scholarly journals Midbrain dopaminergic inputs gate amygdala intercalated cell clusters by distinct and cooperative mechanisms

2020 ◽  
Author(s):  
Ayla Aksoy-Aksel ◽  
Andrea Gall ◽  
Anna Seewald ◽  
Francesco Ferraguti ◽  
Ingrid Ehrlich
Keyword(s):  
Associative Learning ◽  
Prediction Error ◽  
D1 Receptors ◽  
Direct Inhibition ◽  
Extinction Learning ◽  
Intercalated Cell ◽  
Midbrain Neurons ◽  
Dopaminergic Mechanisms ◽  
Inhibitory Interactions ◽  
Fear Behavior

AbstractDopaminergic signaling plays an important role in associative learning including fear and extinction learning. Dopaminergic midbrain neurons encode prediction error-like signals when threats differ from expectations. Within the amygdala, GABAergic intercalated cell (ITC) clusters receive the densest dopaminergic projections, but their physiological consequences are incompletely understood. ITCs are important for fear extinction, a function thought to be supported by activation of ventromedial cluster ITCs that inhibit central amygdala fear output. In mice, we reveal two distinct mechanisms how mesencephalic dopaminergic afferents control ITCs. Firstly, they co-release GABA to mediate rapid, direct inhibition. Secondly, dopamine suppresses inhibitory interactions between distinct ITC clusters via presynaptic D1-receptors. Early extinction training augments both, GABA co-release onto dorso-medial ITCs and dopamine-mediated suppression of dorso- to ventromedial inhibition between ITC clusters. These findings provide novel insights into dopaminergic mechanisms shaping the activity balance between distinct ITC clusters that could support their opposing roles in fear behavior.

scholarly journals Midbrain dopaminergic inputs gate amygdala intercalated cell clusters by distinct and cooperative mechanisms in male mice

eLife ◽  
2021 ◽  
Vol 10 ◽  
Author(s):  
Ayla Aksoy-Aksel ◽  
Andrea Gall ◽  
Anna Seewald ◽  
Francesco Ferraguti ◽  
Ingrid Ehrlich
Keyword(s):  
Prediction Error ◽  
D1 Receptors ◽  
Male Mice ◽  
Direct Inhibition ◽  
Extinction Learning ◽  
Intercalated Cell ◽  
Midbrain Neurons ◽  
Dopaminergic Mechanisms ◽  
Inhibitory Interactions ◽  
Fear Behavior

Dopaminergic signaling plays an important role in associative learning, including fear and extinction learning. Dopaminergic midbrain neurons encode prediction error-like signals when threats differ from expectations. Within the amygdala, GABAergic intercalated cell (ITC) clusters receive one of the densest dopaminergic projections, but their physiological consequences are incompletely understood. ITCs are important for fear extinction, a function thought to be supported by activation of ventromedial ITCs that inhibit central amygdala fear output. In mice, we reveal two distinct novel mechanisms by which mesencephalic dopaminergic afferents control ITCs. Firstly, they co-release GABA to mediate rapid, direct inhibition. Secondly, dopamine suppresses inhibitory interactions between distinct ITC clusters via presynaptic D1 receptors. Early extinction training augments both GABA co-release onto dorsomedial ITCs and dopamine-mediated suppression of dorso- to ventromedial inhibition between ITC clusters. These findings provide novel insights into dopaminergic mechanisms shaping the activity balance between distinct ITC clusters that could support their opposing roles in fear behavior.

scholarly journals Bidirectional control of fear memories by cerebellar neurons projecting to the ventrolateral periaqueductal grey

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Jimena Laura Frontera ◽  
Hind Baba Aissa ◽  
Romain William Sala ◽  
Caroline Mailhes-Hamon ◽  
Ioana Antoaneta Georgescu ◽  
...  
Keyword(s):  
Prefrontal Cortex ◽  
Associative Learning ◽  
Prediction Error ◽  
Functional Role ◽  
Extinction Learning ◽  
Brain Areas ◽  
Periaqueductal Grey ◽  
Cerebellar Neurons ◽  
Emotional System

Abstract Fear conditioning is a form of associative learning that is known to involve different brain areas, notably the amygdala, the prefrontal cortex and the periaqueductal grey (PAG). Here, we describe the functional role of pathways that link the cerebellum with the fear network. We found that the cerebellar fastigial nucleus (FN) sends glutamatergic projections to vlPAG that synapse onto glutamatergic and GABAergic vlPAG neurons. Chemogenetic and optogenetic manipulations revealed that the FN-vlPAG pathway controls bi-directionally the strength of the fear memories, indicating an important role in the association of the conditioned and unconditioned stimuli, a function consistent with vlPAG encoding of fear prediction error. Moreover, FN-vlPAG projections also modulate extinction learning. We also found a FN-parafascicular thalamus pathway, which may relay cerebellar influence to the amygdala and modulates anxiety behaviors. Overall, our results reveal multiple contributions of the cerebellum to the emotional system.

scholarly journals Complex dynamics of choice during operant extinction and a “simple” associative learning account

2020 ◽  
Author(s):  
José R. Donoso ◽  
Julian Packheiser ◽  
Roland Pusch ◽  
Zhiyin Lederer ◽  
Thomas Walther ◽  
...  
Keyword(s):  
Computational Model ◽  
Associative Learning ◽  
Operant Conditioning ◽  
Complex Dynamics ◽  
Rule Learning ◽  
Learning Curves ◽  
Learning Mechanisms ◽  
Learning Session ◽  
Extinction Learning ◽  
Operant Extinction

AbstractExtinction learning, the process of ceasing an acquired behavior in response to altered reinforcement contingencies, is essential for survival in a changing environment. So far, research has mostly neglected the learning dynamics and variability of behavior during extinction learning and instead focused on a few response types that were studied by population averages. Here, we take a different approach by analyzing the trial-by-trial dynamics of operant extinction learning in both pigeons and a computational model. The task involved discriminant operant conditioning in context A, extinction in context B, and a return to context A to test the context-dependent return of the conditioned response (ABA renewal). By studying single learning curves across animals under repeated sessions of this paradigm, we uncovered a rich variability of behavior during extinction learning: (1) Pigeons prefer the unrewarded alternative choice in one-third of the sessions, predominantly during the very first extinction session an animal encountered. (2) In later sessions, abrupt transitions of behavior at the onset of context B emerge, and (3) the renewal effect decays as sessions progress. While these results could be interpreted in terms of rule learning mechanisms, we show that they can be parsimoniously accounted for by a computational model based only on associative learning between stimuli and actions. Our work thus demonstrates the critical importance of studying the trial-by-trial dynamics of learning in individual sessions, and the unexpected power of “simple” associative learning processes.Significance StatementOperant conditioning is essential for the discovery of purposeful actions, but once a stimulus-response association is acquired, the ability to extinguish it in response to altered reward contingencies is equally important. These processes also play a fundamental role in the development and treatment of pathological behaviors such as drug addiction, overeating and gambling. Here we show that extinction learning is not limited to the cessation of a previously reinforced response, but also drives the emergence of complex and variable choices that change from learning session to learning session. At first sight, these behavioral changes appear to reflect abstract rule learning, but we show in a computational model that they can emerge from “simple” associative learning.

The Associative Learning Prediction Error Feedback Loop as Reversible Driving Belt of the Bioenergetics of Longevity

2021 ◽  
Author(s):  
Matthew S Price
Keyword(s):  
Associative Learning ◽  
Prediction Error ◽  
Feedback Loop ◽  
Telomerase Activity ◽  
Behavioral Strategy ◽  
Prediction Errors ◽  
Error Feedback ◽  
Treatment Expectations ◽  
Telomere Shortening ◽  
Inhibitory Learning

Leukocyte telomere shortening is a useful biomarker of biological and cellular age that occurs at an accelerated rate in anxiety disorders and posttraumatic stress disorder (PTSD). Intriguingly, inhibitory learning — the systematic exposure to noxious stimuli that serves as a basis for many treatments for anxiety, phobia, and PTSD —reduces relative telomeres attrition rates and increases protective telomerase activity in a manner predictive of treatment response. How does inhibitory learning, a behavioral strategy, modulate organismal chromosomal activity? Inhibitory learning may induce repeated mismatch between treatment expectations, intrasession states, and eventual outcome. Nevertheless, inhibitory learning can incentivize repetition of the behavior. Thus, this paper aims to conceptualize inhibitory learning as involving a ‘prediction error feedback loop’, i.e., a series of self-perpetuating prediction errors — mismatches between expectations and outcomes — that enhances neural inhibitory regulation to effectuate extinction. Inhibitory learning is necessarily predicated upon an opposing process – excitatory learning – that may be conceptualized as a prediction error feedback loop that operates in reverse to inhibitory learning and enhances neural excitability as arousal. Together, excitatory and inhibitory learning may be elements of an associative learning prediction error feedback loop responsible for modulating neural bioenergetic rates, leading to changes in downstream cellular signaling that could explain reduced or increased rates of leukocyte telomere shortening and telomerase activity from each behavioral strategy, respectively.

scholarly journals A Computational Role for Top–Down Modulation from Frontal Cortex in Infancy

2020 ◽  
Vol 32 (3) ◽  
pp. 508-514 ◽  
Author(s):  
Sagi Jaffe-Dax ◽  
Alex M. Boldin ◽  
Nathaniel D. Daw ◽  
Lauren L. Emberson
Keyword(s):  
Associative Learning ◽  
Preterm Infants ◽  
Prediction Error ◽  
Frontal Lobes ◽  
Full Term ◽  
Computational Learning ◽  
Top Down ◽  
Term Infants ◽  
Young Infants ◽  
Sensory Prediction

Recent findings have shown that full-term infants engage in top–down sensory prediction, and these predictions are impaired as a result of premature birth. Here, we use an associative learning model to uncover the neuroanatomical origins and computational nature of this top–down signal. Infants were exposed to a probabilistic audiovisual association. We find that both groups (full term, preterm) have a comparable stimulus-related response in sensory and frontal lobes and track prediction error in their frontal lobes. However, preterm infants differ from their full-term peers in weaker tracking of prediction error in sensory regions. We infer that top–down signals from the frontal lobe to the sensory regions carry information about prediction error. Using computational learning models and comparing neuroimaging results from full-term and preterm infants, we have uncovered the computational content of top–down signals in young infants when they are engaged in a probabilistic associative learning.

scholarly journals Critical evidence for the prediction error theory in associative learning

2015 ◽  
Vol 5 (1) ◽  
Author(s):  
Kanta Terao ◽  
Yukihisa Matsumoto ◽  
Makoto Mizunami
Keyword(s):  
Associative Learning ◽  
Prediction Error ◽  
Error Theory

Dopamine, prediction error and associative learning: A model-based account

2006 ◽  
Vol 17 (1) ◽  
pp. 61-84 ◽  
Author(s):  
Andrew Smith ◽  
Ming Li ◽  
Sue Becker ◽  
Shitij Kapur
Keyword(s):  
Associative Learning ◽  
Prediction Error ◽  
Model Based

scholarly journals L-DOPA modulates activity in the vmPFC, nucleus accumbens, and VTA during threat extinction learning in humans

eLife ◽  
2021 ◽  
Vol 10 ◽  
Author(s):  
Roland Esser ◽  
Christoph W Korn ◽  
Florian Ganzer ◽  
Jan Haaker
Keyword(s):  
Prefrontal Cortex ◽  
Nucleus Accumbens ◽  
Prediction Error ◽  
Computational Modelling ◽  
Ventromedial Prefrontal Cortex ◽  
Adaptive Behaviour ◽  
Extinction Learning ◽  
Behavioural Treatment ◽  
Related Pathway ◽  
Time Point

Learning to be safe is central for adaptive behaviour when threats are no longer present. Detecting the absence of an expected threat is key for threat extinction learning and an essential process for the behavioural treatment of anxiety-related disorders. One possible mechanism underlying extinction learning is a dopaminergic mismatch signal that encodes the absence of an expected threat. Here we show that such a dopamine-related pathway underlies extinction learning in humans. Dopaminergic enhancement via administration of L-DOPA (vs. Placebo) was associated with reduced retention of differential psychophysiological threat responses at later test, which was mediated by activity in the ventromedial prefrontal cortex that was specific to extinction learning. L-DOPA administration enhanced signals at the time-point of an expected, but omitted threat in extinction learning within the nucleus accumbens, which were functionally coupled with the ventral tegmental area and the amygdala. Computational modelling of threat expectancies further revealed prediction error encoding in nucleus accumbens that was reduced when L-DOPA was administered. Our results thereby provide evidence that extinction learning is influenced by L-DOPA and provide a mechanistic perspective to augment extinction learning by dopaminergic enhancement in humans.

scholarly journals Dopamine transients delivered in learning contexts do not act as model-free prediction errors

2019 ◽  
Author(s):  
Melissa J. Sharpe ◽  
Hannah M. Batchelor ◽  
Lauren E. Mueller ◽  
Chun Yun Chang ◽  
Etienne J.P. Maes ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Associative Learning ◽  
Prediction Error ◽  
Error Term ◽  
Neural Correlates ◽  
Dopamine Neurons ◽  
Prediction Errors ◽  
Model Free ◽  
Reward Prediction ◽  
Excess Value

AbstractDopamine neurons fire transiently in response to unexpected rewards. These neural correlates are proposed to signal the reward prediction error described in model-free reinforcement learning algorithms. This error term represents the unpredicted or ‘excess’ value of the rewarding event. In model-free reinforcement learning, this value is then stored as part of the learned value of any antecedent cues, contexts or events, making them intrinsically valuable, independent of the specific rewarding event that caused the prediction error. In support of equivalence between dopamine transients and this model-free error term, proponents cite causal optogenetic studies showing that artificially induced dopamine transients cause lasting changes in behavior. Yet none of these studies directly demonstrate the presence of cached value under conditions appropriate for associative learning. To address this gap in our knowledge, we conducted three studies where we optogenetically activated dopamine neurons while rats were learning associative relationships, both with and without reward. In each experiment, the antecedent cues failed to acquired value and instead entered into value-independent associative relationships with the other cues or rewards. These results show that dopamine transients, constrained within appropriate learning situations, support valueless associative learning.

Sign in / Sign up

Export Citation Format

Share Document