Topic: reversal learning

To survive in a changing world, animals often need to suppress a previously learnt, but now obsolete, behavior and acquire a new behavior. 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

Neural networks need to continuously adapt their representations to survive in a constantly changing environment. 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