Flexible Control of Mutual Inhibition: A Neural Model of Two-Interval Discrimination

• Published in: Science (American Association for the Advancement of Science) Vol. 307; no. 5712; pp. 1121 - 1124
• Main Authors: Machens, Christian K, Romo, Ranulfo, Brody, Carlos D
• Format: Journal Article
• Language: English
• Published: Washington, DC American Association for the Advancement of Science 18.02.2005; The American Association for the Advancement of Science
• Subjects: Algorithms; Analysis; Anatomical correlates of behavior; Animals; Artificial neural networks; Behavioral neuroscience; Behavioral psychophysiology; Biological and medical sciences; Cognition; Cognitive models; Cognitive Processes; Cognitive Psychology; Computer Simulation; Decision Making; Discrimination (Psychology); Discrimination Learning; Frontal lobe; Frontal Lobe - cytology; Frontal Lobe - physiology; Frontal lobes; Fundamental and applied biological sciences. Psychology; Inhibition; Input output; Instantiation; Macaca; Mathematics; Memory; Mental stimulation; Models, Neurological; Nerve Net - physiology; Networks; Neural Inhibition; Neural network; Neural networks; Neural Networks (Computer); Neurology; Neurons; Neurons - physiology; Neurons, Afferent - physiology; Nonlinear Dynamics; Prefrontal Cortex - cytology; Prefrontal Cortex - physiology; Psychology. Psychoanalysis. Psychiatry; Psychology. Psychophysiology; Psychomotor Performance; Resistance (Psychology); Short Term Memory; Somatosensory Cortex - cytology; Somatosensory Cortex - physiology; Stimuli; Systems Approach; Trajectories; Working memory; United States; Mexico; Frontal lobe; Stimulus; Models; Discrimination; Central nervous system; Encephalon
• ISSN: 0036-8075; 1095-9203
• DOI: 10.1126/science.1104171

Networks adapt to environmental demands by switching between distinct dynamical behaviors. The activity of frontal-lobe neurons during two-interval discrimination tasks is an example of these adaptable dynamics. Subjects first perceive a stimulus, then hold it in working memory, and finally make a decision by comparing it with a second stimulus. We present a simple mutual-inhibition network model that captures all three task phases within a single framework. The model integrates both working memory and decision making because its dynamical properties are easily controlled without changing its connectivity. Mutual inhibition between nonlinear units is a useful design motif for networks that must display multiple behaviors.