A pavlovian model of the amygdala and its influence within the medial temporal lobe

A scalable spiking amygdala model that explains fear conditioning, extinction, renewal and generalization

The amygdala (AMY) is widely implicated in fear learning and fear behaviour, but it remains unclear how the many biological components present within AMY interact to achieve these abilities. Building on previous work, we hypothesize that individual AMY nuclei represent different quantities and that fear conditioning arises from error-driven learning on the synapses between AMY nuclei. We present a computational model of AMY that (a) recreates the divisions and connections between AMY nuclei and their constituent pyramidal and inhibitory neurons; (b) accommodates scalable high-dimensional representations of external stimuli; (c) learns to associate complex stimuli with the presence (or absence) of an aversive stimulus; (d) preserves feature information when mapping inputs to salience estimates, such that these estimates generalize to similar stimuli; and (e) induces a diverse profile of neural responses within each nucleus. Our model predicts (1) defensive responses and neural activities in several experimental conditions, (2) the consequence of artificially ablating particular nuclei and (3) the tendency to generalize defensive responses to novel stimuli. We test these predictions by comparing model outputs to neural and behavioural data from animals and humans. Despite the relative simplicity of our model, we find significant overlap between simulated and empirical data, which supports our claim that the model captures many of the neural mechanisms that support fear conditioning. We conclude by comparing our model to other computational models and by characterizing the theoretical relationship between pattern separation and fear generalization in healthy versus anxious individuals.

Mechanisms of alcohol influence on fear conditioning: a computational model

A connection between stress-related illnesses and alcohol use disorders is extensively documented. Fear conditioning is a standard procedure used to study stress learning and links it to the activation of amygdala circuitry. However, the connection between the changes in amygdala circuit and function induced by alcohol and fear conditioning is not well established. We introduce a computational model to test the mechanistic relationship between amygdala functional and circuit adaptations during fear conditioning and the impact of acute vs. repeated alcohol exposure. In accordance with experiments, both acute and prior repeated alcohol decreases speed and robustness of fear extinction in our simulations. The model predicts that, first, the delay in fear extinction in alcohol is mostly induced by greater activation of the basolateral amygdala (BLA) after fear acquisition due to alcohol-induced modulation of synaptic weights. Second, both acute and prior repeated alcohol shifts the amygdala network away from the robust extinction regime by inhibiting the activity in the central amygdala (CeA). Third, our model predicts that fear memories formed in acute or after chronic alcohol are more connected to the context. Thus, the model suggests how circuit changes induced by alcohol may affect fear behaviors and provides a framework for investigating the involvement of multiple neuromodulators in this neuroadaptive process.

Silence and its effects on the autonomic nervous system: A systematic review

This systematic review explores the influence of silence on the autonomic nervous system. The Polyvagal Theory has been used as a reference model to describe the autonomic nervous system by explaining its role in emotional regulation, social engagement, and adaptive physiological responses. PubMed, Scopus, PsycInfo, EMBASE, and Google Scholar were systematically searched up until July 2023 for relevant studies. The literature search yielded 511 results, and 37 studies were eventually included in this review. Silence affects the autonomic nervous system differently based on whether it is inner or outer silence. Inner silence enhances activity of the ventral vagus, favoring social engagement, and reducing sympathetic nervous system activity and physiological stress. Outer silence, conversely, can induce a heightened state of alertness, potentially triggering vagal brake removal and sympathetic nervous system activation, though with training, it can foster inner silence, preventing such activation. The autonomic nervous system response to silence can also be influenced by other factors such as context, familiarity with silence, presence and quality of outer noise, and empathy.

Optogenetic fMRI for Brain-Wide Circuit Analysis of Sensory Processing

Sensory processing is a complex neurological process that receives, integrates, and responds to information from one's own body and environment, which is closely related to survival as well as neurological disorders. Brain-wide networks of sensory processing are difficult to investigate due to their dynamic regulation by multiple brain circuits. Optogenetics, a neuromodulation technique that uses light-sensitive proteins, can be combined with functional magnetic resonance imaging (ofMRI) to measure whole-brain activity. Since ofMRI has increasingly been used for investigating brain circuits underlying sensory processing for over a decade, we systematically reviewed recent ofMRI studies of sensory circuits and discussed the challenges of optogenetic fMRI in rodents.

A Biologically Inspired Neural Network Model to Gain Insight Into the Mechanisms of Post-Traumatic Stress Disorder and Eye Movement Desensitization and Reprocessing Therapy

Eye movement desensitization and reprocessing (EMDR) therapy is a well-established therapeutic method to treat post-traumatic stress disorder (PTSD). However, how EMDR exerts its therapeutic action has been studied in many types of research but still needs to be completely understood. This is in part due to limited knowledge of the neurobiological mechanisms underlying EMDR, and in part to our incomplete understanding of PTSD. In order to model PTSD, we used a biologically inspired computational model based on firing rate units, encompassing the cortex, hippocampus, and amygdala. Through the modulation of its parameters, we fitted real data from patients treated with EMDR or classical exposure therapy. This allowed us to gain insights into PTSD mechanisms and to investigate how EMDR achieves trauma remission.

Neural Circuits Underlying Social Fear in Rodents: An Integrative Computational Model

Social avoidance in rodents arises from a complex interplay between the prefrontal cortex and subcortical structures, such as the ventromedial hypothalamus and the dorsal periaqueductal gray matter. Experimental studies are revealing the contribution of these areas, but an integrative view and model of how they interact to produce adaptive behavior are still lacking. Here, we present a computational model of social avoidance, proposing a set of integrated hypotheses on the possible macro organization of the brain system underlying this phenomenon. The model is validated by accounting for several different empirical findings and produces predictions to be tested in future experiments.

Effects of Early Life Stress on the Developing Basolateral Amygdala-Prefrontal Cortex Circuit: The Emerging Role of Local Inhibition and Perineuronal Nets

The links between early life stress (ELS) and the emergence of psychopathology such as increased anxiety and depression are now well established, although the specific neurobiological and developmental mechanisms that translate ELS into poor health outcomes are still unclear. The consequences of ELS are complex because they depend on the form and severity of early stress, duration, and age of exposure as well as co-occurrence with other forms of physical or psychological trauma. The long term effects of ELS on the corticolimbic circuit underlying emotional and social behavior are particularly salient because ELS occurs during critical developmental periods in the establishment of this circuit, its local balance of inhibition:excitation and its connections with other neuronal pathways. Using examples drawn from the human and rodent literature, we review some of the consequences of ELS on the development of the corticolimbic circuit and how it might impact fear regulation in a sex- and hemispheric-dependent manner in both humans and rodents. We explore the effects of ELS on local inhibitory neurons and the formation of perineuronal nets (PNNs) that terminate critical periods of plasticity and promote the formation of stable local networks. Overall, the bulk of ELS studies report transient and/or long lasting alterations in both glutamatergic circuits and local inhibitory interneurons (INs) and their associated PNNs. Since the activity of INs plays a key role in the maturation of cortical regions and the formation of local field potentials, alterations in these INs triggered by ELS might critically participate in the development of psychiatric disorders in adulthood, including impaired fear extinction and anxiety behavior.

A global framework for a systemic view of brain modeling

The brain is a complex system, due to the heterogeneity of its structure, the diversity of the functions in which it participates and to its reciprocal relationships with the body and the environment. A systemic description of the brain is presented here, as a contribution to developing a brain theory and as a general framework where specific models in computational neuroscience can be integrated and associated with global information flows and cognitive functions. In an enactive view, this framework integrates the fundamental organization of the brain in sensorimotor loops with the internal and the external worlds, answering four fundamental questions (what, why, where and how). Our survival-oriented definition of behavior gives a prominent role to pavlovian and instrumental conditioning, augmented during phylogeny by the specific contribution of other kinds of learning, related to semantic memory in the posterior cortex, episodic memory in the hippocampus and working memory in the frontal cortex. This framework highlights that responses can be prepared in different ways, from pavlovian reflexes and habitual behavior to deliberations for goal-directed planning and reasoning, and explains that these different kinds of responses coexist, collaborate and compete for the control of behavior. It also lays emphasis on the fact that cognition can be described as a dynamical system of interacting memories, some acting to provide information to others, to replace them when they are not efficient enough, or to help for their improvement. Describing the brain as an architecture of learning systems has also strong implications in Machine Learning. Our biologically informed view of pavlovian and instrumental conditioning can be very precious to revisit classical Reinforcement Learning and provide a basis to ensure really autonomous learning.

Conditional knockout of MET receptor tyrosine kinase in cortical excitatory neurons leads to enhanced learning and memory in young adult mice but early cognitive decline in older adult mice

Human genetic studies established MET gene as a risk factor for autism spectrum disorders. We have previously shown that signaling mediated by MET receptor tyrosine kinase, expressed in early postnatal developing forebrain circuits, controls glutamatergic neuron morphological development, synapse maturation, and cortical critical period plasticity. Here we investigated how MET signaling affects synaptic plasticity, learning and memory behavior, and whether these effects are age-dependent. We found that in young adult (postnatal 2-3 months) Met conditional knockout (Met^fx/fx:emx1^cre, cKO) mice, the hippocampus exhibits elevated plasticity, measured by increased magnitude of long-term potentiation (LTP) and depression (LTD) in hippocampal slices. Surprisingly, in older adult cKO mice (10-12 months), LTP and LTD magnitudes were diminished. We further conducted a battery of behavioral tests to assess learning and memory function in cKO mice and littermate controls. Consistent with age-dependent LTP/LTD findings, we observed enhanced spatial memory learning in 2-3 months old young adult mice, assessed by hippocampus-dependent Morris water maze test, but impaired spatial learning in 10-12 months mice. Contextual and cued learning were further assessed using a Pavlovian fear conditioning test, which also revealed enhanced associative fear acquisition and extinction in young adult mice, but impaired fear learning in older adult mice. Lastly, young cKO mice also exhibited enhanced motor learning. Our results suggest that a shift in the window of synaptic plasticity and an age-dependent early cognitive decline may be novel circuit pathophysiology for a well-established autism genetic risk factor.

A Computational Model Integrating Multiple Phenomena on Cued Fear Conditioning, Extinction, and Reinstatement

Conditioning, extinction, and reinstatement are fundamental learning processes of animal adaptation, also strongly involved in human pathologies such as post-traumatic stress disorder, anxiety, depression, and dependencies. Cued fear conditioning, extinction, restatement, and systematic manipulations of the underlying brain amygdala and medial prefrontal cortex, represent key experimental paradigms to study such processes. Numerous empirical studies have revealed several aspects and the neural systems and plasticity underlying them, but at the moment we lack a comprehensive view. Here we propose a computational model based on firing rate leaky units that contributes to such integration by accounting for 25 different experiments on fear conditioning, extinction, and restatement, on the basis of a single neural architecture having a structure and plasticity grounded in known brain biology. This allows the model to furnish three novel contributions to understand these open issues: (a) the functioning of the central and lateral amygdala system supporting conditioning; (b) the role played by the endocannabinoids system in within- and between-session extinction; (c) the formation of three important types of neurons underlying fear processing, namely fear, extinction, and persistent neurons. The model integration of the results on fear conditioning goes substantially beyond what was done in previous models.

A systems-neuroscience model of phasic dopamine

We describe a neurobiologically informed computational model of phasic dopamine signaling to account for a wide range of findings, including many considered inconsistent with the simple reward prediction error (RPE) formalism. The central feature of this PVLV framework is a distinction between a primary value (PV) system for anticipating primary rewards (Unconditioned Stimuli [USs]), and a learned value (LV) system for learning about stimuli associated with such rewards (CSs). The LV system represents the amygdala, which drives phasic bursting in midbrain dopamine areas, while the PV system represents the ventral striatum, which drives shunting inhibition of dopamine for expected USs (via direct inhibitory projections) and phasic pausing for expected USs (via the lateral habenula). Our model accounts for data supporting the separability of these systems, including individual differences in CS-based (sign-tracking) versus US-based learning (goal-tracking). Both systems use competing opponent-processing pathways representing evidence for and against specific USs, which can explain data dissociating the processes involved in acquisition versus extinction conditioning. Further, opponent processing proved critical in accounting for the full range of conditioned inhibition phenomena, and the closely related paradigm of second-order conditioning. Finally, we show how additional separable pathways representing aversive USs, largely mirroring those for appetitive USs, also have important differences from the positive valence case, allowing the model to account for several important phenomena in aversive conditioning. Overall, accounting for all of these phenomena strongly constrains the model, thus providing a well-validated framework for understanding phasic dopamine signaling. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Beyond the "Conceptual Nervous System": Can computational cognitive neuroscience transform learning theory?

In the last century, learning theory has been dominated by an approach assuming that associations between hypothetical representational nodes can support the acquisition of knowledge about the environment. The similarities between this approach and connectionism did not go unnoticed to learning theorists, with many of them explicitly adopting a neural network approach in the modeling of learning phenomena. Skinner famously criticized such use of hypothetical neural structures for the explanation of behavior (the "Conceptual Nervous System"), and one aspect of his criticism has proven to be correct: theory underdetermination is a pervasive problem in cognitive modeling in general, and in associationist and connectionist models in particular. That is, models implementing two very different cognitive processes often make the exact same behavioral predictions, meaning that important theoretical questions posed by contrasting the two models remain unanswered. We show through several examples that theory underdetermination is common in the learning theory literature, affecting the solvability of some of the most important theoretical problems that have been posed in the last decades. Computational cognitive neuroscience (CCN) offers a solution to this problem, by including neurobiological constraints in computational models of behavior and cognition. Rather than simply being inspired by neural computation, CCN models are built to reflect as much as possible about the actual neural structures thought to underlie a particular behavior. They go beyond the "Conceptual Nervous System" and offer a true integration of behavioral and neural levels of analysis.

A model of amygdala function following plastic changes at specific synapses during extinction

The synaptic networks in the amygdala have been the subject of intense interest in recent times, primarily because of the role of this structure in emotion. Fear and its extinction depend on the workings of these networks, with particular interest in extinction because of its potential to ameliorate adverse symptoms associated with post-traumatic stress disorder. Here we place emphasis on the extinction networks revealed by recent techniques, and on the probable plasticity properties of their synaptic connections. We use modules of neurons representing each of the principal components identified as involved in extinction. Each of these modules consists of neural networks, containing specific ratios of excitatory and specialized inhibitory neurons as well as synaptic plasticity mechanisms appropriate for the component of the amygdala they represent. While these models can produce dynamic output, here we concentrate on the equilibrium outputs and do not model the details of the plasticity mechanisms. Pavlovian fear conditioning generates a fear memory in the lateral amygdala module that leads to activation of neurons in the basal nucleus fear module but not in the basal nucleus extinction module. Extinction protocols excite infralimbic medial prefrontal cortex neurons (IL) which in turn excite so-called extinction neurons in the amygdala, leading to the release of endocannabinoids from them and an increase in efficacy of synapses formed by lateral amygdala neurons on them. The model simulations show how such a mechanism could explain experimental observations involving the role of IL as well as endocannabinoids in different temporal phases of extinction.

Interplay of prefrontal cortex and amygdala during extinction of drug seeking

Extinction of Pavlovian conditioning is a complex process that involves brain regions such as the medial prefrontal cortex (mPFC), the amygdala and the locus coeruleus. In particular, noradrenaline (NA) coming from the locus coeruleus has been recently shown to play a different role in two subregions of the mPFC, the prelimbic (PL) and the infralimbic (IL) regions. How these regions interact in conditioning and subsequent extinction is an open issue. We studied these processes using two approaches: computational modelling and NA manipulation in a conditioned place preference paradigm (CPP) in mice. In the computational model, NA in PL and IL causes inputs arriving to these regions to be amplified, thus allowing them to modulate learning processes in amygdala. The model reproduces results from studies involving depletion of NA from PL, IL, or both in CPP. In addition, we simulated new experiments of NA manipulations in mPFC, making predictions on the possible results. We searched the parameters of the model and tested the robustness of the predictions by performing a sensitivity analysis. We also present an empirical experiment where, in accord with the model, a double depletion of NA from both PL and IL in CPP with amphetamine impairs extinction. Overall the proposed model, supported by anatomical, physiological, and behavioural data, explains the differential role of NA in PL and IL and opens up the possibility to understand extinction mechanisms more in depth and hence to aid the development of treatments for disorders such as addiction.

Learning not to respond: Role of the hippocampus in withholding responses during omission training

Autoshaping is a Pavlovian learning paradigm in which rats experience pairings of a CS and a US independently of their behavior. When the CS is a lever inserted into the test cage and the US is food delivered to an adjacent magazine, many rats acquire a lever-pressing response called 'sign-tracking' even though that response has no effect on the occurrence of either the CS or the US. Since these lever presses are always followed by the US, it has been suggested that sign-tracking could be due to unintended reinforcement of the response. To eliminate the possibility of such instrumental learning the omission schedule, in which a response to the CS cancels the US, was introduced. Previous research has shown that training rats on autoshaping and switching them to an omission schedule generally reduces but does not eliminate sign-tracking, suggesting that it may be due to both Pavlovian and instrumental learning. In the present study naive rats trained on an omission schedule sign-tracked less than a control group exposed to random, unpaired CS and US presentations, suggesting that they learned to withhold the lever press response because of the negative contingency between that response and the US. In a second experiment rats with dorsal hippocampus lesions sign-tracked more than sham-lesioned rats on omission schedules, suggesting that this case of learning not to respond is hippocampus-based. This conclusion is consistent with many previous findings on the inability of hippocampal rats to withhold or suppress responding, and with studies suggesting that one form of extinction of learned responses in normal rats is due to competition from hippocampus-based learning not to respond.

Goal-Directed Behavior and Instrumental Devaluation: A Neural System-Level Computational Model

Devaluation is the key experimental paradigm used to demonstrate the presence of instrumental behaviors guided by goals in mammals. We propose a neural system-level computational model to address the question of which brain mechanisms allow the current value of rewards to control instrumental actions. The model pivots on and shows the computational soundness of the hypothesis for which the internal representation of instrumental manipulanda (e.g., levers) activate the representation of rewards (or "action-outcomes", e.g., foods) while attributing to them a value which depends on the current internal state of the animal (e.g., satiation for some but not all foods). The model also proposes an initial hypothesis of the integrated system of key brain components supporting this process and allowing the recalled outcomes to bias action selection: (a) the sub-system formed by the basolateral amygdala and insular cortex acquiring the manipulanda-outcomes associations and attributing the current value to the outcomes; (b) three basal ganglia-cortical loops selecting respectively goals, associative sensory representations, and actions; (c) the cortico-cortical and striato-nigro-striatal neural pathways supporting the selection, and selection learning, of actions based on habits and goals. The model reproduces and explains the results of several devaluation experiments carried out with control rats and rats with pre- and post-training lesions of the basolateral amygdala, the nucleus accumbens core, the prelimbic cortex, and the dorso-medial striatum. The results support the soundness of the hypotheses of the model and show its capacity to integrate, at the system-level, the operations of the key brain structures underlying devaluation. Based on its hypotheses and predictions, the model also represents an operational framework to support the design and analysis of new experiments on the motivational aspects of goal-directed behavior.