Biology subjects

Iyer, R.

Publications and source records attributed to Iyer, R..

At least 19 recordsLinked to original sources

bioRxiv2025

A Sequential Triple-Drug Strategy for Selective Targeting of p53-Mutant Cancers

The tumor suppressor TP53 gene (p53) is mutated in most human malignancies; however, existing treatment options are largely ineffective, lack selectivity, and cause toxic side effects. To address these clinical problems, we developed a sequential triple-drug strategy for p53 mutant cancer cells. Here we show that a combination of a thymidine analogue (TAS102) plus PARP inhibitor (PARPi) promotes formation of DNA double-strand breaks (DSBs) and G2-arrest specifically in p53 mutant cancer cells. Transcriptome analysis revealed that TAS102-PARPi treatment of p53 mutant cells did not repress DNA replication but activated DSB repair and blocked the mitotic program, consistent with G2-arrest. In contrast, TAS102-PARPi treatment of normal p53 wild-type cells resulted in a temporal G1-arrest and rapid recovery of cell cycle capacity after drug withdrawal. In p53 mutant cancer cells, subsequent blocking of a G2-checkpoint kinase, such as WEE1, released these G2-arrested cells into mitosis, leading to massive cell death. Delayed administration of a G2-kinase inhibitor provides time for p53 wild-type cells to repair DNA, thereby minimizing toxicity to normal tissues. This sequential triple-drug strategy exhibited robust efficacy in preclinical models of colorectal and pancreatic cancers and was well tolerated in mice. Together, our findings illustrate a promising triple-drug strategy for targeting p53 mutant malignancies.

cancer biology↗

bioRxiv2025

Glutamate indicators with increased sensitivity and tailored deactivation rates

Identifying the input-output operations of neurons requires measurements of synaptic transmission simultaneously at many of a neurons thousands of inputs in the intact brain. To facilitate this goal, we engineered and screened 3365 variants of the fluorescent protein glutamate indicator iGluSnFR3 in neuron culture, and selected variants in the mouse visual cortex. Two variants have high sensitivity, fast activation (< 2 ms) and deactivation times tailored for recording large populations of synapses (iGluSnFR4s, 153 ms) or rapid dynamics (iGluSnFR4f, 26 ms). By imaging action-potential evoked signals on axons and visually-evoked signals on dendritic spines, we show that iGluSnFR4s/4f primarily detect local synaptic glutamate with single-vesicle sensitivity. The indicators detect a wide range of naturalistic synaptic transmission, including in the vibrissal cortex layer 4 and in hippocampal CA1 dendrites. iGluSnFR4 increases the sensitivity and scale (4s) or speed (4f) of tracking information flow in neural networks in vivo.

neuroscience↗

bioRxiv2024

SUMO2 Inhibition Reverses Aberrant Epigenetic Rewiring Driven by Synovial Sarcoma Fusion Oncoproteins and Impairs Sarcomagenesis

Synovial Sarcoma (SySa) is an aggressive soft tissue sarcoma that accounts for 5 - 10% of all soft tissue sarcomas. Current treatment involves radiation and radical surgery including limb amputation, highlighting the urgent need to develop targeted therapies. We reasoned that transcriptional rewiring by the fusion protein SS18-SSX, the sole oncogenic driver in SySa, creates specific vulnerabilities that can be exploited for treatment. To uncover genes that are selectively essential for SySa, we mined The Cancer Dependency Map (DepMap) data to identify genes that specifically impact the fitness of SySa compared to other tumor cell lines. Targeted CRISPR library screening of SySa-selective candidates revealed that the small ubiquitin-like modifier 2 (SUMO2) was one of the strongest dependencies both in vitro as well as in vivo. TAK-981, a clinical-stage small molecule SUMO2 inhibitor potently inhibited growth and colony-forming ability. Strikingly, transcriptomic studies showed that pharmacological SUMO2 inhibition with TAK-981 treatment elicited a profound reversal of a gene expression program orchestrated by SS18-SSX fusions. Of note, genetic or pharmacological SUMO2 inhibition reduced global and chromatin levels of the SS18-SSX fusion protein with a concomitant reduction in histone 2A lysine 119 ubiquitination (H2AK119ub), an epigenetic mark that plays an important role in SySa pathogenesis. Taken together, our studies identify SUMO2 as a novel, selective vulnerability in SySa. Since SUMO2 inhibitors are currently in Phase 1/2 clinical trials for other cancers, our findings present a novel avenue for targeted treatment of synovial sarcoma. SIGNIFICANCEOur study identifies SUMO2 as a selective dependency in synovial sarcoma. We demonstrate that the SUMO2/3 inhibitor TAK-981 impairs sarcomagenesis and reverses the SS18-SSX fusion-driven oncotranscriptome. Our study indicates that SUMO2 inhibition may be an attractive therapeutic option in synovial sarcoma.

cancer biology↗

bioRxiv2024

Breast cancer spheroids prefer activated macrophages as an accomplice: An in vitro study

Cancer, a heterogeneous disease in nature, often requires help from diverse pro-tumor or tumor-associated- cells, which are recruited and persevered within the stroma. Pro-tumor stromal cells provide the essential support needed for tumor growth, metastasis, and development of drug resistance in due time. Tumor-associated macrophages, one of such cells, are essential to tumor microenvironment and tumor survival. In recent years, TAMs have been identified as potential drug targets and therapeutic agents, which encourages the in-depth characterization of their crosstalk with the tumors. The current study has successfully developed a cost-effective in vitro platform for Chemokine Assisted Recruitment of Macrophages to spheroids mimicking the physiology of TAM recruitment. Firstly, monocytic cell line (U937) were converted into activated naive macrophages (M0) and pro-and anti-inflammatory (M1 and M2) subtypes. Monocytes, M0, M1, and M2 macrophages are characterized extensively. Secondly, the naive and polarized macrophages were subjected to chemokine-dependent recruitment into monotypic and heterotypic breast cancer spheroids. The nature of the recruitment is further investigated by assessing the profile of chemokines and chemokine receptors. Recruited macrophages are also observed to manipulate spheroid behavior in many ways. The recruited macrophages also exhibit an increased level of Siglec-1 (CD169), one of the potential TAM markers. The current platforms potential for application can be extended to understand the recruitment process of other immune/stromal cells to solid tumors. It could be a potential addition to the arrays of in vitro platforms developed to screen the efficiency of cell-based immunotherapeutics in the future.

cancer biology↗

bioRxiv2024

Physics-driven structural docking and protein language models accelerate antibody screening and design for broad-spectrum antiviral therapy

Therapeutic antibodies have become one of the most influential therapeutics in modern medicine to fight against infectious pathogens, cancer, and many other diseases. However, experimental screening for highly efficacious targeting antibodies is labor-intensive and of high cost, which is exacerbated by evolving antigen targets under selective pressure such as fast-mutating viral variants. As a proof-of-concept, we developed a machine learning-assisted antibody generation pipeline AbGen that greatly accelerates the screening and re-design of immunoglobulins G (IgGs) against a broad spectrum of SARS-CoV-2 coronavirus variant strains. Our AbGen centers around a novel antibody language model (AbLM) that is pretrained on 12 million generic protein domain sequences and fine-tuned on 4,000+ paired VH-VL sequences, with IgG-specific CDR-masking and VH-VL cross-attention. AbLM provides a latent space of IgG sequence embeddings for AbGen, including (a) landscapes of IgGs activities in neutralizing the wild-type virus are analyzed through structure prediction for IgG and IgG-antigen (viral protein spikes receptor binding domain, RBD) interactions; and (b) landscapes of IgGs susceptibility in neutralizing variant viruses are predicted through Gaussian process regression, despite that as few as 14 clinical antibodies responses to variants of concern are available. The AbGen pipeline was applied to over 1300 IgG sequences we collected from RBD-binding B cells of convalescent patients. With experimental validations, AbGen efficiently prioritized IgG candidates against a broad spectrum of viral variants (wildtype, Delta, and Omicron), preventing the infection of host cells in vitro and hACE2 transgenic mice in vivo. Compared to other existing protein language models that require 10-100 times more model parameters, AbLM improved the precision from around 50% to 75% to predict IgGs with low variant susceptibility. Furthermore, AbGen enables structure-based computational protein redesign for selected IgG clones with single amino acid substitutions at the RBD-binding interface that doubled the IgG blockade efficacy for one of the severe, therapy-resistant strains - Delta (B.1.617). Our work expedites applications of artificial intelligence in antibody screen and re- design combining data-driven protein language models and Kriging for antibody sequence analysis and activity prediction, in synergy with physics-driven protein docking and design for antibody-antigen interface analyses and functional optimization.

immunology↗

bioRxiv2023

Neural patterns associated with mixed valence feelings differ in consistency and predictability throughout the brain

Mixed valence feelings, defined by the simultaneous presence of positive and negative affect, remain understudied in neuroscience. We used functional magnetic resonance imaging to investigate subjects watching an animated short film known to induce mixed feelings. These same subjects were asked to identify the time periods during which they had experienced positive, negative, and mixed feelings. Using Hidden-Markov models, we found that activity in the insula, amygdala, nucleus accumbens, and anterior cingulate allowed us to predict the onset of new feeling states as determined by individual self-report. Further analyses found spatiotemporally unique and consistent neural patterns in the insular cortex for univalent states, but not for mixed valence states. However, ventromedial prefrontal cortex and anterior cingulate exhibited unique neural consistency for both univalent and mixed valence states. This study is the first to reveal direct evidence for a neurally consistent representation of mixed feelings in the central nervous system.

neuroscience↗

bioRxiv2023

Whole genome sequencing of Borrelia burgdorferi isolates reveals linked clusters of plasmid-borne accessory genome elements associated with virulence.

Lyme disease is the most common vector-borne disease in North America and Europe. The clinical manifestations of Lyme disease vary based on the genospecies of the infecting Borrelia burgdorferi spirochete, but the microbial genetic elements underlying these associations are not known. Here, we report the whole genome sequence (WGS) and analysis of 299 patient-derived B. burgdorferi sensu stricto (Bbss) isolates from patients in the Eastern and Midwestern US and Central Europe. We develop a WGS-based classification of Bbss isolates, confirm and extend the findings of previous single- and multi-locus typing systems, define the plasmid profiles of human-infectious Bbss isolates, annotate the core and strain-variable surface lipoproteome, and identify loci associated with disseminated infection. A core genome consisting of [~]800 open reading frames and a core set of plasmids consisting of lp17, lp25, lp36, lp28-3, lp28-4, lp54, and cp26 are found in nearly all isolates. Strain-variable (accessory) plasmids and genes correlate strongly with phylogeny. Using genetic association study methods, we identify an accessory genome signature associated with dissemination and define the individual plasmids and genes that make up this signature. Strains within the RST1/WGS A subgroup, particularly a subset marked by the OspC type A genotype, are associated with increased rates of dissemination. OspC type A strains possess a unique constellation of strongly linked genetic changes including the presence of lp56 and lp28-1 plasmids and a cluster of genes that may contribute to their enhanced virulence compared to other genotypes. The patterns of OspC type A strains typify a broader paradigm across Bbss isolates, in which genetic structure is defined by correlated groups of strain-variable genes located predominantly on plasmids, particularly for expression of surface-exposed lipoproteins. These clusters of genes are inherited in blocks through strain-specific patterns of plasmid occupancy and are associated with the probability of invasive infection.

microbiology↗

bioRxiv2021

Geometry of inter-areal interactions in mouse visual cortex

The response of a set of neurons in an area is the result of the sensory input, the interaction of the neurons within the area as well as the long range interactions between areas. We aimed to study the relation between interactions among multiple areas, and if they are fixed or dynamic. The structural connectivity provides a substrate for these interactions, but anatomical connectivity is not known in sufficient detail and it only gives us a static picture. Using the Allen Brain Observatory Visual Coding Neuropixels dataset, which includes simultaneous recordings of spiking activity from up to 6 hierarchically organized mouse cortical visual areas, we estimate the functional connectivity between neurons using a linear model of responses to flashed static grating stimuli. We characterize functional connectivity between populations via interaction subspaces. We find that distinct subspaces of a source area mediate interactions with distinct target areas, supporting the notion that cortical areas use distinct channels to communicate. Most importantly, using a piecewise linear model for activity within each trial, we find that these interactions evolve dynamically over tens of milliseconds following a stimulus presentation. Inter-areal subspaces become more aligned with the intra-areal subspaces during epochs in which a feedforward wave of activity propagates through visual cortical areas. When the short-term dynamics are averaged over, we find that the interaction subspaces are stable over multiple stimulus blocks. These findings have important implications for understanding how information flows through biological neural networks composed of interconnected modules, each of which may have a distinct functional specialization.

neuroscience↗

bioRxiv2020

Modulation of MHC-E transport by viral decoy ligands is required for RhCMV/SIV vaccine efficacy

Strain 68-1 rhesus cytomegalovirus (RhCMV) vectors expressing simian immunodeficiency virus (SIV) antigens elicit CD8+ T cells that recognize peptide epitopes presented by major histocompatibility complex (MHC)-II and MHC-E molecules, instead of MHC-Ia, and are uniquely able to mediate stringent control and subsequent clearance of highly pathogenic SIV in [~]50% of vaccinated rhesus macaques (RMs). We show that the MHC-E ligand VMAPRTLLL (VL9), encoded by the Rh67 gene (or its HCMV UL40 counterpart) is required for recognition of RhCMV-infected fibroblasts by MHC-E-restricted CD8+ T cells via its ability to promote intracellular MHC-E transport. Moreover, deletion of Rh67 from 68-1 RhCMV/SIV vectors, or mutation of its embedded VL9 ligand, abrogated induction of MHC-E-restricted CD8+ T cell responses, leaving responses that exclusively target MHC-II-restricted epitopes. These MHC-II-presented CD8+ T cell responses, though comparable in response magnitude and functional differentiation to responses arising from the efficacious 68-1 vector, did not protect RMs against SIV challenge, indicating that Rh67/UL40-enabled direct priming of MHC-E-targeted CD8+ T cells is a crucial element of RhCMV/SIV vaccine efficacy. One Sentence SummaryA cytomegalovirus protein (Rh67/UL40) that upregulates MHC-E expression on RhCMV/SIV-vector infected cells is required for induction of MHC-E-restricted CD8+ T cells and for protection against SIV.

immunology↗

bioRxiv2020

Exploiting oxalotrophic bacteria to control Aspergillus niger lung infections

Aspergillus fungi are opportunistic pathogens that affect a large number of people worldwide. Many aspects of Aspergillus spp. pathogenesis toward humans are known, but their ability to enhance their infectious potential by manipulating the environmental pH of its host has not been considered yet. In this study, we tested the hypothesis that by producing oxalic acid, Aspergillus niger can manipulate pH during lung infection and thus, interfering with this process could limit pathogenicity. To test this hypothesis, we co-cultured A. niger with oxalotrophic bacteria in increasingly complex testing systems (Petri dishes and 3D-cell cultures systems). In in vitro tests, oxalotrophic bacteria limit oxalic acid production and suppressed the pH shift induced by A. niger. In 3D-cell cultures (Transwells(R) and Bronchioles-on-a-chip), A. niger also modified pH, Ca2+ and oxalic acid concentrations. Co-inoculation with as little as 10 cells of the oxalatrophic bacterium strongly inhibited the germination and development of A. niger and returned each of the three parameters to the baseline physiological values of uninfected cells. This biocontrol interaction between oxalotrophic bacteria and oxalate-producing A. niger could represent a paradigm shift in the fight against opportunistic fungal pathogens, where the host environment is rendered less permissive to fungal development.

microbiology↗

bioRxiv2019

Transformation of population code from dLGN to V1 facilitates linear decoding

How neural populations represent sensory information, and how that representation is transformed from one brain area to another, are fundamental questions of neuroscience. The dorsolateral geniculate nucleus (dLGN) and primary visual cortex (V1) represent two distinct stages of early visual processing. Classic sparse coding theories propose that V1 neurons represent local features of images. More recent theories have argued that the visual pathway transforms visual representations to become increasingly linearly separable. To test these ideas, we simultaneously recorded the spiking activity of mouse dLGN and V1 in vivo. We find strong evidence for both sparse coding and linear separability theories. Surprisingly, the correlations between neurons in V1 (but not dLGN) were shaped as to be irrelevant for stimulus decoding, a feature which we show enables linear separability. Therefore, our results suggest that the dLGN-V1 transformation reshapes correlated variability in a manner that facilitates linear decoding while producing a sparse code.

neuroscience↗

bioRxiv2019

Widespread presence of direction-reversing neurons in the mouse visual system

Direction selectivity - the preference of motion in one direction over the opposite - is a fundamental property of visual neurons across species. We find that a substantial proportion of direction selective neurons in the mouse visual system reverse their preferred direction of motion in response to drifting gratings at different spatiotemporal parameters. A spatiotemporally asymmetric filter model recapitulates our experimental observations.

neuroscience↗

bioRxiv2019

A survey of spiking activity reveals a functional hierarchy of mouse corticothalamic visual areas

The mammalian visual system, from retina to neocortex, has been extensively studied at both anatomical and functional levels. Anatomy indicates the cortico-thalamic system is hierarchical, but characterization of cellular-level functional interactions across multiple levels of this hierarchy is lacking, partially due to the challenge of simultaneously recording activity across numerous regions. Here, we describe a large, open dataset (part of the Allen Brain Observatory) that surveys spiking from units in six cortical and two thalamic regions responding to a battery of visual stimuli. Using spike cross-correlation analysis, we find that inter-area functional connectivity mirrors the anatomical hierarchy from the Allen Mouse Brain Connectivity Atlas. Classical functional measures of hierarchy, including visual response latency, receptive field size, phase-locking to a drifting grating stimulus, and autocorrelation timescale are all correlated with the anatomical hierarchy. Moreover, recordings during a visual task support the behavioral relevance of hierarchical processing. Overall, this dataset and the hierarchy we describe provide a foundation for understanding coding and dynamics in the mouse cortico-thalamic visual system.

neuroscience↗

bioRxiv2019

Does the neuronal noise in cortex help generalization?

One remarkable feature of neuronal activity in the mammalian cortex is the high level of variability in response to repeated stimuli. First, we used an open dataset, the Allen Brain Observatory, to quantify the distribution of responses to repeated presentations of natural movies. We find that even for their preferred moment in the movie clip, neurons have high variability which cannot be well captured by Gaussian or Poisson distributions. A large fraction of responses are better fit by log-normal or Gaussian mixture models with two components. These distributions are similar to activity distributions during training of deep neural networks using dropout. This poses the interesting hypothesis: is the role of cortical noise to help in generalization during learning?\n\nSecond, to ensure the robustness of our results we analyzed electrophysiological recordings in the same areas of mouse visual cortex, again using repeated natural movie presentations and found similar response distributions. To make sure that the trial-by-trial variations we observe are not due exclusively to the result of changes in state, we constructed a population coupling model, where each neurons activity is coupled to a low-dimension version of the activity of all other simultaneously recorded neurons. The population coupling model can capture global, brain-wide activity fluctuations that are state-dependent. The residuals from this model also show non-Gaussian noise distributions.\n\nThird, we ask a more specific question: is the noise in the cortex more likely to move the representation of the stimulus in-class versus out-of-class? To address this question, we analyzed the responses of neurons across trials from multiple sections of different movie clips. We observe that the noise in the cortex better aligns to in-class variations. We argue that a useful noise for learning generalizations is to move from representations of different exemplars in-class, similar to cortical noise.

neuroscience↗

bioRxiv2019

Systematic Integration of Structural and Functional Data into Multi-Scale Models of Mouse Primary Visual Cortex

AO_SCPLOWBSTRACTC_SCPLOWStructural rules underlying functional properties of cortical circuits are poorly understood. To explore these rules systematically, we integrated information from extensive literature curation and large-scale experimental surveys into a data-driven, biologically realistic model of the mouse primary visual cortex. The model was constructed at two levels of granularity, using either biophysically-detailed or point-neurons, with identical network connectivity. Both variants were compared to each other and to experimental recordings of neural activity during presentation of visual stimuli to awake mice. While constructing and tuning these networks to recapitulate experimental data, we identified a set of rules governing cell-class specific connectivity and synaptic strengths. These structural constraints constitute hypotheses that can be tested experimentally. Despite their distinct single cell abstraction, spatially extended or point-models, both perform similarly at the level of firing rate distributions. All data and models are freely available as a resource for the community.

neuroscience↗

bioRxiv2018

Predicting Brain Age Using Structural Neuroimaging and Deep Learning

Early detection of age-related diseases will greatly benefit from a model of the underlying biological aging process. In this paper, we develop a brain-age predictor by using structural magnetic resonance imaging (SMRI) and deep learning and evaluate the predicted brain age as a marker of brain-aging in Alzheimer's disease. Our approach does not require any domain knowledge in that it trains end-to-end on the SMRI image itself, and has been validated on real SMRI data collected from elderly subjects. We developed two different models by using convolutional neural network (CNN) based regression and bucket classification to predict brain ages from SMRI images. Our models achieved root mean squared errors (RMSE) of 5.54 and 6.44 years in predicting brain ages of healthy subjects. Further analysis showed that there is a substantial difference between the predicted brain ages of cognitively impaired and healthy subjects with similar chronological ages.

bioengineering↗

bioRxiv2018

Comparing DNA, RNA and protein levels for measuring microbial activity in nitrogen-amended soils

Multi-omic techniques can offer a comprehensive overview of microbial communities at the gene, transcript and protein levels. However, to what extent these levels reflect in situ process rates is less clear, especially in highly complex habitats such as soils. Here we performed microcosm incubations using soil from a site with a history of agricultural management. Microcosms, amended with isotopically labelled ammonium and urea to simulate a fertilization event, showed nitrification (up to 4.1 {+/-} 0.87 {micro}g N-NO3- g-1 dry soil d-1) and accumulation of N2O after 192 hours of incubation. Nitrification activity (NH4+[->]NH2OH[->]NO2-[->]NO3-) was accompanied by a 6-fold increase in relative expression of the 16S rRNA gene (RNA/DNA) between 10 and 192 hours of incubation for ammonia-oxidizing bacteria (AOB) Nitrosomonas and Nitrosospira. In contrast, ammonia-oxidizing archaea (AOA) and complete ammonia oxidizer (comammox) nitrifiers showed stable gene expression during incubations but were generally more abundant (DNA level) than their Betaproteobacteria AOB counterparts. A strong relationship between nitrification activity and (mostly) betaproteobacterial ammonia monooxygenase (amoA; NH4+[->]NH2OH) and nitrite oxidoreductase (nxrA; NO2-[->]NO3-) transcript abundances revealed that mRNA levels quantitatively reflected measured activity and were generally more sensitive than the DNA level in the microcosm incubations. Although peptides related to housekeeping proteins from nitrite-oxidizing microorganisms were detected, their abundance was not significantly correlated with activity, revealing that meta-proteomics provided only a qualitative assessment of activity. Altogether, these findings underscore the strengths and limitations of multi-omic approaches for assessing complex microbial communities and provide the molecular means to assess nitrification processes in soils.\n\nIMPORTANCEEven though the use of omic approaches has expanded our knowledge of the diversity of microbial communities in natural and engineered systems, it is less clear how well the use of whole community DNA-, RNA- or protein-based approaches reflect microbial activities. To this end, we directly compared the different levels of molecular information (i.e., DNA, RNA or proteins) in order to assess which level best correlated with isotope-based measurements of nitrification activity in agricultural soils after fertilization. This work reveals the strengths as well as the associated limitations of metagenomic, metatranscriptomic, and metaproteomic approaches in serving as reliable proxies for examining microbial activities in highly diverse environments like soils.

microbiology↗

bioRxiv2018

Visual physiology of the Layer 4 cortical circuit in silico

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.\n\nAUTHOR SUMMARYHow can we capture the incredible complexity of brain circuits in quantitative models, and what can such models teach us about mechanisms underlying brain activity? To answer these questions, we set out to build extensive, bio-realistic models of brain circuitry employing systematic datasets on brain structure and function. Here we report the first modeling results of this project, focusing on the layer 4 of the primary visual cortex (V1) of the mouse. Our simulations reproduced a variety of experimental observations in a large battery of visual stimuli. The results elucidated circuit mechanisms determining patters of neuronal activity in layer 4 - in particular, the roles of feedforward thalamic inputs and specific patterns of intracortical connectivity in producing tuning of neuronal responses to the orientation of motion. Simplification of neuronal models led to specific deficiencies in reproducing experimental data, giving insights into how biological details contribute to various aspects of brain activity. To enable future development of more sophisticated models, we make the software code, the model, and simulation results publicly available.

neuroscience↗