@article {70084, title = {Imaging cellular activity simultaneously across all organs of a vertebrate reveals body-wide circuits}, journal = {bioRxiv}, year = {2025}, month = {2025 August 22}, abstract = {

All cells in an animal collectively ensure, moment-to-moment, the survival of the whole organism in the face of environmental stressors1,2. Physiology seeks to elucidate the intricate network of interactions that sustain life, which often span multiple organs, cell types, and timescales, but a major challenge lies in the inability to simultaneously record time-varying cellular activity throughout the entire body.We developed WHOLISTIC, a method to image second-timescale, time-varying intracellular dynamics across cell-types of the vertebrate body. By advancing and integrating volumetric fluorescence microscopy, machine learning, and pancellular transgenic expression of calcium sensors in transparent young Danio rerio (zebrafish) and adult Danionella, the method enables real-time recording of cellular dynamics across the organism. Calcium is a universal intracellular messenger, with a large array of cellular processes depending on changes in calcium concentration across varying time-scales, making it an ideal proxy of cellular activity3.Using this platform to screen the dynamics of all cells in the body, we discovered unexpected responses of specific cell types to stimuli, such as chondrocyte reactions to cold, meningeal responses to ketamine, and state-dependent activity, such as oscillatory ependymal-cell activity during periods of extended motor quiescence. At the organ scale, the method uncovered pulsating traveling waves along the kidney nephron. At the multi-organ scale, we uncovered muscle synergies and independencies, as well as muscle-organ interactions. Integration with optogenetics allowed us to all-optically determine the causal direction of brain-body interactions. At the whole-organism scale, the method captured the rapid brainstem-controlled redistribution of blood flow across the body.Finally, we advanced Whole-Body Expansion Microscopy4 to provide ground-truth molecular and ultrastructural anatomical context, explaining the spatiotemporal structure of activity captured by WHOLISTIC. Together, these innovations establish a new paradigm for systems biology, bridging cellular and organismal physiology, with broad implications for both fundamental research and drug discovery.

}, doi = {10.1101/2025.08.20.670374}, url = {https://www.biorxiv.org/content/early/2025/08/22/2025.08.20.670374.1}, author = {Ruetten, Virginia M. S. and Zheng, Wei and Siwanowicz, Igor and Mensh, Brett D. and Eddison, Mark and Hu, Amy and Chi, Yunfeng and Lemire, Andrew L. and Guo, Caiying and Kadobianskyi, Mykola and Renz, Marc and Lelek-Greskovic, Sara and He, Yisheng and Close, Kari and Ihrke, Gudrun and Dev, Aparna and Petruncio, Alyson and Wan, Yinan and Engert, Florian and Fishman, Mark C. and Judkewitz, Benjamin and Rubinov, Mikail and Keller, Philipp J. and Satou, Chie and Yu, Guoqiang and Tillberg, Paul W. and Sahani, Maneesh and Ahrens, Misha B.} } @article {48838, title = {Visual motion computation in recurrent neural networks.}, journal = {bioRxiv}, year = {2017}, month = {2017 Jan 09}, pages = {099101}, abstract = {

Populations of neurons in primary visual cortex (V1) transform direct thalamic inputs into a cortical representation which acquires new spatio-temporal properties. One of these properties, motion selectivity, has not been strongly tied to putative neural mechanisms, and its origins remain poorly understood. Here we propose that motion selectivity is acquired through the recurrent mechanisms of a network of strongly connected neurons. We first show that a bank of V1 spatiotemporal receptive fields can be generated accurately by a network which receives only instantaneous inputs from the retina. The temporal structure of the receptive fields is generated by the long timescale dynamics associated with the high magnitude eigenvalues of the recurrent connectivity matrix. When these eigenvalues have complex parts, they generate receptive fields that are inseparable in time and space, such as those tuned to motion direction. We also show that the recurrent connectivity patterns can be learnt directly from the statistics of natural movies using a temporally-asymmetric Hebbian learning rule. Probed with drifting grating stimuli and moving bars, neurons in the model show patterns of responses analogous to those of direction-selective simple cells in primary visual cortex. These computations are enabled by a specific pattern of recurrent connections, that can be tested by combining connectome reconstructions with functional recordings.

}, doi = {https://doi.org/10.1101/099101}, author = {Pachitariu, Marius and Sahani, Maneesh} } @article {48842, title = {Inhibitory control of correlated intrinsic variability in cortical networks.}, journal = {eLife}, volume = {5}, year = {2016}, month = {2016 Dec 07}, abstract = {

Cortical networks exhibit intrinsic dynamics that drive coordinated, large-scale fluctuations across neuronal populations and create noise correlations that impact sensory coding. To investigate the network-level mechanisms that underlie these dynamics, we developed novel computational techniques to fit a deterministic spiking network model directly to multi-neuron recordings from different rodent species, sensory modalities, and behavioral states. The model generated correlated variability without external noise and accurately reproduced the diverse activity patterns in our recordings. Analysis of the model parameters suggested that differences in noise correlations across recordings were due primarily to differences in the strength of feedback inhibition. Further analysis of our recordings confirmed that putative inhibitory neurons were indeed more active during desynchronized cortical states with weak noise correlations. Our results demonstrate that network models with intrinsically-generated variability can accurately reproduce the activity patterns observed in multi-neuron recordings and suggest that inhibition modulates the interactions between intrinsic dynamics and sensory inputs to control the strength of noise correlations.

}, issn = {2050-084X}, doi = {10.7554/eLife.19695}, author = {Stringer, Carsen and Pachitariu, Marius and Steinmetz, Nicholas A and Okun, Michael and Bartho, Peter and Harris, Kenneth D and Sahani, Maneesh and Lesica, Nicholas A} } @article {65751, title = {Inhibitory control of correlated intrinsic variability in cortical networks}, journal = {Elife}, volume = {5}, year = {2016}, month = {12/2016}, pages = {e19695}, abstract = {

Cortical networks exhibit intrinsic dynamics that drive coordinated, large-scale fluctuations across neuronal populations and create noise correlations that impact sensory coding. To investigate the network-level mechanisms that underlie these dynamics, we developed novel computational techniques to fit a deterministic spiking network model directly to multi-neuron recordings from different rodent species, sensory modalities, and behavioral states. The model generated correlated variability without external noise and accurately reproduced the diverse activity patterns in our recordings. Analysis of the model parameters suggested that differences in noise correlations across recordings were due primarily to differences in the strength of feedback inhibition. Further analysis of our recordings confirmed that putative inhibitory neurons were indeed more active during desynchronized cortical states with weak noise correlations. Our results demonstrate that network models with intrinsically-generated variability can accurately reproduce the activity patterns observed in multi-neuron recordings and suggest that inhibition modulates the interactions between intrinsic dynamics and sensory inputs to control the strength of noise correlations.

}, doi = {https://doi.org/10.7554/eLife.19695}, author = {Stringer, Carsen and Pachitariu, Marius and Steinmetz, Nicholas A and Okun, Michael and Bartho, Peter and Harris, Kenneth D and Sahani, Maneesh and Lesica, Nicholas A} } @article {48830, title = {Learning enhances sensory and multiple non-sensory representations in primary visual cortex.}, journal = {Neuron}, volume = {86}, year = {2015}, month = {2015 Jun 17}, pages = {1478-90}, abstract = {

We determined how learning modifies neural representations in primary visual cortex (V1) during acquisition of a visually guided behavioral task. We imaged the activity of the same layer 2/3 neuronal populations as mice learned to discriminate two visual patterns while running through a virtual corridor, where one pattern was rewarded. Improvements in behavioral performance were closely associated with increasingly distinguishable population-level representations of task-relevant stimuli, as a result of stabilization of existing and recruitment of new neurons selective for these stimuli. These effects correlated with the appearance of multiple task-dependent signals during learning: those that increased neuronal selectivity across the population when expert animals engaged in the task, and those reflecting anticipation or behavioral choices specifically in neuronal subsets preferring the rewarded stimulus. Therefore, learning engages diverse mechanisms that modify sensory and non-sensory representations in V1 to adjust its processing to task requirements and the behavioral relevance of visual stimuli.

}, keywords = {Animals, Calcium, Discrimination (Psychology), Female, Learning, Luminescent Proteins, Male, Mice, Mice, Inbred C57BL, Mice, Transgenic, Models, Neurological, Neurons, Nonlinear Dynamics, Optogenetics, Photic Stimulation, Sensory Receptor Cells, User-Computer Interface, Vision, Ocular, Visual Cortex}, issn = {1097-4199}, doi = {10.1016/j.neuron.2015.05.037}, author = {Poort, Jasper and Khan, Adil G and Pachitariu, Marius and Nemri, Abdellatif and Orsolic, Ivana and Krupic, Julija and Bauza, Marius and Sahani, Maneesh and Keller, Georg B and Mrsic-Flogel, Thomas D and Hofer, Sonja B} } @article {48831, title = {State-dependent population coding in primary auditory cortex.}, journal = {The Journal of Neuroscience : the official journal of the Society for Neuroscience}, volume = {35}, year = {2015}, month = {2015 Feb 04}, pages = {2058-73}, abstract = {

Sensory function is mediated by interactions between external stimuli and intrinsic cortical dynamics that are evident in the modulation of evoked responses by cortical state. A number of recent studies across different modalities have demonstrated that the patterns of activity in neuronal populations can vary strongly between synchronized and desynchronized cortical states, i.e., in the presence or absence of intrinsically generated up and down states. Here we investigated the impact of cortical state on the population coding of tones and speech in the primary auditory cortex (A1) of gerbils, and found that responses were qualitatively different in synchronized and desynchronized cortical states. Activity in synchronized A1 was only weakly modulated by sensory input, and the spike patterns evoked by tones and speech were unreliable and constrained to a small range of patterns. In contrast, responses to tones and speech in desynchronized A1 were temporally precise and reliable across trials, and different speech tokens evoked diverse spike patterns with extremely weak noise correlations, allowing responses to be decoded with nearly perfect accuracy. Restricting the analysis of synchronized A1 to activity within up states yielded similar results, suggesting that up states are not equivalent to brief periods of desynchronization. These findings demonstrate that the representational capacity of A1 depends strongly on cortical state, and suggest that cortical state should be considered as an explicit variable in all studies of sensory processing.

}, keywords = {Animals, Auditory Cortex, Cortical Synchronization, Evoked Potentials, Auditory, Gerbillinae, Male, Neurons}, issn = {1529-2401}, doi = {10.1523/JNEUROSCI.3318-14.2015}, author = {Pachitariu, Marius and Lyamzin, Dmitry R and Sahani, Maneesh and Lesica, Nicholas A} } @conference {48833, title = {Extracting regions of interest from biological images with convolutional sparse block coding.}, booktitle = {Neural Information Processing Systems (NIPS 2013)}, year = {2013}, month = {2013 Dec 05}, address = {Lake Tahoe}, abstract = {

Biological tissue is often composed of cells with similar morphologies replicated throughout large volumes and many biological applications rely on the accurate identification of these cells and their locations from image data. Here we develop a generative model that captures the regularities present in images composed of repeating elements of a few different types. Formally, the model can be described as convolutional sparse block coding. For inference we use a variant of convolutional matching pursuit adapted to block-based representations. We extend the K-SVD learning algorithm to subspaces by retaining several principal vectors from the SVD decomposition instead of just one. Good models with little cross-talk between subspaces can be obtained by learning the blocks incrementally. We perform extensive experiments on simulated images and the inference algorithm consistently recovers a large proportion of the cells with a small number of false positives. We fit the convolutional model to noisy GCaMP6 two-photon images of spiking neurons and to Nissl-stained slices of cortical tissue and show that it recovers cell body locations without supervision. The flexibility of the block-based representation is reflected in the variability of the recovered cell shapes.

}, url = {http://papers.nips.cc/paper/5167-extracting-regions-of-interest-from-biological-images-with-convolutional-sparse-block-coding}, author = {Pachitariu, Marius and Packer, Adam M and Pettit, Noah and Dalgleish, Henry and H{\"a}usser, Michael and Sahani, Maneesh} } @conference {48834, title = {Recurrent linear models of simultaneously-recorded neural populations.}, booktitle = {Neural Information Processing Systems (NIPS 2013)}, year = {2013}, month = {2013 Dec 05}, address = {Lake Tahoe}, abstract = {

Population neural recordings with long-range temporal structure are often best understood in terms of a shared underlying low-dimensional dynamical process. Advances in recording technology provide access to an ever larger fraction of the population, but the standard computational approaches available to identify the collective dynamics scale poorly with the size of the dataset. Here we describe a new, scalable approach to discovering the low-dimensional dynamics that underlie simultaneously recorded spike trains from a neural population. Our method is based on recurrent linear models (RLMs), and relates closely to timeseries models based on recurrent neural networks. We formulate RLMs for neural data by generalising the Kalman-filter-based likelihood calculation for latent linear dynamical systems (LDS) models to incorporate a generalised-linear observation process. We show that RLMs describe motor-cortical population data better than either directly-coupled generalised-linear models or latent linear dynamical system models with generalised-linear observations. We also introduce the cascaded linear model (CLM) to capture low-dimensional instantaneous correlations in neural populations. The CLM describes the cortical recordings better than either Ising or Gaussian models and, like the RLM, can be fit exactly and quickly. The CLM can also be seen as a generalization of a low-rank Gaussian model, in this case factor analysis. The computational tractability of the RLM and CLM allow both to scale to very high-dimensional neural data.

}, url = {http://papers.nips.cc/paper/4877-recurrent-linear-models-of-simultaneously-recorded-neural-populations}, author = {Pachitariu, Marius and Petreska, Biljana and Sahani, Maneesh} } @article {48841, title = {Regularization and nonlinearities for neural language models: when are they needed?}, journal = {arXiv}, year = {2013}, month = {2013 Jan 23}, pages = {arXiv:1301.5650}, abstract = {

Neural language models (LMs) based on recurrent neural networks (RNN) are some of the most successful word and character-level LMs. Why do they work so well, in particular better than linear neural LMs? Possible explanations are that RNNs have an implicitly better regularization or that RNNs have a higher capacity for storing patterns due to their nonlinearities or both. Here we argue for the first explanation in the limit of little training data and the second explanation for large amounts of text data. We show state-of-the-art performance on the popular and small Penn dataset when RNN LMs are regularized with random dropout. Nonetheless, we show even better performance from a simplified, much less expressive linear RNN model without off-diagonal entries in the recurrent matrix. We call this model an impulse-response LM (IRLM). Using random dropout, column normalization and annealed learning rates, IRLMs develop neurons that keep a memory of up to 50 words in the past and achieve a perplexity of 102.5 on the Penn dataset. On two large datasets however, the same regularization methods are unsuccessful for both models and the RNN{\textquoteright}s expressivity allows it to overtake the IRLM by 10 and 20 percent perplexity, respectively. Despite the perplexity gap, IRLMs still outperform RNNs on the Microsoft Research Sentence Completion (MRSC) task. We develop a slightly modified IRLM that separates long-context units (LCUs) from short-context units and show that the LCUs alone achieve a state-of-the-art performance on the MRSC task of 60.8\%. Our analysis indicates that a fruitful direction of research for neural LMs lies in developing more accessible internal representations, and suggests an optimization regime of very high momentum terms for effectively training such models.

}, url = {https://arxiv.org/abs/1301.5650}, author = {Pachitariu, Marius and Sahani, Maneesh} } @conference {48835, title = {Learning visual motion in recurrent neural networks.}, booktitle = {Neural Information Processing Systems (NIPS 2012)}, year = {2012}, month = {12/2003}, address = {Lake Tahoe}, abstract = {

We present a dynamic nonlinear generative model for visual motion based on a latent representation of binary-gated Gaussian variables. Trained on sequences of images, the model learns to represent different movement directions in different variables. We use an online approximate-inference scheme that can be mapped to the dynamics of networks of neurons. Probed with drifting grating stimuli and moving bars of light, neurons in the model show patterns of responses analogous to those of direction-selective simple cells in primary visual cortex. Most model neurons also show speed tuning and respond equally well to a range of motion directions and speeds aligned to the constraint line of their respective preferred speed. We show how these computations are enabled by a specific pattern of recurrent connections learned by the model.

}, url = {http://papers.nips.cc/paper/4814-learning-visual-motion-in-recurrent-neural-networks}, author = {Pachitariu, Marius and Sahani, Maneesh} } @article {5672, title = {Observers exploit stochastic models of sensory change to help judge the passage of time.}, journal = {Current Biology}, volume = {21}, year = {2011}, month = {2011 Feb 8}, pages = {200-6}, abstract = {

Sensory stimulation can systematically bias the perceived passage of time, but why and how this happens is mysterious. In this report, we provide evidence that such biases may ultimately derive from an innate and adaptive use of stochastically evolving dynamic stimuli to help refine estimates derived from internal timekeeping mechanisms. A simplified statistical model based on probabilistic expectations of stimulus change derived from the second-order temporal statistics of the natural environment makes three predictions. First, random noise-like stimuli whose statistics violate natural expectations should induce timing bias. Second, a previously unexplored obverse of this effect is that similar noise stimuli with natural statistics should reduce the variability of timing estimates. Finally, this reduction in variability should scale with the interval being timed, so as to preserve the overall Weber law of interval timing. All three predictions are borne out experimentally. Thus, in the context of our novel theoretical framework, these results suggest that observers routinely rely on sensory input to augment their sense of the passage of time, through a process of Bayesian inference based on expectations of change in the natural environment.

}, keywords = {Biological, Humans, Models, Photic Stimulation, Stochastic Processes, Time Perception}, issn = {1879-0445}, doi = {10.1016/j.cub.2010.12.043}, author = {Ahrens, Misha B and Sahani, Maneesh} } @article {5673, title = {Inferring input nonlinearities in neural encoding models.}, journal = {Network}, volume = {19}, year = {2008}, month = {2008}, pages = {35-67}, abstract = {

We describe a class of models that predict how the instantaneous firing rate of a neuron depends on a dynamic stimulus. The models utilize a learnt pointwise nonlinear transform of the stimulus, followed by a linear filter that acts on the sequence of transformed inputs. In one case, the nonlinear transform is the same at all filter lag-times. Thus, this "input nonlinearity" converts the initial numerical representation of stimulus value to a new representation that provides optimal input to the subsequent linear model. We describe algorithms that estimate both the input nonlinearity and the linear weights simultaneously; and present techniques to regularise and quantify uncertainty in the estimates. In a second approach, the model is generalized to allow a different nonlinear transform of the stimulus value at each lag-time. Although more general, this model is algorithmically more straightforward to fit. However, it has many more degrees of freedom than the first approach, thus requiring more data for accurate estimation. We test the feasibility of these methods on synthetic data, and on responses from a neuron in rodent barrel cortex. The models are shown to predict responses to novel data accurately, and to recover several important neuronal response properties.

}, keywords = {Models, Neural Networks (Computer), Neurological, Neurons, Nonlinear Dynamics}, issn = {1361-6536}, doi = {10.1080/09548980701813936}, author = {Ahrens, Misha B and Paninski, Liam and Sahani, Maneesh} } @article {5505, title = {Nonlinearities and contextual influences in auditory cortical responses modeled with multilinear spectrotemporal methods.}, journal = {The Journal of Neuroscience}, volume = {28}, year = {2008}, month = {2008 Feb 20}, pages = {1929-42}, abstract = {

The relationship between a sound and its neural representation in the auditory cortex remains elusive. Simple measures such as the frequency response area or frequency tuning curve provide little insight into the function of the auditory cortex in complex sound environments. Spectrotemporal receptive field (STRF) models, despite their descriptive potential, perform poorly when used to predict auditory cortical responses, showing that nonlinear features of cortical response functions, which are not captured by STRFs, are functionally important. We introduce a new approach to the description of auditory cortical responses, using multilinear modeling methods. These descriptions simultaneously account for several nonlinearities in the stimulus-response functions of auditory cortical neurons, including adaptation, spectral interactions, and nonlinear sensitivity to sound level. The models reveal multiple inseparabilities in cortical processing of time lag, frequency, and sound level, and suggest functional mechanisms by which auditory cortical neurons are sensitive to stimulus context. By explicitly modeling these contextual influences, the models are able to predict auditory cortical responses more accurately than are STRF models. In addition, they can explain some forms of stimulus dependence in STRFs that were previously poorly understood.

}, keywords = {Acoustic Stimulation, Animals, Auditory Cortex, Biological, Mice, Models, Nonlinear Dynamics, Rats, Time Factors}, issn = {1529-2401}, doi = {10.1523/JNEUROSCI.3377-07.2008}, author = {Ahrens, Misha B and Linden, Jennifer F and Sahani, Maneesh} }