R-3 Repository :: Browsing by Author "Tolias, Andreas S."

Browsing by Author "Tolias, Andreas S."

Now showing 1 - 12 of 12

A CRISPR toolbox for generating intersectional genetic mouse models for functional, molecular, and anatomical circuit mapping
(Springer Nature, 2022) Lusk, Savannah J.; McKinney, Andrew; Hunt, Patrick J.; Fahey, Paul G.; Patel, Jay; Chang, Andersen; Sun, Jenny J.; Martinez, Vena K.; Zhu, Ping Jun; Egbert, Jeremy R.; Allen, Genevera; Jiang, Xiaolong; Arenkiel, Benjamin R.; Tolias, Andreas S.; Costa-Mattioli, Mauro; Ray, Russell S.
The functional understanding of genetic interaction networks and cellular mechanisms governing health and disease requires the dissection, and multifaceted study, of discrete cell subtypes in developing and adult animal models. Recombinase-driven expression of transgenic effector alleles represents a significant and powerful approach to delineate cell populations for functional, molecular, and anatomical studies. In addition to single recombinase systems, the expression of two recombinases in distinct, but partially overlapping, populations allows for more defined target expression. Although the application of this method is becoming increasingly popular, its experimental implementation has been broadly restricted to manipulations of a limited set of common alleles that are often commercially produced at great expense, with costs and technical challenges associated with production of intersectional mouse lines hindering customized approaches to many researchers. Here, we present a simplified CRISPR toolkit for rapid, inexpensive, and facile intersectional allele production.
Binary and analog variation of synapses between cortical pyramidal neurons
(eLife Sciences Publications Ltd., 2022) Dorkenwald, Sven; Turner, Nicholas L.; Macrina, Thomas; Lee, Kisuk; Lu, Ran; Wu, Jingpeng; Bodor, Agnes L.; Bleckert, Adam A.; Brittain, Derrick; Kemnitz, Nico; Silversmith, William M.; Ih, Dodam; Zung, Jonathan; Zlateski, Aleksandar; Tartavull, Ignacio; Yu, Szi-Chieh; Popovych, Sergiy; Wong, William; Castro, Manuel; Jordan, Chris S.; Wilson, Alyssa M.; Froudarakis, Emmanouil; Buchanan, JoAnn; Takeno, Marc M.; Torres, Russel; Mahalingam, Gayathri; Collman, Forrest; Schneider-Mizell, Casey M.; Bumbarger, Daniel J.; Li, Yang; Becker, Lynne; Suckow, Shelby; Reimer, Jacob; Tolias, Andreas S.; Macarico da Costa, Nuno; Reid, R. Clay; Seung, H. Sebastian
Learning from experience depends at least in part on changes in neuronal connections. We present the largest map of connectivity to date between cortical neurons of a defined type (layer 2/3 [L2/3] pyramidal cells in mouse primary visual cortex), which was enabled by automated analysis of serial section electron microscopy images with improved handling of image defects (250 × 140 × 90 μm3 volume). We used the map to identify constraints on the learning algorithms employed by the cortex. Previous cortical studies modeled a continuum of synapse sizes by a log-normal distribution. A continuum is consistent with most neural network models of learning, in which synaptic strength is a continuously graded analog variable. Here, we show that synapse size, when restricted to synapses between L2/3 pyramidal cells, is well modeled by the sum of a binary variable and an analog variable drawn from a log-normal distribution. Two synapses sharing the same presynaptic and postsynaptic cells are known to be correlated in size. We show that the binary variables of the two synapses are highly correlated, while the analog variables are not. Binary variation could be the outcome of a Hebbian or other synaptic plasticity rule depending on activity signals that are relatively uniform across neuronal arbors, while analog variation may be dominated by other influences such as spontaneous dynamical fluctuations. We discuss the implications for the longstanding hypothesis that activity-dependent plasticity switches synapses between bistable states.
Decoding Depression Severity From Intracranial Neural Activity
(Elsevier, 2023) Xiao, Jiayang; Provenza, Nicole R.; Asfouri, Joseph; Myers, John; Mathura, Raissa K.; Metzger, Brian; Adkinson, Joshua A.; Allawala, Anusha B.; Pirtle, Victoria; Oswalt, Denise; Shofty, Ben; Robinson, Meghan E.; Mathew, Sanjay J.; Goodman, Wayne K.; Pouratian, Nader; Schrater, Paul R.; Patel, Ankit B.; Tolias, Andreas S.; Bijanki, Kelly R.; Pitkow, Xaq; Sheth, Sameer A.
Background Disorders of mood and cognition are prevalent, disabling, and notoriously difficult to treat. Fueling this challenge in treatment is a significant gap in our understanding of their neurophysiological basis. Methods We recorded high-density neural activity from intracranial electrodes implanted in depression-relevant prefrontal cortical regions in 3 human subjects with severe depression. Neural recordings were labeled with depression severity scores across a wide dynamic range using an adaptive assessment that allowed sampling with a temporal frequency greater than that possible with typical rating scales. We modeled these data using regularized regression techniques with region selection to decode depression severity from the prefrontal recordings. Results Across prefrontal regions, we found that reduced depression severity is associated with decreased low-frequency neural activity and increased high-frequency activity. When constraining our model to decode using a single region, spectral changes in the anterior cingulate cortex best predicted depression severity in all 3 subjects. Relaxing this constraint revealed unique, individual-specific sets of spatiospectral features predictive of symptom severity, reflecting the heterogeneous nature of depression. Conclusions The ability to decode depression severity from neural activity increases our fundamental understanding of how depression manifests in the human brain and provides a target neural signature for personalized neuromodulation therapies.
Deep convolutional models improve predictions of macaque V1 responses to natural images
(Public Library of Science, 2019) Cadena, Santiago A.; Denfield, George H.; Walker, Edgar Y.; Gatys, Leon A.; Tolias, Andreas S.; Bethge, Matthias; Ecker, Alexander S.
Despite great efforts over several decades, our best models of primary visual cortex (V1) still predict spiking activity quite poorly when probed with natural stimuli, highlighting our limited understanding of the nonlinear computations in V1. Recently, two approaches based on deep learning have emerged for modeling these nonlinear computations: transfer learning from artificial neural networks trained on object recognition and data-driven convolutional neural network models trained end-to-end on large populations of neurons. Here, we test the ability of both approaches to predict spiking activity in response to natural images in V1 of awake monkeys. We found that the transfer learning approach performed similarly well to the data-driven approach and both outperformed classical linear-nonlinear and wavelet-based feature representations that build on existing theories of V1. Notably, transfer learning using a pre-trained feature space required substantially less experimental time to achieve the same performance. In conclusion, multi-layer convolutional neural networks (CNNs) set the new state of the art for predicting neural responses to natural images in primate V1 and deep features learned for object recognition are better explanations for V1 computation than all previous filter bank theories. This finding strengthens the necessity of V1 models that are multiple nonlinearities away from the image domain and it supports the idea of explaining early visual cortex based on high-level functional goals.
Distinct organization of two cortico-cortical feedback pathways
(Springer Nature, 2022) Shen, Shan; Jiang, Xiaolong; Scala, Federico; Fu, Jiakun; Fahey, Paul; Kobak, Dmitry; Tan, Zhenghuan; Zhou, Na; Reimer, Jacob; Sinz, Fabian; Tolias, Andreas S.
Neocortical feedback is critical for attention, prediction, and learning. To mechanically understand its function requires deciphering its cell-type wiring. Recent studies revealed that feedback between primary motor to primary somatosensory areas in mice is disinhibitory, targeting vasoactive intestinal peptide-expressing interneurons, in addition to pyramidal cells. It is unknown whether this circuit motif represents a general cortico-cortical feedback organizing principle. Here we show that in contrast to this wiring rule, feedback between higher-order lateromedial visual area to primary visual cortex preferentially activates somatostatin-expressing interneurons. Functionally, both feedback circuits temporally sharpen feed-forward excitation eliciting a transient increase–followed by a prolonged decrease–in pyramidal cell activity under sustained feed-forward input. However, under feed-forward transient input, the primary motor to primary somatosensory cortex feedback facilitates bursting while lateromedial area to primary visual cortex feedback increases time precision. Our findings argue for multiple cortico-cortical feedback motifs implementing different dynamic non-linear operations.
Diverse task-driven modeling of macaque V4 reveals functional specialization towards semantic tasks
(Public Library of Science, 2024) Cadena, Santiago A.; Willeke, Konstantin F.; Restivo, Kelli; Denfield, George; Sinz, Fabian H.; Bethge, Matthias; Tolias, Andreas S.; Ecker, Alexander S.
Responses to natural stimuli in area V4—a mid-level area of the visual ventral stream—are well predicted by features from convolutional neural networks (CNNs) trained on image classification. This result has been taken as evidence for the functional role of V4 in object classification. However, we currently do not know if and to what extent V4 plays a role in solving other computational objectives. Here, we investigated normative accounts of V4 (and V1 for comparison) by predicting macaque single-neuron responses to natural images from the representations extracted by 23 CNNs trained on different computer vision tasks including semantic, geometric, 2D, and 3D types of tasks. We found that V4 was best predicted by semantic classification features and exhibited high task selectivity, while the choice of task was less consequential to V1 performance. Consistent with traditional characterizations of V4 function that show its high-dimensional tuning to various 2D and 3D stimulus directions, we found that diverse non-semantic tasks explained aspects of V4 function that are not captured by individual semantic tasks. Nevertheless, jointly considering the features of a pair of semantic classification tasks was sufficient to yield one of our top V4 models, solidifying V4’s main functional role in semantic processing and suggesting that V4’s selectivity to 2D or 3D stimulus properties found by electrophysiologists can result from semantic functional goals.
Revealing nonlinear neural decoding by analyzing choices
(Springer Nature, 2021) Yang, Qianli; Walker, Edgar; Cotton, R. James; Tolias, Andreas S.; Pitkow, Xaq
Sensory data about most natural task-relevant variables are entangled with task-irrelevant nuisance variables. The neurons that encode these relevant signals typically constitute a nonlinear population code. Here we present a theoretical framework for quantifying how the brain uses or decodes its nonlinear information. Our theory obeys fundamental mathematical limitations on information content inherited from the sensory periphery, describing redundant codes when there are many more cortical neurons than primary sensory neurons. The theory predicts that if the brain uses its nonlinear population codes optimally, then more informative patterns should be more correlated with choices. More specifically, the theory predicts a simple, easily computed quantitative relationship between fluctuating neural activity and behavioral choices that reveals the decoding efficiency. This relationship holds for optimal feedforward networks of modest complexity, when experiments are performed under natural nuisance variation. We analyze recordings from primary visual cortex of monkeys discriminating the distribution from which oriented stimuli were drawn, and find these data are consistent with the hypothesis of near-optimal nonlinear decoding.
Robust deep learning object recognition models rely on low frequency information in natural images
(PLOS, 2023) Li, Zhe; Caro, Josue Ortega; Rusak, Evgenia; Brendel, Wieland; Bethge, Matthias; Anselmi, Fabio; Patel, Ankit B.; Tolias, Andreas S.; Pitkow, Xaq
Machine learning models have difficulty generalizing to data outside of the distribution they were trained on. In particular, vision models are usually vulnerable to adversarial attacks or common corruptions, to which the human visual system is robust. Recent studies have found that regularizing machine learning models to favor brain-like representations can improve model robustness, but it is unclear why. We hypothesize that the increased model robustness is partly due to the low spatial frequency preference inherited from the neural representation. We tested this simple hypothesis with several frequency-oriented analyses, including the design and use of hybrid images to probe model frequency sensitivity directly. We also examined many other publicly available robust models that were trained on adversarial images or with data augmentation, and found that all these robust models showed a greater preference to low spatial frequency information. We show that preprocessing by blurring can serve as a defense mechanism against both adversarial attacks and common corruptions, further confirming our hypothesis and demonstrating the utility of low spatial frequency information in robust object recognition.
Structure and function of axo-axonic inhibition
(eLife Sciences Publications Ltd, 2021) Schneider-Mizell, Casey M.; Bodor, Agnes L.; Collman, Forrest; Brittain, Derrick; Bleckert, Adam; Dorkenwald, Sven; Turner, Nicholas L.; Macrina, Thomas; Lee, Kisuk; Lu, Ran; Wu, Jingpeng; Zhuang, Jun; Nandi, Anirban; Hu, Brian; Buchanan, JoAnn; Takeno, Marc M.; Torres, Russel; Mahalingam, Gayathri; Bumbarger, Daniel J.; Li, Yang; Chartrand, Thomas; Kemnitz, Nico; Silversmith, William M.; Ih, Dodam; Zung, Jonathan; Zlateski, Aleksandar; Tartavull, Ignacio; Popovych, Sergiy; Wong, William; Castro, Manuel; Jordan, Chris S.; Froudarakis, Emmanouil; Becker, Lynne; Suckow, Shelby; Reimer, Jacob; Tolias, Andreas S.; Anastassiou, Costas A.; Seung, H. Sebastian; Reid, R. Clay; Costa, Nuno Maçarico da
Inhibitory neurons in mammalian cortex exhibit diverse physiological, morphological, molecular, and connectivity signatures. While considerable work has measured the average connectivity of several interneuron classes, there remains a fundamental lack of understanding of the connectivity distribution of distinct inhibitory cell types with synaptic resolution, how it relates to properties of target cells, and how it affects function. Here, we used large-scale electron microscopy and functional imaging to address these questions for chandelier cells in layer 2/3 of the mouse visual cortex. With dense reconstructions from electron microscopy, we mapped the complete chandelier input onto 153 pyramidal neurons. We found that synapse number is highly variable across the population and is correlated with several structural features of the target neuron. This variability in the number of axo-axonic ChC synapses is higher than the variability seen in perisomatic inhibition. Biophysical simulations show that the observed pattern of axo-axonic inhibition is particularly effective in controlling excitatory output when excitation and inhibition are co-active. Finally, we measured chandelier cell activity in awake animals using a cell-type-specific calcium imaging approach and saw highly correlated activity across chandelier cells. In the same experiments, in vivo chandelier population activity correlated with pupil dilation, a proxy for arousal. Together, these results suggest that chandelier cells provide a circuit-wide signal whose strength is adjusted relative to the properties of target neurons.
Sustained deep-tissue voltage recording using a fast indicator evolved for two-photon microscopy
(Elsevier, 2022) Liu, Zhuohe; Lu, Xiaoyu; Villette, Vincent; Gou, Yueyang; Colbert, Kevin L.; Lai, Shujuan; Guan, Sihui; Land, Michelle A.; Lee, Jihwan; Assefa, Tensae; Zollinger, Daniel R.; Korympidou, Maria M.; Vlasits, Anna L.; Pang, Michelle M.; Su, Sharon; Cai, Changjia; Froudarakis, Emmanouil; Zhou, Na; Patel, Saumil S.; Smith, Cameron L.; Ayon, Annick; Bizouard, Pierre; Bradley, Jonathan; Franke, Katrin; Clandinin, Thomas R.; Giovannucci, Andrea; Tolias, Andreas S.; Reimer, Jacob; Dieudonné, Stéphane; St-Pierre, François
Genetically encoded voltage indicators are emerging tools for monitoring voltage dynamics with cell-type specificity. However, current indicators enable a narrow range of applications due to poor performance under two-photon microscopy, a method of choice for deep-tissue recording. To improve indicators, we developed a multiparameter high-throughput platform to optimize voltage indicators for two-photon microscopy. Using this system, we identified JEDI-2P, an indicator that is faster, brighter, and more sensitive and photostable than its predecessors. We demonstrate that JEDI-2P can report light-evoked responses in axonal termini of Drosophila interneurons and the dendrites and somata of amacrine cells of isolated mouse retina. JEDI-2P can also optically record the voltage dynamics of individual cortical neurons in awake behaving mice for more than 30 min using both resonant-scanning and ULoVE random-access microscopy. Finally, ULoVE recording of JEDI-2P can robustly detect spikes at depths exceeding 400 μm and report voltage correlations in pairs of neurons.
Three types of incremental learning
(Springer Nature, 2022) van de Ven, Gido M.; Tuytelaars, Tinne; Tolias, Andreas S.
Incrementally learning new information from a non-stationary stream of data, referred to as ‘continual learning’, is a key feature of natural intelligence, but a challenging problem for deep neural networks. In recent years, numerous deep learning methods for continual learning have been proposed, but comparing their performances is difficult due to the lack of a common framework. To help address this, we describe three fundamental types, or ‘scenarios’, of continual learning: task-incremental, domain-incremental and class-incremental learning. Each of these scenarios has its own set of challenges. To illustrate this, we provide a comprehensive empirical comparison of currently used continual learning strategies, by performing the Split MNIST and Split CIFAR-100 protocols according to each scenario. We demonstrate substantial differences between the three scenarios in terms of difficulty and in terms of the effectiveness of different strategies. The proposed categorization aims to structure the continual learning field, by forming a key foundation for clearly defining benchmark problems.
Understanding Robustness and Generalization of Artificial Neural Networks Through Fourier Masks
(Frontiers Media S.A., 2022) Karantzas, Nikos; Besier, Emma; Ortega Caro, Josue; Pitkow, Xaq; Tolias, Andreas S.; Patel, Ankit B.; Anselmi, Fabio
Despite the enormous success of artificial neural networks (ANNs) in many disciplines, the characterization of their computations and the origin of key properties such as generalization and robustness remain open questions. Recent literature suggests that robust networks with good generalization properties tend to be biased toward processing low frequencies in images. To explore the frequency bias hypothesis further, we develop an algorithm that allows us to learn modulatory masks highlighting the essential input frequencies needed for preserving a trained network's performance. We achieve this by imposing invariance in the loss with respect to such modulations in the input frequencies. We first use our method to test the low-frequency preference hypothesis of adversarially trained or data-augmented networks. Our results suggest that adversarially robust networks indeed exhibit a low-frequency bias but we find this bias is also dependent on directions in frequency space. However, this is not necessarily true for other types of data augmentation. Our results also indicate that the essential frequencies in question are effectively the ones used to achieve generalization in the first place. Surprisingly, images seen through these modulatory masks are not recognizable and resemble texture-like patterns.

Browsing by Author "Tolias, Andreas S."

Results Per Page

Sort Options