncertainty (statistical variance) of monetary rewards. Participants discriminated behaviorally between stimuli associated with different expected values and uncertainty. Stimuli associated with higher expected values elicited monotonically increasing activations in distinct regions of the striatum, irrespective of different combinations of magnitude and probability. Stimuli associated with higher uncertainty (variance) elicited increasing activations in the lateral orbitofrontal cortex. Uncertainty-related activations covaried with individual risk aversion in lateral orbitofrontal regions and risk-seeking in more medial areas. Furthermore, activations in expected value-coding regions in prefrontal cortex covaried differentially with uncertainty depending on risk attitudes of individual participants, suggesting that separate prefrontal regions are involved in risk aversion and seeking. These data demonstrate the distinct coding in key reward structures of the two basic and crucial decision parameters, expected value, and uncertainty.

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