An insular cortex - lateral amygdala network in fear learning

Shriya Palchaudhuri
EPFL, 2022
Thèse EPFL

Résumé

Animals are capable of evaluating sensory cues for possible threats and adapting their behaviours accordingly. Fear learning is an evolutionarily conserved behaviour crucial for animal survival, during which sensory percepts with a negative reinforcing quality, also called unconditioned stimuli (US), influence the valence of initially neutral sensory cues (conditioned stimulus, CS). The lateral amygdala (LA) is thought to integrate information about sensory cues like tones (the CS), and information about primary reinforcers like painful stimuli (the US). Nevertheless, synaptic afferents to the LA and upstream brain areas remain understudied in fear learning. The posterior insular cortex (pInsCx) has been shown to project to the LA and is known to be a multimodal sensory area which processes auditory, somatosensory-nociceptive, and interoceptive information, making it an interesting brain area with a putative role in fear learning. My PhD thesis therefore aimed to investigate a possible connection from the pInsCx to the LA, and the role of this pathway in the fear learning circuitry. I first used anatomical anterograde and retrograde tracing strategies in the mouse to study the afferents to the LA. Besides previously known inputs, I found that the granular (GI) and the dysgranular (DI) areas of the pInsCx strongly project to the anterior LA. With optogenetically-assisted circuit mapping (OACM), I found a glutamatergic monosynaptic connection from the pInsCx to LA principal neurons that, in agreement with the anatomical findings, was localized more to the anterior LA. In comparison, a well-studied auditory thalamic (MGm/PIN) input was localized more to the posterior LA. Despite this regional input specificity, ~30% LA principal neurons received projections from both areas. Thus, in the first part, using anatomical tracing and OACM, I have unambiguously shown a robust functional excitatory connection from the pInsCx (GI/DI) to the LA.In the second part of my thesis, I aimed to characterize the role of the pInsCx and the pInsCx-LA pathway in fear learning. Using archaerhodopsin-mediated optogenetic inhibition, I found that silencing of footshock-driven activity of pInsCx neurons in-vivo, partially blocks auditory-cued fear memory. These results were also supported by chemogenetic silencing of the pInsCx during the training day, which led to a modest impairment of fear retrieval a day later. Next, to investigate the role of the pInsCx-LA projection more specifically, I used a retrograde expression approach to silence LA-projectors during the footshock, which again, suppressed fear memory one day later. In further ex-vivo experiments, I found that EPSCs at the pInsCx-LA connection exhibited an increased AMPA/NMDA ratio following fear learning, indicating that plasticity occurs at this pathway during fear learning. Finally, miniature microscopic Ca2+ imaging of LA neurons combined with halorhodopsin-mediated axon inhibition, showed that the pInsCx-LA excitatory synapse contributes to transmitting footshock information to a sub-population of LA neurons. This study unravels the relationship between two serial nodes, the pInsCx and the LA, in the fear learning network. Thus, the pInsCx-LA connection is a novel pathway in auditory-cued fear memory that is strengthened with learning and that partly provides aversive information to the amygdala.

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https://infoscience.epfl.ch/record/295387?ln=fr

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Shriya Palchaudhuri
EPFL, 2022
Thèse EPFL

Résumé

Official source

https://infoscience.epfl.ch/record/295387?ln=fr

À propos de ce résultat

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Theory of representation learning in cortical neural networks

Carlos Stein Naves de Brito

Our brain continuously self-organizes to construct and maintain an internal representation of the world based on the information arriving through sensory stimuli. Remarkably, cortical areas related to different sensory modalities appear to share the same functional unit, the neuron, and develop through the same learning mechanism, synaptic plasticity. It motivates the conjecture of a unifying theory to explain cortical representational learning across sensory modalities. In this thesis we present theories and computational models of learning and optimization in neural networks, postulating functional properties of synaptic plasticity that support the apparent universal learning capacity of cortical networks. In the past decades, a variety of theories and models have been proposed to describe receptive field formation in sensory areas. They include normative models such as sparse coding, and bottom-up models such as spike-timing dependent plasticity. We bring together candidate explanations by demonstrating that in fact a single principle is sufficient to explain receptive field development. First, we show that many representative models of sensory development are in fact implementing variations of a common principle: nonlinear Hebbian learning. Second, we reveal that nonlinear Hebbian learning is sufficient for receptive field formation through sensory inputs. A surprising result is that our findings are independent of specific details, and allow for robust predictions of the learned receptive fields. Thus nonlinear Hebbian learning and natural statistics can account for many aspects of receptive field formation across models and sensory modalities. The Hebbian learning theory substantiates that synaptic plasticity can be interpreted as an optimization procedure, implementing stochastic gradient descent. In stochastic gradient descent inputs arrive sequentially, as in sensory streams. However, individual data samples have very little information about the correct learning signal, and it becomes a fundamental problem to know how many samples are required for reliable synaptic changes. Through estimation theory, we develop a novel adaptive learning rate model, that adapts the magnitude of synaptic changes based on the statistics of the learning signal, enabling an optimal use of data samples. Our model has a simple implementation and demonstrates improved learning speed, making this a promising candidate for large artificial neural network applications. The model also makes predictions on how cortical plasticity may modulate synaptic plasticity for optimal learning. The optimal sampling size for reliable learning allows us to estimate optimal learning times for a given model. We apply this theory to derive analytical bounds on times for the optimization of synaptic connections. First, we show this optimization problem to have exponentially many saddle-nodes, which lead to small gradients and slow learning. Second, we show that the number of input synapses to a neuron modulates the magnitude of the initial gradient, determining the duration of learning. Our final result reveals that the learning duration increases supra-linearly with the number of synapses, suggesting an effective limit on synaptic connections and receptive field sizes in developing neural networks.

EPFL2016

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Fear Learning: An Evolving Picture for Plasticity at Synaptic Afferents to the Amygdala

Denys Osypenko, Shriya Palchaudhuri, Ralf Schneggenburger

Unraveling the neuronal mechanisms of fear learning might allow neuroscientists to make links between a learned behavior and the underlying plasticity at specific synaptic connections. In fear learning, an innocuous sensory event such as a tone (called the conditioned stimulus, CS) acquires an emotional value when paired with an aversive outcome (unconditioned stimulus, US). Here, we review earlier studies that have shown that synaptic plasticity at thalamic and cortical afferents to the lateral amygdala (LA) is critical for the formation of auditory-cued fear memories. Despite the early progress, it has remained unclear whether there are separate synaptic inputs that carry US information to the LA to act as a teaching signal for plasticity at CS-coding synapses. Recent findings have begun to fill this gap by showing, first, that thalamic and cortical auditory afferents can also carry US information; second, that the release of neuromodulators contributes to US-driven teaching signals; and third, that synaptic plasticity additionally happens at connections up- and downstream of the LA. Together, a picture emerges in which coordinated synaptic plasticity in serial and parallel circuits enables the formation of a finely regulated fear memory.

SAGE PUBLICATIONS INC2022

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Humans and animals learn by modifying the synaptic strength between neurons, a phenomenon known as synaptic plasticity. These changes can be induced by rather short stimuli (lasting, for instance, only a few seconds), yet, in order to be useful for long-term memory, they should remain stable for months or years. Experimentalists study synaptic plasticity by applying a vast variety of protocols. In the present thesis we focus on protocols that fall under two main categories: (i) Those that induce synaptic modifications that last for only a few hours ("early phase" of plasticity) (ii) Those that allow synapses to undergo a sequence of steps that transforms the rapid changes occurring during the "early phase" into a stable memory trace ("late phase" of plasticity). The goal of this thesis is to better understand synaptic plasticity across these different phases, early and late, by creating compact mathematic models to describe the plasticity mechanisms. Our approach allows for a synthetic view of the field as well as the exploration of functional consequences of learning. In this direction, we propose a model for the induction of synaptic plasticity that depends on the presynaptic spike time and nonlinearly on the postsynaptic voltage. The model is able to reproduce a broad range of experimental protocols such as voltage-clamp experiments and spike-timing experiments. Since the voltage is a key element in the model, we describe the neuronal activity by using a compact neuron model that faithfully reproduces the voltage time course of pyramidal neurons. In addition, this model of the induction of synaptic plasticity is combined with a trigger process for protein synthesis, and the final stabilization mechanism in order to describe the "late phase". In this combinatory form, it is able to explain experimental phenomena known as tagging experiments and to make testable predictions. A study of functional consequences of the induction model reveals selectivity in the inputs, independent component analysis computation and a tight relation between connectivity and coding. In parallel a top-down approach finding independent components is used to derive a rate-based learning rule which shows structural correlations with the induction model. This unified model across different time scales allowing the stabilization of synapses is crucial to understand learning and memory processes in animals and humans, and a necessary ingredient for any large-scale model of the brain.

EPFL2009

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Theory of representation learning in cortical neural networks

Carlos Stein Naves de Brito

EPFL2016

EPFL2009