Associative learning

The dopaminergic system facilitates associative learning and motivation by signaling reward expectation and positive or negative valence in selected brain regions. Previous studies measuring somatic and axonal activity suggest a heterogeneous representation of reinforcement signaling by dopaminergic neurons, but it remains unclear how dopamine signals for expectation and valence interact in various brain regions. Here, we used an improved fluorescent dopamine sensor to record dopamine signals associated with reward predictions and valence in eight distinct striatal and non-striatal regions. We

Adaptive behavior is critically dependent on associative learning, where environmental cues are linked with subsequent positive or negative outcomes. At the core of this process, primary neocortical sensory areas serve as pivotal nodes, processing stimuli and distributing information to cortical and subcortical networks. Layer 5 (L5) of the cortex comprises two types of pyramidal projection neurons---intratelencephalic (IT) and extratelencephalic (ET) neurons---each with distinct downstream targets. Sensory cortex exhibits plastic changes after forming a sensory-reward association and L5 neuro

Associative learning relies on the contingency between stimuli and outcomes, yet the neural mechanisms instantiating contingency remain unclear. We investigated dopamine activity in the ventral striatum of mice performing a Pavlovian contingency degradation task. Both anticipatory licking behavior and dopamine responses to a conditioned stimulus diminished when additional rewards were delivered without cues (uncued) but remained consistent when the same additional rewards were paired with a new cue. These findings challenge traditional contingency-based accounts and the recent causal learning

A vast variety of sensory cues are represented as distinct patterns of neural responses in the brain. To retrieve memories associated with sensory cues, one would assume that the representations of sensory signals should be stable over time. However, recent development of population-level recording techniques has revealed that these representations can drift over time $^{1}$. To investigate the mechanisms and consequences of representational drift, we formulate a random feedforward network model $^{2,3}$ that incorporates stochastic fluctuations in membrane potential and synaptic strength $^{4

Odor naming is considered as a particularly challenging task. In free odor naming scenarios people often fail to respond with any linguistic label to certain odors they smell, resulting in what is called an omission (i.e., a lack of response). The cognitive demands and the nature of interactions between olfaction and language related brain’s neural resources are not well understood [1]. Behavioral task: the data that we used to compare our model output were collected from a Swedish National Study on Aging and Care in Kungsholmen (SNAC-K), where ~2500 subjects underwent an odor naming test of

Associative learning is crucial for adapting to environmental changes. Interactions among neuronal populations involving the dorso-medial prefrontal cortex (dmPFC) in rodents are proposed to regulate associative memory. However, how these neuronal populations store and process information about the association remains unclear. Here we developed a pipeline for longitudinal two-photon imaging and computational dissection of neural population activities in male mouse dmPFC during fear-conditioning procedures, enabling us to detect learning-dependent changes in the dmPFC network topology. After co

Neuromodulators like dopamine are critical regulators of associative learning. Indeed, dopaminergic neurons in the dorsal tegmental area interact with the lateral central amygdala (CEl) circuitry involved in fear behavior. Thus, fear learning modulates the microcircuit of the CEl composed of somatostatin-positive (SST+) and protein kinase C-positive (PKCδ+) neurons. During this process, dopamine 1 receptor (D1R) activation enables LTP induction in SST+ CEl neurons (Groessl et al. 2018, Nat Neurosci 21:952-962).In fact, a detailed analysis of dopaminergic cellular effects on synaptic transmissi

…have been the main focus in the learning and memory field, other forms of associative learning are gaining attention. One example is higher-order conditioning, mediated learning, which allows non-reinforced neutral stimuli to induce attraction or aversion through associations with directly reinforced cues. In rodents, sensory preconditioning, and second-order conditioning tasks have been used to assess higher-order conditioning. In this study, we develo

Pavlovian conditioning describes a form of associative learning wherein initially neutral cues acquire motivational value through repeated pairing with rewards. Two learning profiles have been identified in how such cues acquire value and bias performance: sign-trackers, i.e., individuals who attribute high incentive (or attractive) value to reward-predictive cues, and goal-trackers, i.e., individuals who acquire the predictive value of cues but use it to directly approach the reward. The acquisition of incentive value seems crucial in making sign-trackers maladaptive, favouring behaviours lik

This study explores the fundamental aspects of acquired equivalence learning, where equivalence develops between seemingly unrelated stimuli due to shared consequences. The Rutgers Acquired Equivalence Test (RAET) is a paradigm developed to investigate visually guided equivalence learning, consisting of a learning phase and a test phase. The basal ganglia-frontal cortex pathways play a key role in the learning phase, and the test phase is linked to the functions of the hippocampus-medial temporal lobe.We developed three additional learning tests that differ in the complexity or modality of the

…in brain activity 24 hours post-learning. Mice with associative learning experience showed a notable increase in c-Fos-positive cells in specific brain areas known for their role in fear memory formation, such as the prelimbic, retrosplenial, and temporal associative cortex, alongside various amygdala nuclei

…known about how distinct features of associative memories are encoded in neuronal ensembles within and across distinct prefrontal subregions and the role of these representations in the encoding and retrieval of emotional memories. In this study, we aim to elucidate the interplay between prefrontal subregions a

…a central role in different types of associative learning, including conditioning and extinction learning. The NCL receives a dense innervation of tyrosine hydroxylase (TH) immunopositive fibers, which form characteristic basket formations around their target somata. This distinctive innervation profile enabled the identification of the NCL in pigeons, chickens, zebra finches, crows and blackcaps. In this study, we characterized the catecholaminergic system of the Japane

Vicarious fear learning is a highly effective means to learn of potential dangers, as well as a result of having both emotional empathy and associative learning. Studies in social neuroscience have found projections and cell types essential for vicarious fear learning. However, it is unknown what aspects of the cortical network dynamics are essential for and contribute to processing information during social learning, which can be conveniently studied with imaging and advanced electrophysiology setups requiring head fixation. In particular, this project aims to study how the medial associative

…movements during active touch and associative learning in simple stimulus detection tasks, its contribution to behavior becomes crucial as cognitive load and perceptual demands escalate. The gap-crossing task has thus emerged as a model for studying wS1 functions, requiring freely-moving mice to actively employ their whiskers to locate a target platform after a

…(NE) and serotonin (5-HT) in associative learning and the correlation of their release with the reward prediction error (RPE). To address this, we trained mice on a sound detection Pavlovian conditioning task with a 50% reward schedule that allowed examining clean representations of postivite (rewarded trials) and negative RPE (reward omission trials), while we measured DA, ACh, NE and 5-HT release by fiber photometry. Behavioral updating of value representations based on the outcome of the previous trial wa

…obscures the actual dynamics of learning, which can occur within few trials and differ between subjects. For instance, reexamination of previous data (Uengoer and Lachnit, 2006) reveals that during learning subjects do not change their responses gradually, but abruptly (Fig. A). These “jumps” occur at different trials for each subject and averaging therefore paints a misleading picture of gradual learning curves. These data are not easily accounted for with simple associative lea

Is context nothing more than "a backdrop where learning occurs?" In learning theory there is an ongoing debate about what constitutes a context. This implies a strict division between stimuli that act as cues and those that act as context. In extinction learning, a highly context-sensitive learning process, after the cessation of a behavior, its reappearance as renewal occurs after the change of context. In this study, we tested the ability of competing stimuli to act as context in an appetitive extinction learning paradigm in pigeons (Columba livia) . The ability of each stimulus to act as a

Psychological and somatic sickness symptoms are common adverse side effects of immunotherapy and chemotherapy. Due to the unpleasantness of these symptoms, many patients develop negative treatment expectations during the course of therapy and often show anticipatory behavioral and physiological responses upon re-exposure to treatment-related cues. Despite broad clinical implications, the underlying molecular and neurobiological mechanisms of sickness-induced negative treatment expectations and their physiological consequences are largely unknown and difficult to study in humans. This calls for

…analyzed under the perspective of associative learning, reframing continuous drug intake as a learning process may open a new path for treatment optimization. In this context, behaviorally conditioned pharmacological effects, obtained by means of associative learning, have been successfully implemented as strategies to

Learning to predict rewards based on environmental cues is essential for survival. It is widely believed that animals learn to predict rewards by updating predictions whenever the outcome deviates from expectations. Such violations of predictions are called reward prediction errors (RPEs). RPEs are the critical teaching signal in the most widely accepted model for associative learning—temporal difference reinforcement learning (TDRL). As TDRL RPE has been successful at explaining the activity dynamics of dopamine, it has become the dominant theory of dopamine’s role as the critical regulator o

A fundamental and distinguishing feature of human intelligence is that we transfer and accumulate knowledge as a society and across generations. We suggest that for communication and accumulation, explicit (but not implicit) intelligence is likely encapsulated by a network of attractors representing abstractions such as objects or actions and direct connections between them, rather than one that utilizes hidden layers of representation. We propose a network model for explicit human intelligence that is composed of attractors and learned connections as elemental units. As attractors are added a

…errors as a driving force in learning processes and the updating of our models of the world and of ourselves. This process is postulated to manifest both at explicit levels (e.g., subjective responses) and at implicit levels (e.g., eye movements, reaction time) of behavior. We developed a novel associative learning task in immersive virtual reality, in which a cue probabilistically predicted the subsequent location of a target to which participants had to reach. Specifically, the current study (N = 30) examined: (1) wh

…critical for cerebellum-dependent associative learning. Studying these signals in head-fixed mice facilitates the use of imaging, electrophysiological, and optogenetic methods. Here, a low-cost behavioral platform (~$1000) was developed that allows tracking of associative learning in head-fixed mice that locomote freely on a running wheel. The platform incorporates two common associative learning paradigms: eyeblink conditioning and delayed tactile startle conditioning. Behavior is tracked using a camera and the wheel movement