Stable one-dimensional periodic waves in Kerr-type saturable and quadratic nonlinear media

We review the latest progress and properties of the families of bright and dark one-dimensional periodic waves propagating in saturable Kerr-type and quadratic nonlinear media. We show how saturation of the nonlinear response results in appearance of stability (instability) bands in focusing (defocusing) medium, which is in sharp contrast with the properties of periodic waves in Kerr media. One of the key results discovered is the stabilization of multicolor periodic waves in quadratic media. In particular, dark-type waves are shown to be metastable, while bright-type waves are completely stable in a broad range of energy flows and material parameters. This yields the first known example of completely stable periodic wave patterns propagating in conservative uniform media supporting bright solitons. Such results open the way to the experimental observation of the corresponding self-sustained periodic wave patterns.Comment: 29 pages, 10 figure

Community-based benchmarking improves spike rate inference from two-photon calcium imaging data

In recent years, two-photon calcium imaging has become a standard tool to probe the function of neural circuits and to study computations in neuronal populations. However, the acquired signal is only an indirect measurement of neural activity due to the comparatively slow dynamics of fluorescent calcium indicators. Different algorithms for estimating spike rates from noisy calcium measurements have been proposed in the past, but it is an open question how far performance can be improved. Here, we report the results of the spikefinder challenge, launched to catalyze the development of new spike rate inference algorithms through crowd-sourcing. We present ten of the submitted algorithms which show improved performance compared to previously evaluated methods. Interestingly, the top-performing algorithms are based on a wide range of principles from deep neural networks to generative models, yet provide highly correlated estimates of the neural activity. The competition shows that benchmark challenges can drive algorithmic developments in neuroscience

Fast reverse replays of recent spatiotemporal trajectories in a robotic hippocampal model

A number of computational models have recently emerged in an attempt to understand the dynamics of hippocampal replay, but there has been little progress in testing and implementing these models in real-world robotics settings. Presented here is a bioinspired hippocampal CA3 network model, that runs in real-time to produce reverse replays of recent spatiotemporal sequences in a robotic spatial navigation task. For the sake of computational efficiency, the model is composed of continuous-rate based neurons, but incorporates two biophysical properties that have recently been hypothesised to play an important role in the generation of reverse replays: intrinsic plasticity and short-term plasticity. As this model only replays recently active trajectories, it does not directly address the functional properties of reverse replay, for instance in robotic learning tasks, but it does support further investigations into how reverse replays could contribute to functional improvements

Robots that imagine – can hippocampal replay be utilized for robotic mnemonics?

Neurophysiological studies on hippocampal replay, which was a phenomenon first shown in rodents as the reactivation of previously active hippocampal cells, has shown it to be potentially important for mnemonic functions such as memory consolidation/recall, learning and planning. Since its discovery, a small number of neuronal models have been developed to attempt to describe the workings of this phenomenon. But it may be possible to utilize hippocampal replay to help solve some of the difficult challenges that face robotic cognition, learning and memory, and/or be used for the development of biomimetic robotics. Here we review these models in the hope of learning their workings, and see that their neural network structures may be integrated into current neural network based algorithms for robotic spatial memory, and perhaps are particularly suited for reinforcement learning paradigms