Mechanisms underlying the formation of the amygdalar fear memory trace: A computational perspective

Feng, Feng; Samarth, Pranit; Paré, Denis; Nair, Satish S.

doi:10.1016/j.neuroscience.2016.02.059

Cited by 19 publications

(12 citation statements)

References 38 publications

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“…Hence, assignment of LA neurons to a memory trace depends on a competitive process, consistent with experimental data [89]. The nature, specificity, and details of synaptic competition in fear memory trace formation are further examined in two subsequent biophysical modeling studies [31,90].…”

Section: Biophysically Realistic Modelssupporting

confidence: 60%

“…With improved understanding of the neurobiology of fear learning and rapid advance in computational power, computational models of the amygdala and extended circuits have evolved from the early simple rule-based models (e.g., [22]) to anatomically constrained connectionist type models (e.g., [23,24]), to large-scale spiking neuron models (e.g., [25]), and more biophysically realistic conductance-based models [26][27][28][29][30][31]. These models addressed the various aspects of the functional roles of the amygdala in emotional learning including relative contribution of the thalamo-amygdala and cortical-amygdala pathways in fear conditioning [23,32], contextual modulation of fear acquisition and extinction [25,33], neural mechanisms of extinction [26], impact of infralimbic cortex in fear suppression [27,34], and the role of competitive synaptic interactions in fear memory formation [28,29].…”

Section: Introductionmentioning

confidence: 99%

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Computational Models of the Amygdala in Acquisition and Extinction of Conditioned Fear

Li¹

2017

The Amygdala - Where Emotions Shape Perception, Learning and Memories

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The amygdala plays a central role in both acquisition and expression of conditioned fear associations and dysregulation of the amygdala leads to fear and anxiety disorders such as posttraumatic stress disorder (PTSD). Computational modeling has served as an important tool to understand the cellular and circuit mechanisms of fear acquisition and extinction. This review provides a critical appraisal of existing computational modeling studies of the amygdala and extended circuitry in acquisition and extinction of learned fear associations. It gives a broad overview of the computational techniques applied to amygdala modeling with an emphasis on how computational models could shed light on the neural mechanisms of fear learning, inform experimental design, and lead to specific, experimentally testable hypotheses. It covers different types of published models including rule-based models, connectionist type models, phenomenological spiking neuronal models, and detailed biophysical conductance-based models. Specific attention is given to the evolution of amygdala models from simple rule-based and connectionist type models to more sophisticated and biologically realistic models. Future direction on computational modeling of the amygdala and associated networks in emotional learning is also discussed.

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Section: Biophysically Realistic Modelssupporting

confidence: 60%

Section: Introductionmentioning

confidence: 99%

Computational Models of the Amygdala in Acquisition and Extinction of Conditioned Fear

Li¹

2017

The Amygdala - Where Emotions Shape Perception, Learning and Memories

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“…A 100-cell network model of the rodent lateral amygdala is adapted from a recently reported model from our group (Kim et al, 2013a, Kim et al, 2015, Feng et al, 2016) where dendritic processing was largely limited to shaping the PSP from the synapse to the soma. Specifically, the performance of the one-compartmental model developed using the proposed approach is compared with that of a 3-compartmental model in Kim et al (2013a).…”

Section: Resultsmentioning

confidence: 99%

Distinct current modules shape cellular dynamics in model neurons

et al. 2016

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Numerous intrinsic currents are known to collectively shape neuronal membrane potential dynamics, or neuronal signatures. Although how sets of currents shape specific signatures such as spiking characteristics or oscillations has been studied individually, it is less clear how a neuron’s suite of currents jointly shape its entire set of signatures. Biophysical conductance based models of neurons represent a viable tool to address this important question. We hypothesized that currents are grouped into distinct modules that shape specific neuronal characteristics or signatures, such as resting potential, sub-threshold oscillations, and spiking waveforms, for several classes of neurons. For such a grouping to occur, the currents within one module should have minimal functional interference with currents belonging to other modules. This condition is satisfied if the gating functions of currents in the same module are grouped together on the voltage axis; in contrast, such functions are segregated along the voltage axis for currents belonging to different modules. We tested this hypothesis using four published example case models and found it to be valid for these classes of neurons. This insight into the neurobiological organization of currents also suggests an intuitive, systematic, and robust methodology to develop biophysical single cell models with multiple biological characteristics applicable for both hand- and automated- tuning approaches. We illustrate the methodology using two example case rodent pyramidal neurons, from the lateral amygdala and the hippocampus. The methodology also helped reveal that a single core compartment model could capture multiple neuronal properties. Such biophysical single compartment models have potential to improve the fidelity of large network models.

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“…Effectively, the model showed that subsets of more excitable projection cells band together by virtue of their excitatory interconnections to suppress plasticity in other projection cells via the recruitment of local-circuit cells. 42 , 68 Another prediction from the model was that the level of inhibtion in the system controlled the size of the fear memory trace. 68 …”

Section: Prior Computational Models Of Pavlovian Fear and Extinctionmentioning

confidence: 99%

Biologically based neural circuit modelling for the study of fear learning and extinction

2016

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The neuronal systems that promote protective defensive behaviours have been studied extensively using Pavlovian conditioning. In this paradigm, an initially neutral-conditioned stimulus is paired with an aversive unconditioned stimulus leading the subjects to display behavioural signs of fear. Decades of research into the neural bases of this simple behavioural paradigm uncovered that the amygdala, a complex structure comprised of several interconnected nuclei, is an essential part of the neural circuits required for the acquisition, consolidation and expression of fear memory. However, emerging evidence from the confluence of electrophysiological, tract tracing, imaging, molecular, optogenetic and chemogenetic methodologies, reveals that fear learning is mediated by multiple connections between several amygdala nuclei and their distributed targets, dynamical changes in plasticity in local circuit elements as well as neuromodulatory mechanisms that promote synaptic plasticity. To uncover these complex relations and analyse multi-modal data sets acquired from these studies, we argue that biologically realistic computational modelling, in conjunction with experiments, offers an opportunity to advance our understanding of the neural circuit mechanisms of fear learning and to address how their dysfunction may lead to maladaptive fear responses in mental disorders.

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Mechanisms underlying the formation of the amygdalar fear memory trace: A computational perspective

Cited by 19 publications

References 38 publications

Computational Models of the Amygdala in Acquisition and Extinction of Conditioned Fear

Computational Models of the Amygdala in Acquisition and Extinction of Conditioned Fear

Distinct current modules shape cellular dynamics in model neurons

Biologically based neural circuit modelling for the study of fear learning and extinction

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