Biology subjects

Sahani, M.

Publications and source records attributed to Sahani, M..

At least 19 recordsLinked to original sources

bioRxiv2026

Production of a resilient biosurfactant using agro-wastes and Paenibacillus dendritiformis isolated from petroleum-contaminated soil: a break-even analysis at pilot scale

Wastes from mustard oil mills and dairy were used as partial replacement of pure reagents in modified Bushnell and Haas broth media for production of biosurfactant by Paenibacillus dendritiformis isolated from petroleum-contaminated soil. The process was optimised by construction of a response model using Taguchi experimental design, leading to a 28.23% increase in performance characteristics and a 13.23 % increase in yield of biosurfactant. The optimized production medium consisted of carbon @7.9 %, nitrogen @ 1.1% and agitation rate 180 r.p.m. This was provided by adding glucose @ 2%, ammonium nitrate @0.2%, mustard oil seed meal @10% and whey@ 10% in modified Bushnell Haas broth. The biosurfactant produced retained its properties during exposure to a wide range of pH values (5-11), high temperatures (up to 121C), high salinities of up to 5% and also five -fold concentration. Approximately three- fold increase in yield of the biosurfactant (25g/L to 68.5g/L) was obtained upon scaling-up the production from 100ml to 1500 ml in a lab scale fermenter with agitation @ 150r.p.m. and D.O. levels maintained at 6mg/L. Power calculations and the cost thereof was calculated (Rs 10.24 /L) and summed up with the cost of raw materials Rs 8.40/L and found to be Rs 18.64/L of product .Since 68.5g product is present in 1L, the cost is calculated to be Rs 271/- per kg. Comparison with selling price of currently used chemical surfactants like Linear Alkyl Benzene sulphonic acid (LAB) and sodium Lauryl sulphate and Sodium Lauryl ether sulphate (SLS and SLES) reveals that prices of these products varies from Rs 120 to 600 per kg depending on manufacturer and quality, order quantity. From the above studies it can be seen that production and use of this multiutility biosurfactant has, besides a clear advantage over chemically produced surfactants in terms of sustainability, a fairly good comparison with selling price of currently used chemical surfactants. The present study on the production costs (excluding capital costs) successfully demonstrates a break even analysis into the production of biosurfactants.

microbiology↗

bioRxiv2025

Imaging cellular activity simultaneously across all organs of a vertebrate reveals body-wide circuits

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 (WHole Organism Live Imaging System for recording Tissue and IntraCellular activity), 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 with proof of concept in 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 most 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.

neuroscience↗

bioRxiv2024

Discovering Temporally Compositional Neural Manifolds with Switching Infinite GPFA

Gaussian Process Factor Analysis (GPFA) is a powerful latent variable model for extracting low-dimensional manifolds underlying population neural activities. However, one limitation of standard GPFA models is that the number of latent factors needs to be pre-specified or selected through heuristic-based processes, and that all factors contribute at all times. We propose the infinite GPFA model, a fully Bayesian non-parametric extension of the classical GPFA by incorporating an Indian Buffet Process (IBP) prior over the factor loading process, such that it is possible to infer a potentially infinite set of latent factors, and the identity of those factors that contribute to neural firings in a compositional manner at each time point. Learning and inference in the infinite GPFA model is performed through variational expectation-maximisation, and we additionally propose scalable extensions based on sparse variational Gaussian Process methods. We empirically demonstrate that the infinite GPFA model correctly infers dynamically changing activations of latent factors on a synthetic dataset. By fitting the infinite GPFA model to population activities of hippocampal place cells during spatial tasks with alternating random foraging and spatial memory phases, we identify novel non-trivial and behaviourally meaningful dynamics in the neural encoding process.

neuroscience↗

bioRxiv2023

Contextual modulation is a stable feature of the neural code in auditory cortex of awake mice

The perceptual salience of a sound depends on the acoustic context in which it appears, and can vary on a timescale of milliseconds. At the level of single neurons in the auditory cortex, spectrotemporal tuning for particular sounds is shaped by a similarly fast and systematic nonlinear sensitivity to acoustic context. Does this neuronal context sensitivity "drift" over time in awake animals, or is it a stable feature of sound representation in the auditory cortex? We used chronically implanted tetrode arrays in awake mice to measure the electrophysiological responses of auditory cortical neurons to spectrotemporally complex, rapidly varying sounds across many days. For each neuron in each recording session, we applied the nonlinear-linear "context model" to estimate both a principal (spectrotemporal) receptive [fi]eld and a "contextual gain [fi]eld" describing the neurons nonlinear sensitivity to acoustic context. We then quanti[fi]ed the stability of these [fi]elds within and across days, using spike waveforms to match neurons recorded in multiple sessions. Contextual gain [fi]elds of auditory cortical neurons in awake mice were remarkably stable across many days of recording, and comparable in stability to principal receptive [fi]elds. Interestingly, there were small but signi[fi]cant effects of changes in locomotion or pupil size on the ability of the context model to [fi]t temporal fluctuations in the neuronal response. We conclude that both spectrotemporal tuning and nonlinear sensitivity to acoustic context are stable features of neuronal sound representation in the awake auditory cortex, which can be modulated by behavioral state.

neuroscience↗

bioRxiv2023

Inferring context-dependent computations through linear approximations of prefrontal cortex dynamics

The complex neural population activity of prefrontal cortex (PFC) is a hallmark of cognitive processes. How these rich dynamics emerge and support neural computations is largely unknown. Here, we infer mechanisms underlying the context-dependent selection and integration of sensory inputs by fitting dynamical models to PFC population responses of behaving monkeys. A class of models implementing linear dynamics driven by external inputs accurately captured the PFC responses within each context, achieving performance comparable to models without linear constraints. Two distinct mechanisms of input selection and integration were equally consistent with the data. One implemented context-dependent recurrent dynamics, as previously proposed, and relied on transient input amplification. The other relied on the subtle contextual modulation of the inputs, providing quantitative constraints on the attentional effects in sensory areas required to explain flexible PFC responses and behavior. Both mechanisms consistently revealed properties of inputs and recurrent dynamics missing in more simplified, incomplete descriptions of PFC responses. By revealing mechanisms consistent with rich cortical dynamics, our modeling approach provides a principled and general framework to link neural population activity and computation.

neuroscience↗

bioRxiv2022

Direct neural perturbations reveal a dynamical mechanism for robust computation

The rich repertoire of skilled mammalian behavior is the product of neural circuits that generate robust and flexible patterns of activity distributed across populations of neurons. Decades of associative studies have linked many behaviors to specific patterns of population activity, but association alone cannot reveal the dynamical mechanisms that shape those patterns. Are local neural circuits high-dimensional dynamical reservoirs able to generate arbitrary superpositions of patterns with appropriate excitation? Or might circuit dynamics be shaped in response to behavioral context so as to generate only the low-dimensional patterns needed for the task at hand? Here, we address these questions within primate motor cortex by delivering optogenetic and electrical microstimulation perturbations during reaching behavior. We develop a novel analytic approach that relates measured activity to theoretically tractable, dynamical models of excitatory and inhibitory neurons. This computational model captures the dynamical effects of these perturbations and demonstrates that motor cortical activity during reaching is shaped by a self-contained, low-dimensional dynamical system. The subspace containing task-relevant dynamics proves to be oriented so as to be robust to strong non-normal amplification within cortical circuits. This task dynamics space exhibits a privileged causal relationship with behavior, in that stimulation in motor cortex perturb reach kinematics only to the extent that it alters neural states within this subspace. Our results resolve long-standing questions about the dynamical structure of cortical activity associated with movement, and illuminate the dynamical perturbation experiments needed to understand how neural circuits throughout the brain generate complex behavior.

neuroscience↗

bioRxiv2022

Bio-inspired neural networks implement different recurrent visual processing strategies than task-trained ones do

Behavioral studies suggest that recurrence in the visual system is important for processing degraded stimuli. There are two broad anatomical forms this recurrence can take, lateral or feedback, each with different assumed functions. Here we add four different kinds of recurrence--two of each anatomical form--to a feedforward convolutional neural network and find all forms capable of increasing the ability of the network to classify noisy digit images. Specifically, we take inspiration from findings in biology by adding predictive feedback and lateral surround suppression. To compare these forms of recurrence to anatomically-matched counterparts we also train feedback and lateral connections directly to classify degraded images. Counter-intuitively, we find that the anatomy of the recurrence is not related to its function: both forms of task-trained recurrence change neural activity and behavior similarly to each other and differently from their bio-inspired anatomical counterparts. By using several analysis tools frequently applied to neural data, we identified the distinct strategies used by the predictive versus task-trained networks. Specifically, predictive feedback de-noises the representation of noisy images at the first layer of the network and decreases its dimensionality, leading to an expected increase in classification performance. Surprisingly, in the task-trained networks, representations are not de-noised over time at the first layer (in fact, they become noiser and dimensionality increases) yet these dynamics do lead to de-noising at later layers. The analyses used here can be applied to real neural recordings to identify the strategies at play in the brain. Our analysis of an fMRI dataset weakly supports the predictive feedback model but points to a need for higher-resolution cross-regional data to understand recurrent visual processing..

neuroscience↗

bioRxiv2021

Learning shapes cortical dynamics to enhance integration of relevant sensory input

Adaptive sensory behavior is thought to depend on processing in recurrent cortical circuits, but how dynamics in these circuits shapes the integration and transmission of sensory information is not well understood. Here, we study neural coding in recurrently connected networks of neurons driven by sensory input. We show analytically how information available in the network output varies with the alignment between feedforward input and the integrating modes of the circuit dynamics. In light of this theory, we analyzed neural population activity in the visual cortex of mice that learned to discriminate visual features. We found that over learning, slow patterns of network dynamics realigned to better integrate input relevant to the discrimination task. This realignment of network dynamics could be explained by changes in excitatory-inhibitory connectivity amongst neurons tuned to relevant features. These results suggest that learning tunes the temporal dynamics of cortical circuits to optimally integrate relevant sensory input. HighlightsO_LIA new theoretical principle links recurrent circuit dynamics to optimal sensory coding C_LIO_LIPredicts that high-SNR input dimensions activate slowly decaying modes of dynamics C_LIO_LIPopulation dynamics in primary visual cortex realign during learning as predicted C_LIO_LIStimulus-specific changes in E-I connectivity in recurrent circuits explain realignment C_LI

neuroscience↗

bioRxiv2021

Residual dynamics resolves recurrent contributions to neural computation

Relating neural activity to behavior requires an understanding of how neural computations arise from the coordinated dynamics of distributed, recurrently connected neural populations. However, inferring the nature of recurrent dynamics from partial recordings of a neural circuit presents significant challenges. Here, we show that some of these challenges can be overcome by a fine-grained analysis of the dynamics of neural residuals, i.e. trial-by-trial variability around the mean neural population trajectory for a given task condition. Residual dynamics in macaque pre-frontal cortex (PFC) in a saccade-based perceptual decision-making task reveals recurrent dynamics that is time-dependent, but consistently stable, and suggests that pronounced rotational structure in PFC trajectories during saccades is driven by inputs from upstream areas. The properties of residual dynamics restrict the possible contributions of PFC to decision-making and saccade generation, and suggest a path towards fully characterizing distributed neural computations with large-scale neural recordings and targeted causal perturbations.

neuroscience↗

bioRxiv2021

Learning and attention increase visual response selectivity through distinct mechanisms

Selectivity of cortical neurons for sensory stimuli can increase across days as animals learn their behavioral relevance, and across seconds when animals switch attention. While both phenomena are expressed in the same cortical circuit, it is unknown whether they rely on similar mechanisms. We imaged activity of the same neuronal populations in primary visual cortex as mice learned a visual discrimination task and subsequently performed an attention switching task. Selectivity changes due to learning and attention were uncorrelated in individual neurons. Selectivity increases after learning mainly arose from selective suppression of responses to one of the task relevant stimuli but from selective enhancement and suppression during attention. Learning and attention differentially affected interactions between excitatory and PV, SOM and VIP inhibitory cell classes. Circuit modelling revealed that cell class-specific top-down inputs best explained attentional modulation, while the reorganization of local functional connectivity accounted for learning related changes. Thus, distinct mechanisms underlie increased discriminability of relevant sensory stimuli across longer and shorter time scales.

neuroscience↗

bioRxiv2020

Small-scale field assessment of efficacy of the autodissemination approach against Aedes sp. in an urban area.

This is the first study to evaluate the efficacy of an autodissemination approach, as suggested by WHO. Therefore, the efficacy of an autodissemination approach in small-scale field trials against wild Aedes sp. population was evaluated in an urbanized setting, Malaysia. Lethal ovitraps enhanced with pyriproxyfen were used to control Aedes sp. populations at treatment sites, with the autodissemination activity was assessed using the WHO larval bioassays. Lethal ovitraps enhanced with pyriproxyfen effectively reduced of Aedes sp. population. All autodissemination stations were shown to be visited by Aedes sp. mosquitoes with 100% complete inhibition against eggs and larvae development. In the larvae bioassay, pupae mortality ranged from 14 to 40%. Statistically, a significant reduction of Aedes sp. population in the treatment sites compared to the untreated areas. The study proved for the autodissemination of pyriproxyfen to breeding habitats by wild Aedes sp. This technique is highly potentially for vector control activities. Future evaluation should focus on large-scale field trials. Author SummarySince 2012, Dataran Automobil, Seksyen 15, Shah Alam, was declared as one of the dengue hotspot areas. Major vector control activities were conducted by government, NGOs, social communities, and local authorities, but the number still rising. We conducted a new invention of autodissemination concepts in this area by an entomological study on mosquito populations reduction and dispersal abilities of the technique. We found that the technique has proven to control mosquito populations, but the other factors such as epidemiology link still unclear and need further clarification. Our finding highlighted the effectiveness of autodissemination strategies that can be considered as one of the alternative tools in vector control programme.

ecology↗

bioRxiv2019

Movement initiation and grasp representation in premotor and primary motor cortex mirror neurons

Pyramidal tract neurons (PTNs) within macaque rostral ventral premotor cortex (F5) and primary motor cortex (M1) provide direct input to spinal circuitry and are critical for skilled movement control, but surprisingly, can also be active during passive action observation. We recorded from single neurons, including identified PTNs in the hand and arm area of primary motor cortex (M1) (n=189), and in premotor area F5 (n=115) of two adult male macaques, while they executed, observed, or simply withheld (NoGo) reach-to-grasp and hold actions. We found that F5 maintains a more sustained, similar representation of grasping actions during both execution and observation. In contrast, although some M1 neurons mirrored during the grasp and hold, M1 population activity during observation contained signatures of a withholding state. This suggests that M1 and its output may dissociates signals required for the initiation of movement from those associated with the representation of grasp in order to flexibly guide behaviour. Significance StatementVentral premotor cortex (area F5) maintains a similar representation of grasping actions during both execution and observation. Primary motor cortex and its outputs dissociate between movement and non-movement states.

bioinformatics↗

bioRxiv2019

Sustained Activation of PV+ Interneurons in Auditory Cortex Enables Robust Divisive Gain Control for Complex and Naturalistic Stimuli

Sensory cortices must flexibly adapt their operations to internal states and external requirements. Modulation of specific inhibitory interneurons may provide a network-level mechanism for adjustments on behaviourally relevant timescales. Understanding of the computational roles of such modulation has mostly been restricted to phasic optogenetic activation and short, transient stimuli. Here, we aimed to extend the understanding of modulation of cortical inhibition by using sustained, network-wide optogenetic activation of parvalbumin-positive interneurons in core auditory cortex to study modulation of responses to transient, sustained, and naturalistic stimuli. We found highly conserved spectral and temporal tuning, despite profoundly reduced overall network activity. This reduction was predominantly divisive, and consistent across simple, complex, and naturalistic stimuli. A recurrent network model with power-law input-output functions replicated our results. We conclude that modulation of parvalbumin-positive interneurons on timescales typical of more sustained neuromodulation may provide a means for robust divisive gain control conserving stimulus representations.

neuroscience↗

bioRxiv2019

Dendritic calcium signals in rhesus macaque motor cortex drive an optical brain-computer interface

Calcium imaging has rapidly developed into a powerful tool for recording from large populations of neurons in vivo. Imaging in rhesus macaque motor cortex can enable the discovery of new principles of motor cortical function and can inform the design of next generation brain-computer interfaces (BCIs). Surface two-photon (2P) imaging, however, cannot presently access somatic calcium signals of neurons from all layers of macaque motor cortex due to photon scattering. Here, we demonstrate an implant and imaging system capable of chronic, motion-stabilized two-photon (2P) imaging of calcium signals from in macaques engaged in a motor task. By imaging apical dendrites, some of which originated from deep layer 5 neurons, as as well as superficial cell bodies, we achieved optical access to large populations of deep and superficial cortical neurons across dorsal premotor (PMd) and gyral primary motor (M1) cortices. Dendritic signals from individual neurons displayed tuning for different directions of arm movement, which was stable across many weeks. Combining several technical advances, we developed an optical BCI (oBCI) driven by these dendritic signals and successfully decoded movement direction online. By fusing 2P functional imaging with CLARITY volumetric imaging, we verify that an imaged dendrite, which contributed to oBCI decoding, originated from a putative Betz cell in motor cortical layer 5. This approach establishes new opportunities for studying motor control and designing BCIs.

neuroscience↗

bioRxiv2019

A neurally plausible model for online recognition and postdiction

Humans and other animals are frequently near-optimal in their ability to integrate noisy and ambiguous sensory data to form robust percepts, which are informed both by sensory evidence and by prior experience about the causal structure of the environment. It is hypothesized that the brain establishes these structures using an internal model of how the observed patterns can be generated from relevant but unobserved causes. In dynamic environments, such integration often takes the form of postdiction, wherein later sensory evidence affects inferences about earlier percepts. As the brain must operate in current time, without the luxury of acausal propagation of information, how does such postdictive inference come about? Here, we propose a general framework for neural probabilistic inference in dynamic models based on the distributed distributional code (DDC) representation of uncertainty, naturally extending the underlying encoding to incorporate implicit probabilistic beliefs about both present and past. We show that, as in other uses of the DDC, an inferential model can be learned efficiently using samples from an internal model of the world. Applied to stimuli used in the context of psychophysics experiments, the framework provides an online and plausible mechanism for inference, including postdictive effects.

neuroscience↗

bioRxiv2018

Brittleness in model selection analysis of single neuron firing rates

Models of complex heterogeneous systems like the brain are inescapably incomplete, and thus always falsified with enough data. As neural data grow in volume and complexity, absolute measures of adequacy are being replaced by model selection methods that rank the relative accuracy of competing theories. Selection still depends on incomplete mathematical instantiations, but the implicit expectation is that ranking is robust to their details. Here we highlight a contrary finding of \"brittleness,\" where data matching one theory conceptually are ranked closer to an instance of another. In particular, selection between recent models of decision making is conceptually misleading when data are simulated with minor distributional mismatch, with mixed secondary signals, or with non-stationary parameters; and decision-related responses in macaque cortex show features suggesting that these effects may impact empirical results. We conclude with recommendations to mitigate such brittleness when using model selection to study neural signals.

neuroscience↗

bioRxiv2018

Temporal alignment and latent Gaussian process factor inference in population spike trains

We introduce a novel scalable approach to identifying common latent structure in neural population spike-trains, which allows for variability both in the trajectory and in the rate of progression of the underlying computation. Our approach is based on shared latent Gaussian processes (GPs) which are combined linearly, as in the Gaussian Process Factor Analysis (GPFA) algorithm. We extend GPFA to handle unbinned spike-train data by incorporating a continuous time point-process likelihood model, achieving scalability with a sparse variational approximation. Shared variability is separated into terms that express condition dependence, as well as trial-to-trial variation in trajectories. Finally, we introduce a nested GP formulation to capture variability in the rate of evolution along the trajectory. We show that the new method learns to recover latent trajectories in synthetic data, and can accurately identify the trial-to-trial timing of movement-related parameters from motor cortical data without any supervision.

neuroscience↗

bioRxiv2018

An ultralight head-mounted camera system integrates detailed behavioral monitoring with multichannel electrophysiology in freely moving mice

Breakthroughs in understanding the neural basis of natural behavior require neural recording and intervention to be paired with high-fidelity multimodal behavioral monitoring. An extensive genetic toolkit for neural circuit dissection, and well-developed neural recording technology, make the mouse a powerful model organism for systems neuroscience. However, methods for high-bandwidth acquisition of behavioral signals in mice remain limited to fixed-position cameras and other off-animal devices, complicating the monitoring of animals freely engaged in natural behaviors. Here, we report the development of an ultralight head-mounted camera system combined with head-movement sensors to simultaneously monitor eye position, pupil dilation, whisking, and pinna movements along with head motion in unrestrained, freely behaving mice. The power of the combined technology is demonstrated by observations linking eye position to head orientation; whisking to non-tactile stimulation; and, in electrophysiological experiments, visual cortical activity to volitional head movements.

neuroscience↗