Advances in fluorescence microscopy enable monitoring larger brain areas in-vivo with finer time resolution. The resulting data rates require reproducible analysis pipelines that are reliable, fully automated, and scalable to datasets generated over the course of months. We present CaImAn, an open-source library for calcium imaging data analysis. CaImAn provides automatic and scalable methods to address problems common to pre-processing, including motion correction, neural activity identification, and registration across different sessions of data collection. It does this while requiring minimal user intervention, with good scalability on computers ranging from laptops to high-performance computing clusters. CaImAn is suitable for two-photon and one-photon imaging, and also enables real-time analysis on streaming data. To benchmark the performance of CaImAn we collected and combined a corpus of manual annotations from multiple labelers on nine mouse two-photon datasets. We demonstrate that CaImAn achieves near-human performance in detecting locations of active neurons

The ability of animals to accumulate sensory information across time is fundamental to decision-making. Using a mouse behavioral paradigm where navigational decisions are based on accumulating pulses of visual cues, I compared neural activity in primary visual (V1) to secondary visual and retrosplenial cortices. Even in V1, only a small fraction of neurons had sensory-like responses to cues. Instead, all areas were grossly similar in how neural populations contained a large variety of task-related information from sensory to cognitive, including cue timings, accumulated counts, place/time, decision and reward outcome. Across-trial influences were prevalent, possibly relevant to how animal behavior incorporates past contexts. Intriguingly, all these variables also modulated the amplitudes of sensory responses. While previous work often modeled the accumulation process as integration, the observed scaling of sensory responses by accumulated counts instead suggests a recursive process where sensory responses are gradually amplified. I show that such a multiplicative feedback-loop algorithm better explains psychophysical data than integration, particularly in how the performance transitions into following Weber-Fechner's Law only at high counts.

A fundamental goal of systems neuroscience is to understand how neural activity gives rise to natural behavior. In order to achieve this goal, we must first build comprehensive models that offer quantitative descriptions of behavior. We develop a new class of probabilistic models to tackle this challenge in the study of larval zebrafish, an important model organism for neuroscience. Larval zebrafish locomote via sequences of punctate swim bouts--brief flicks of the tail--which are naturally modeled as a marked point process. However, these sequences of swim bouts belie a set of discrete and continuous internal states, latent variables that are not captured by standard point process models. We incorporate these variables as latent marks of a point process and explore various models for their dynamics. To infer the latent variables and fit the parameters of this model, we develop an amortized variational inference algorithm that targets the collapsed posterior distribution, analytically marginalizing out the discrete latent variables. With a dataset of over 120,000 swim bouts, we show that our models reveal interpretable discrete classes of swim bouts and continuous internal states like hunger that modulate their dynamics. These models are a major step toward understanding the natural behavioral program of the larval zebrafish and, ultimately, its neural underpinnings.

Macroscale fluorescence imaging is increasingly used to observe biological samples. However, it may suffer from spectral interferences that originate from ambient light or autofluorescence of the sample or its support. In this manuscript, we built a simple and inexpensive fluorescence macroscope, which has been used to evaluate the performance of Speed OPIOM (Out of Phase Imaging after Optical Modulation), which is a reference-free dynamic contrast protocol, to selectively image reversibly photoswitchable fluorophores as labels against detrimental autofluorescence and ambient light. By tuning the intensity and radial frequency of the modulated illumination to the Speed OPIOM resonance and adopting a phase-sensitive detection scheme that ensures noise rejection, we enhanced the sensitivity and the signal-to-noise ratio for fluorescence detection in blot assays by factors of 50 and 10, respectively, over direct fluorescence observation under constant illumination. Then, we overcame the strong autofluorescence of growth media that are currently used in microbiology and realized multiplexed fluorescence observation of colonies of spectrally similar fluorescent bacteria with a unique configuration of excitation and emission wavelengths. Finally, we easily discriminated fluorescent labels from the autofluorescent and reflective background in labeled leaves, even under the interference of incident light at intensities that are comparable to sunlight. The proposed approach is expected to find multiple applications, from biological assays to outdoor observations, in fluorescence macroimaging.

Population recordings of calcium activity are a major source of insight into neural function. Large dataset sizes often require automated methods, but automation can introduce errors that are difficult to detect. Here we show that automatic time course estimation can sometimes lead to significant misattribution errors, in which fluorescence is ascribed to the wrong cell. Misattribution arises when the shapes of overlapping cells are imperfectly defined, or when entire cells or processes are not identified, and misattribution can even be produced by methods specifically designed to handle overlap. To diagnose this problem, we develop a transient-by-transient metric and a visualization tool that allow users to quickly assess the degree of misattribution in large populations. To filter out misattribution, we also design a robust estimator that explicitly accounts for contaminating signals in a generative model. Our methods can be combined with essentially any cell finding technique, empowering users to diagnose and correct at large scale a problem that has the potential to significantly alter scientific conclusions.

Noncovalent interactions between single-stranded DNA (ssDNA) oligonucleotides and single wall carbon nanotubes (SWNTs) have provided a unique class of tunable chemistries for a variety of applications. However, mechanistic insight into both the photophysical and intermolecular phenomena underlying their utility is lacking, which results in obligate heuristic approaches for producing ssDNA-SWNT based technologies. In this work, we present an ultrasensitive "turn-on" nanosensor for neuromodulators dopamine and norepinephrine with strong relative change in fluorescence intensity (Δ F/ F) of up to 3500%, a signal appropriate for in vivo neuroimaging, and uncover the photophysical principles and intermolecular interactions that govern the molecular recognition and fluorescence modulation of this nanosensor synthesized from the spontaneous self-assembly of (GT) ssDNA rings on SWNTs. The fluorescence modulation of the ssDNA-SWNT conjugate is shown to exhibit remarkable sensitivity to the ssDNA sequence chemistry, length, and surface density, providing a set of parameters with which to tune nanosensor dynamic range, analyte selectivity and strength of fluorescence turn-on. We employ classical and quantum mechanical molecular dynamics simulations to rationalize our experimental findings. Calculations show that (GT) ssDNA form ordered rings around (9,4) SWNTs, inducing periodic surface potentials that modulate exciton recombination lifetimes. Further evidence is presented to elucidate how dopamine analyte binding modulates SWNT fluorescence. We discuss the implications of our findings for SWNT-based molecular imaging applications.

Chemical signaling between neurons in the brain can be divided into two major categories: fast synaptic transmission and neuromodulation. Fast synaptic transmission, mediated by amino acids such as glutamate and GABA, occurs on millisecond time scales and results in the influx of ions through ligand-gated ion channels on postsynaptic neurons (Figure 1A). Electrophysiological and optical imaging tools, including genetically encoded voltage indicators, have enabled neuroscientists to link cause (neurotransmitter release) and effect (membrane polarization) of synaptic transmission in time and space. Unlike classical neurotransmitters, neuromodulators do not produce immediate electrical effects that excite or inhibit target neurons. Instead, neuromodulators tune the intrinsic or synaptic properties of neurons, most commonly through interaction with G-protein-coupled receptors (GPCRs) (Figure 1B). Neuromodulators can escape the synaptic cleft and diffuse broadly, allowing them to influence the activity of many neurons in a state-dependent manner. Therefore, the spatial component of neuromodulator flux is fundamentally important. However, the temporal and/or spatial limitations of techniques classically used to study neuromodulation, such as microdialysis and fast-scan cyclic voltammetry (FSCV), make it difficult to interpret how neuromodulator release affects the plasticity or function of target neuronal populations on a moment-to-moment basis. Therefore, tools that can detect neuromodulators with high spatiotemporal resolution are critical for understanding their impact on neural computations that control behavior in health and disease.

Despite advances in experimental techniques and accumulation of large datasets concerning the composition and properties of the cortex, quantitative modeling of cortical circuits under in-vivo-like conditions remains challenging. Here we report and publicly release a biophysically detailed circuit model of layer 4 in the mouse primary visual cortex, receiving thalamo-cortical visual inputs. The 45,000-neuron model was subjected to a battery of visual stimuli, and results were compared to published work and new in vivo experiments. Simulations reproduced a variety of observations, including effects of optogenetic perturbations. Critical to the agreement between responses in silico and in vivo were the rules of functional synaptic connectivity between neurons. Interestingly, after extreme simplification the model still performed satisfactorily on many measurements, although quantitative agreement with experiments suffered. These results emphasize the importance of functional rules of cortical wiring and enable a next generation of data-driven models of in vivo neural activity and computations.