Reversal learning

Flexible behaviors are critical for survival in diverse and dynamic environments. However, the synaptic mechanisms that underlie flexible behaviors are largely unknown. Neuronal responses during behavior are diverse, ranging from reliable ‘classical’ responses (e.g. pure tone tuning in auditory cortex) to irregular ‘non-classically responsive’ firing. Previous work has shown that both classically and non-classically responsive cortical neurons contain significant information about sensory stimuli and behavioral decisions and are necessary for task performance suggesting that non-classically re

…This process is known as reversal learning (RL). The neural mechanisms underlying RL in spatial navigation has received limited attention and it remains unclear what neural mechanisms maintain behavioral flexibility. To address this issue, we extended an existing closed-loop simulator of spatial navigation and learning [1], based on spiking neural networks. The activity of place cells and boundary cells were fed as inputs to action selection neurons, which

…To test behavioral flexibility reversal learning paradigms are often used [1]. This flexible adaptation of entrained representations can be mediated by unlearning and forgetting [2]. One possible factor for passive forgetting could be the spontaneous remodeling of neuronal circuitry [3]. Acknowledging that previous studies have presented conflicting views on the effects of forgetting during reversal learning, we hypothesize two potential scenarios: Hypothesis I ('Reusing'): Reusing

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

…relative contributions, we developed a reversal learning task using Freibox, a custom-built, open-source platform for behavioral experiments. Reversal learning performance in this task depends on the OFC, which we demon

Pain expectations are formed by learning the association between specific circumstances and the occurrence of pain. These expectations are known to influence pain perception. Whether this learning is statistically optimal remains debated. If it is, the learning process of the participant should reflect their perception of the precision of each observation (stochasticity) and of the instability of the environment (volatility).Further, it is unclear whether inter-individual differences observed in experimental pain learning studies are due to temporary states or intrinsic traits.To probe these q

…role of forgetting in the context of reversal learning in mice, complemented by a simulated analysis of synaptic remodeling in a recurrent neural network.In our experiment, mice were trained to discriminate between two stimuli in a go/no-go task, followed by a retention test after intervals of either 2 or 16 days. This phase was succeeded by reversal learning, where the stimulus contingencies were switched. We observed that mic

Mental disorders, including depression and anxiety, are a major health concern worldwide, affecting more than 540 million people globally. Although there are treatments available to help with various symptoms, cognitive deficits, particularly impaired executive functions, are often not addressed. In this study, we aim to investigate the effectiveness of HBK-15, a multimodal compound known for its rapid antidepressant-like effect, in reducing cognitive flexibility deficits in mice.We used male Balb/c mice. To assess the effect on cognitive flexibility, we used touchscreen-based two-choice pairw

Context-dependent signals shape the representation of sensory stimuli in the brain and determine which stimuli are amplified to guide behaviour. Top-down attentional signals were proposed to modulate the apical gain of pyramidal neurons in the sensory cortex, but the exact nature of this interaction remains poorly understood. Long-range orbitofrontal cortex (OFC) projections were found to respond to changes in task contingency, targeting somatostatin-expressing (SST+) neurons in the sensory cortex. We suggest that the apical gain modulation reflects the stimulus value, i.e. the future reward e

…RSC cause impairment in exploring and learning new alternative options when previous ones were available but no longer appropriate, suggesting its importance for set-shifting behaviors1. Building on previous evidence of impaired reversal learning in 6-month-old 5xFAD mice2, this study focuses on uncovering the synaptic correlates of reversal learning in the RSC in the context of AD. Therefore, we use a set-shifting task that challenges the animals to devel

…new ones, a phenomenon known as reversal learning (RL). Despite ample knowledge about neural representations of space, the mechanisms of RL in spatial navigation remain understudied. To fill this gap, we extended an existing closed-loop simulator of spatial navigation and learning (Ghazinouri et al. 2023). The model is based on a spiking neural network that captures features of the hippocampus, specifically place cells and boundary cells. These cells feed inputs into action selection neurons, whose activity

…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

Cognitive flexibility is a function of the prefrontal cortex (PFC) that allows shifting strategies to adjust performance in changing environments. The dorsal raphe nucleus (DRN) provides most of the serotonergic inputs to the PFC. Clinical and pharmacological evidence suggests that prefrontal impairments or serotonergic alterations affect cognitive flexibility. However, the neuronal correlates of cognitive flexibility are largely unknown, and whether modulation of the neuronal activity in the DRN affects flexible behaviors remains unclear. We investigated how projections from the DRN modulate

Reinforcement learning implies to associate an outcome with an action performed in the past, sometimes several seconds after its execution. How the artificial intelligence and brain associate these two events refers to the credit assignment problem (CAP). We hypothesized that the required temporal convergence of an eligibility trace encoding the planned action, and the referring outcome of that action takes place in the same brain area, to reinforce an optimal policy. By using a combination of pharmacogenetic/optogenetic manipulations and one-photon calcium imaging in awake behaving mice, we p

Learning to learn is a separate function from classical learning, which makes our learning more efficient and flexible. It is particularly associated with primates (Harlow, 1953) and linked to the lateral prefrontal cortex (LPFC) by lesions studies (Browning et al., 2007). LPFC forms an interacting circuit with the midcingulate cortex (MCC) which plays an important role in outcome and performance evaluation (Procyk et al., 2016). Most electrophysiological recordings are made after extensive training, so they ignore neurophysiological changes that permit learning to learn. This project identifi

…has not previously been studied for learning and memory performance. Our experiment trained 6-9 month-old male and female Nrxn1+/- and Nrxn1+/+ wildtype (C57BL/6J) mice for odour discrimination learning in an operant olfactometer with a sucrose reward for responding (licking) to the CS+ odour and no reward for responding to the CS- odour. Once the mice had met the criterion of 85% correct, they were tested in a reversal learning paradigm. The total errors made by the Nrxn1+/- mice and wildtype mice were a

Beliefs and expectations, also referred to as priors, shape how we perceive our environment. In a complex and ever-changing world, prior beliefs need to be flexibly and continuously integrated into sensory decision processes to guide adaptive behavior. Despite the advances in understanding the integration of priors in visual decisions at the computational and algorithmic levels, its neural mechanisms remain unclear. The goal of this study was to understand the neural dynamics involved in belief formation and updating. Here, we used high-density electroencephalogram recordings to constrain and

Reversal learning, a form of cognitive flexibility disrupted in many brain disorders, involves switching from one response to another when the reward contingencies of the responses are reversed. The hippocampus, especially the ventral hippocampus (VH), connects to fronto-striatal circuits regulating reversal learning. Previous studies reported that hippocampal lesions impaired, whereas chemogenetic activation of VH facilitated, reversal learning acquisition in animal models. However, there is limited evidence on how VH activity affects distinct stages of reversal learning. Therefore, we examin

In reversal learning tasks, large-scale circuits in multiple brain areas are involved in encoding multiple decision variables, such as trial outcomes, action values and action switching. It is unknown how spatiotemporal patterns of cortical encoding differentially encode these variables and whether the encoding depends on the level of engagement in the task. Here, we used a combination of large-scale imaging techniques and computational methods to investigate the representation of reward-related decision variables at the widefield and single-neuron levels. Widefield imaging and unsupervised an

…paradigm to study this behavior is reversal learning, where the values of stimuli are suddenly reversed, for example from positive to negative. Here we investigate how computational models of neural circuits can express the many behavioral strategies empirically observed in reversal learning. We report that simply introducing more neurons in a neu

The ability to flexibly respond to sensory cues in dynamic environments is essential to adaptive auditory-guided behaviors such as navigation and communication. How do neural circuits flexibly gate sensory information to select appropriate behavioral strategies based on sensory input and context? Auditory neural responses during behavior are diverse, ranging from highly-reliable ‘classical’ responses (i.e. robust, frequency-tuned cells) to irregular or seemingly random ‘non-classically responsive’ firing patterns (i.e., nominally non-responsive cells) that fail to demonstrate any significant t

…used a hippocampus-dependent spatial reversal task to measure hippocampal ensemble dynamics when mice switched navigational goals. Using Miniscope calcium imaging, we identified neuronal ensembles (co-active neurons) in dorsal CA1 that were spatially tuned and stable across training sessions. When reward locations were moved during a

…mice were assessed in a T-maze reversal learning task, which requires subjects to reverse a previo