Topic: Memory trace

Timescales characterize how fast the observables change in time. In neuroscience, they can be estimated from the measured activity and can be used, for example, as a signature of the memory trace in the network. I will first discuss the inference of the timescales from the neuroscience data comprised of the short trials and introduce a new unbiased method. Then, I will apply the method to the data recorded from a local population of cortical neurons from the visual area V4. I will demonstrate that the ongoing spiking activity unfolds across at least two distinct timescales - fast and slow - an

Perception and action are biased by our recent experiences. Even when a sequence of stimuli are randomly presented, responses are sometimes attracted toward the past. The mechanism of such bias, recently termed serial dependence, is still under investigation. Currently, there is mixed evidence indicating that such bias could be either from a sensory and perceptual origin or occurring only at decisional stages. In this talk, I will present recent findings from our group showing that biases are decreased when disrupting the memory trace in a premotor region in a simple visuomotor task. In additi

The head direction (HD) system is classically modeled as a ring attractor network which ensures a stable representation of the animal’s head direction. This unidimensional description popularized the view of the HD system as the brain’s internal compass. However, unlike a globally consistent magnetic compass, the orientation of the HD system is dynamic, depends on local cues and exhibits remapping across familiar environments5. Such a system requires mechanisms to remember and align to familiar landmarks, which may not be well described within the classic 1-dimensional framework. To search for