Accueil/ expose
Amygdalar mechanisms for innate, learned, and regulated behavior
mardi 07 octobre 2014

Loading the player ...
Descriptif

Conférence de Daniel Salzman dans le cadre du colloquium de l'IEC.

Adaptive emotional behavior requires subjects to generate responses to innately rewarding or aversive stimuli as well as to conditioned stimuli whose relationship to impending reinforcement often changes through learning or regulation. Evidence has accumulated implicating the amygdala as a critical structure in mediating these processes. We pursue a two-pronged approach for elucidating amygdalar mechanisms underlying innate, learned, and regulated emotional behavior. First, we use a genetic strategy to identify representations of innately rewarding and aversive stimuli (unconditioned stimuli, USs) in the basolateral amygdala (BLA) and examine their role in innate and learned responses. Activation of an ensemble of US-responsive cells in the BLA elicits innate physiological and behavioral responses of different valence. Activation of this US ensemble can also reinforce appetitive and aversive learning when paired with differing neutral stimuli. Moreover, activation of US-responsive cells in the BLA is necessary for the expression of a conditioned response. Neural representations of conditioned and unconditioned stimuli must therefore ultimately connect to US-responsive cells in the BLA to elicit both innate and learned responses. Second, we examine the neurophysiological mechanisms that may mediate how representations of emotional significance in the amygdala may be regulated. Subjects perform a task in which reinforcement prediction required identifying a stimulus, knowing the context in which the stimulus appeared, and understanding context-dependent reinforcement contingencies. It is commonly assumed that processing in the prefrontal cortex (PFC) helps confer emotional flexibility on this type of task since PFC neurons encode rules, goals and other abstract information. Surprisingly, we discovered that neurons in the amygdala also represents abstract cognitive information. Disappearance of this abstract representation in the amygdala predicts errors in reward anticipation, a finding not observed for PFC. These data emphasize the potential importance of maintaining abstract cognitive information in the amygdala to support the flexible regulation of emotion.

Voir aussi


  • Aucun exposé du même auteur.
  • Base neurale de la mémoire spatiale : Po...
    Alain Berthoz
  • Interprétations spontanées, inférences p...
    Emmanuel Sander
  • Cognitive, developmental and cultural ba...
    Atsushi Senju
  • The origin of prosociality : a comparati...
    Nicolas Claidière
  • (Dis)organizational principles for neuro...
    Miguel Maravall Rodriguez
  • From speech to language in infancy
    Alejandrina Cristia
  • The Neural Marketplace
    Kenneth Harris
  • Why the Internet won't get you any more ...
    Robin Dunbar
  • Synergies in Language Acquisition
    Mark Johnson
  • The neuroeconomics of simple choice
    Antonio Rangel
  • Phonological Effects on the Acquisition ...
    Katherine Demuth
  • Inner speech in action : EMG data durin...
    Hélène Loevenbruck
  • Use of phonetic detail in word learning
    Paola Escudero
  • What is special about eye contact ?
    Laurence Conty
  • The inference theory of discourse refere...
    Amit Almor
  • Syntactic computations, the cartography ...
    Luigi Rizzi
  • Levels of communication and lexical sema...
    Peter Gärdenfors
  • Explanation and Inference
    Igor Douven
  • Consciousness, Action, PAM !
    Thor Grunbaum
  • Principles of Neural Design
    Peter Sterling
  • Precursors to valuation
    Timothy Behrens
  • Is machine learning a good model of huma...
    Yann LeCun
  • Following and leading social gaze
    Andrew Bayliss
  • It’s the neuron: how the brain really wo...
    Charles Randy Gallistel
  • Biological Information: Genetic, epigene...
    Paul Griffiths
  • From necessity to sufficiency in memory ...
    Karim Benchenane
  • Comparing the difficulty of different ty...
    LouAnn Gerken
  • A big data approach towards functional b...
    Bertrand Thirion
  • Sign language and language emergence
    Marie Coppola
  • The collaborative making of an encyclope...
    Dario Taraborelli
  • The Evolution of Punishment
    Nichola Raihani
  • Metacontrol of reinforcement learning
    Sam Gershman
  • Homo Cyberneticus: Neurocognitive consid...
    Tamar Makin
  • Reverse Engineering Visual Intelligence
    Jim DiCarlo
  • What is listening effort?
    Ingrid Johnsrude
  • Genomic analysis of 1.5 million people r...
    Paige Harden
  • The Language of Life: exploring the orig...
    Catherine Hobaiter
  • Deliberate ignorance: The curious choic...
    Ralph Hertwig
  • The social brain in adolescence
    Sarah-Jayne Blakemore
  • Big data about small people: Studying ch...
    Michael Frank
  • Individual Differences in Lifespan Cogni...
    Stuart Richie
  • Why are humans still smarter than machin...
    James L. (Jay) McClelland
  • Contextual effects, image statistics, an...
    Odelia Schwartz
  • Problem solving in acellular slime mold...
    Audrey Dussutour
  • Redrawing the lines between language an...
    Neil Cohn
  • Choice and value : the biology of decisi...
    Alex Kacelnik
  • What happened to the 'mental' in 'menta...
    Joseph LeDoux
  • Rethinking sex and the brain: Beyond th...
    Daphna Joel
  • How robust are meta-analyses to publicat...
    Maya Mathur
  • How family background affects children’...
    Sophie Von Stumm
Auteur(s)
Daniel Salzman
Columbia University

Plus sur cet auteur
Voir la fiche de l'auteur

Cursus :

Daniel Salzman est chercheur au département de neurosciences et de psychologie à l'Université de Columbia.

Cliquer ICI pour fermer
Annexes
Téléchargements :
   - Télécharger l'audio (mp3)

Dernière mise à jour : 06/02/2015