Inference of action potentials (‘spikes’) from neuronal calcium signals is complicated by the scarcity of simultaneous measurements of action potentials and calcium signals (‘ground truth’). We compiled a large, diverse ground-truth database from publicly available and newly performed recordings in zebrafish and mice covering a broad range of calcium indicators, cell types, and signal-to-noise ratios, comprising a total of >35 recording hours from 298 neurons. We developed an algorithm for spike inference (CASCADE) that is based on supervised deep networks, takes advantage of the ground-truth database, infers absolute spike rates, and outperforms existing model-based algorithms. To optimize performance for unseen imaging data, CASCADE retrains itself by resampling ground-truth data to match the respective sampling rate and noise level; therefore, no parameters need to be adjusted by the user. In addition, we developed systematic performance assessments for unseen data, openly release a resource toolbox, and provide a user-friendly cloud-based implementation.

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.

Sensory systems balance stability and plasticity to optimize stimulus representations in dynamic environments. We studied these processes in the olfactory system of adult zebrafish. Activity patterns evoked by repeated odor stimulation were measured by multiphoton calcium imaging in the olfactory bulb (OB) and in telencephalic area Dp, the homolog of olfactory cortex. Whereas odor responses in the OB were highly reproducible, responses of Dp neurons adapted over trials and exhibited substantial variability that could be attributed to ongoing activity and to systematic changes in neuronal representations following each stimulus. An NMDA receptor antagonist did not affect the magnitude of odor responses but strongly reduced the variability and experience-dependent modification of odor responses in Dp. As a consequence, odor representations became stable over trials. These results demonstrate that odor representations in higher brain areas are continuously modified by experience, supporting the view that olfactory processing is inseparable from memory, even in the absence of reinforcement.