The role of the LGN on the spatial frequency dependence of surround suppression in V1: Investigations using a computational model

AbstractIn the context of flexible and adaptive animal behavior, the orbitofrontal cortex (OFC) is found to be one of the crucial regions in the prefrontal cortex (PFC) influencing the downstream processes of decision-making and learning in the sub-cortical regions. Although OFC has been implicated to be important in a variety of related behavioral processes, the exact mechanisms are unclear, through which the OFC encodes or processes information related to decision-making and learning. Here, we propose a systems-level view of the OFC, positioning it at the nexus of sub-cortical systems and other prefrontal regions. Particularly we focus on one of the most recent implications of neuroscientific evidences regarding the OFC - possible functional dissociation between two of its sub-regions : lateral and medial. We present a system-level computational model of decision-making and learning involving the two sub-regions taking into account their individual roles as commonly implicated in neuroscientific studies. We emphasize on the role of the interactions between the sub-regions within the OFC as well as the role of other sub-cortical structures which form a network with them. We leverage well-known computational architecture of thalamo-cortical basal ganglia loops, accounting for recent experimental findings on monkeys with lateral and medial OFC lesions, performing a 3-arm bandit task. First we replicate the seemingly dissociate effects of lesions to lateral and medial OFC during decision-making as a function of value-difference of the presented options. Further we demonstrate and argue that such an effect is not necessarily due to the dissociate roles of both the subregions, but rather a result of complex temporal dynamics between the interacting networks in which they are involved.Author summaryWe first highlight the role of the Orbitofrontal Cortex (OFC) in value-based decision making and goal-directed behavior in primates. We establish the position of OFC at the intersection of cortical mechanisms and thalamo-basal ganglial circuits. In order to understand possible mechanisms through which the OFC exerts emotional control over behavior, among several other possibilities, we consider the case of dissociate roles of two of its topographical subregions - lateral and medial parts of OFC. We gather predominant roles of each of these sub-regions as suggested by numerous experimental evidences in the form of a system-level computational model that is based on existing neuronal architectures. We argue that besides possible dissociation, there could be possible interaction of these sub-regions within themselves and through other sub-cortical structures, in distinct mechanisms of choice and learning. The computational framework described accounts for experimental data and can be extended to more comprehensive detail of representations required to understand the processes of decision-making, learning and the role of OFC and subsequently the regions of prefrontal cortex in general.

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Examining the role of red background in magnocellular contribution to face perception

10.7287/peerj.preprints.1295v1 ◽

2015 ◽

Cited By ~ 1

Author(s):

Bhuvanesh Awasthi ◽

Mark A Williams ◽

Jason Friedman

Keyword(s):

Spatial Frequency ◽

Face Perception ◽

Red Light ◽

Specific Property ◽

High Spatial Frequency ◽

Arm Movement ◽

Frequency Component ◽

Behavioral Evidence ◽

Movement Curvature

This study examines the role of the magnocellular system in the early stages of face perception, in particular sex categorization. Utilizing the specific property of magnocellular suppression in red light, we investigated visually guided reaching to low and high spatial frequency hybrid faces against red and grey backgrounds. The arm movement curvature measure shows that reduced response of the magnocellular pathway interferes with the low spatial frequency component of face perception. This is the first definitive behavioral evidence for magnocellular contribution to face perception.

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Using Speakers' Referential Intentions to Model Early Cross-Situational Word Learning

Psychological Science ◽

10.1111/j.1467-9280.2009.02335.x ◽

2009 ◽

Vol 20 (5) ◽

pp. 578-585 ◽

Cited By ~ 120

Author(s):

Michael C. Frank ◽

Noah D. Goodman ◽

Joshua B. Tenenbaum

Keyword(s):

Computational Model ◽

Word Learning ◽

Word Meaning ◽

Mutual Exclusivity ◽

Object Individuation ◽

Word Meanings ◽

Inference Problems ◽

Corpus Data ◽

Situational Learning

Word learning is a “chicken and egg” problem. If a child could understand speakers' utterances, it would be easy to learn the meanings of individual words, and once a child knows what many words mean, it is easy to infer speakers' intended meanings. To the beginning learner, however, both individual word meanings and speakers' intentions are unknown. We describe a computational model of word learning that solves these two inference problems in parallel, rather than relying exclusively on either the inferred meanings of utterances or cross-situational word-meaning associations. We tested our model using annotated corpus data and found that it inferred pairings between words and object concepts with higher precision than comparison models. Moreover, as the result of making probabilistic inferences about speakers' intentions, our model explains a variety of behavioral phenomena described in the word-learning literature. These phenomena include mutual exclusivity, one-trial learning, cross-situational learning, the role of words in object individuation, and the use of inferred intentions to disambiguate reference.

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The role of cell death during neocortical neurogenesis and synaptogenesis: implications from a computational model for the rat and mouse

Developmental Brain Research ◽

10.1016/j.devbrainres.2004.03.020 ◽

2004 ◽

Vol 151 (1-2) ◽

pp. 43-54 ◽

Cited By ~ 22

Author(s):

Julia M. Gohlke ◽

William C. Griffith ◽

Elaine M. Faustman

Keyword(s):

Cell Death ◽

Computational Model

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A computational model integrating brain electrophysiology and metabolism highlights the key role of extracellular potassium and oxygen

Journal of Theoretical Biology ◽

10.1016/j.jtbi.2018.02.029 ◽

2018 ◽

Vol 446 ◽

pp. 238-258 ◽

Cited By ~ 8

Author(s):

D. Calvetti ◽

G. Capo Rangel ◽

L. Gerardo Giorda ◽

E. Somersalo

Keyword(s):

Computational Model ◽

Extracellular Potassium ◽

Brain Electrophysiology

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Designing a Computational Model of Learning

Machine Learning ◽

10.4018/978-1-60960-818-7.ch203 ◽

2012 ◽

pp. 147-174

Author(s):

David Gibson

Keyword(s):

Artificial Intelligence ◽

Computational Model ◽

Cognitive Models ◽

Design Framework ◽

Assessment Framework ◽

Design Considerations ◽

Learner Assessment ◽

How People Learn ◽

Evolutionary Perspectives

What would a game or simulation need to have in order to teach a teacher how people learn? This chapter uses a four-part framework of knowledge, learner, assessment, and community (Bransford et al., 2000) to discuss design considerations for building a computational model of learning. A teaching simulation—simSchool—helps illustrate selected psychological, physical, and cognitive models and how intelligence can be represented in software agents. The design discussion includes evolutionary perspectives on artificial intelligence and the role of the conceptual assessment framework (Mislevy et al., 2003) for automating feedback to the simulation user. The purpose of the chapter is to integrate a number of theories into a design framework for a computational model of learning.

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