Biology subjects

Luna, R.

Publications and source records attributed to Luna, R..

12 recordsLinked to original sources

bioRxiv2026

Dynamic balance of sparse flux vectors for efficient simulation of culture dynamics and metabolic network reduction

Dynamic Flux Balance Analysis (DFBA) enables simulation of microbial culture dynamics under changing environmental conditions, but remains computationally expensive for tasks such as parameter calibration and fermentation optimization when applied using genome-scale metabolic models (GEMs). To address this challenge, we introduce Dynamic Flux Vector Balancing (DFVB), a reformulation of DFBA that solves an equivalent problem using a pre-computed, sparse basis of flux solutions that reduces the dimensionality of the internal optimization problem without information loss. Notably, DFVB provides a compact, interpretable representation of flux states that can readily identify dynamically inactive pathways and enable simulation-based automatic metabolic network reduction. We showed that DFVB produces the same culture dynamics as DFBA across multiple model scales and conditions, and identifies inactive reactions more accurately than Flux Variability Analysis (FVA) when compared to transcriptomic data profiles. Furthermore, computational performance analyses demonstrated that integrating DFVB with solver warm-start strategies and model reduction enhances computational efficiency relative to DFBA, yielding up to 3-fold reductions in simulation time for large-scale metabolic models. Finally, kinetic parameter estimation of culture dynamics with DFVB in two fermentation scenarios using a large-scale yeast GEM reached equal or higher prediction fidelity and narrower confidence intervals than DFBA, indicating improved parameter identifiability and robustness. Together, these results position DFVB as a scalable, robust, and biologically coherent framework for dynamic metabolic modeling, easing the integration of GEMs for culture dynamics simulation.

bioinformatics↗

bioRxiv2025

Subanesthetic Ketamine Disrupts Predictive Signaling in the Prefrontal Cortex

Corollary discharge (CD) signals allow the brain to predict and suppress the sensory consequences of its own actions, providing stability to perception and thought. Disruption of these predictive mechanisms has long been hypothesized to contribute to the disorganization of experience in schizophrenia, yet direct circuit-level evidence has been lacking. Here, we show that ketamine, a dissociative N-methyl-D-aspartate receptor (NMDAR) antagonist, at subanesthetic doses, selectively disrupts CD signaling in the lateral prefrontal cortex (LPFC)--a region thought to be the seed of mental representations and one of the most affected areas in schizophrenia. We recorded activity from 1,342 neurons in LPFC areas 8a and 9/46 of macaques performing a visuospatial working-memory task in a virtual environment, before and after subanesthetic ketamine administration. Ketamine impaired performance and increased overall firing rates but markedly suppressed saccade-related responses carrying CD signals. This led to decreased discriminability of eye movement signals. This finding links two major ideas in neuroscience research: the role of disrupted glutamate signaling and the failure of the brains predictive models. It provides evidence for how these mechanisms may interact in the prefrontal cortex to disturb the sense of reality.

neuroscience↗

bioRxiv2024

Overcoming the limitations of motion sensor models by considering dendritic computations

The estimation of motion is a fundamental process for any sighted animal. Computational models for motion sensors have a long and successful history but they still suffer from fundamental shortcomings, as they disagree with physiological evidence and each model is dedicated to a specific type of motion, which is controversial from a biological standpoint. In this work we propose a new approach for modeling motion sensors that considers dendritic computations, a key aspect for predicting single-neuron responses that had previously been absent from motion models. We show how, by taking into account the dynamic and input-dependent nature of dendritic nonlinearities, our motion sensor model is able to overcome the fundamental limitations of standard approaches.

neuroscience↗

bioRxiv2024

Spike frequency adaptation in primate lateral prefrontal cortex neurons results from interplay between intrinsic properties and circuit dynamics

Recordings of cortical neurons isolated from brain slices and dissociated from their networks, display intrinsic spike frequency adaptation (I-SFA) to a constant current input. Interestingly, extracellular recordings in behaving subjects also show extrinsic-SFA (E-SFA) in response to sustained visual stimulation. Because neurons are isolated from brain networks in slice recordings, it is challenging to infer how I-SFA contributes to E-SFA in interconnected brains during behavior. To investigate this, we recorded responses of macaque lateral prefrontal cortex neurons in vivo during a visually guided saccade task and in acute brain slices in vitro. Broad spiking (BS) putative pyramidal cells and narrow spiking (NS) putative inhibitory interneurons exhibited E-SFA in vivo. In acute brain slices, both cell types displayed I-SFA though their magnitudes differed. To investigate how in vitro I-SFA contributes to in vivo E-SFA, we developed a data-driven hybrid circuit model in which local NS neurons are driven by BS input. We observed that model NS cell responses show longer SFA than observed in vivo. Introducing inhibition of NS cells to the model circuit removed this discrepancy. Our results indicate that both I-SFA and inhibitory circuit dynamics contribute to E-SFA in LPFC neurons. They highlight the contribution of single neuron and network dependent computations to neural activity underlying behavior.

neuroscience↗

bioRxiv2024

Task-specific topographical maps of neural activity in the primate lateral prefrontal cortex

Neurons in the primate lateral prefrontal cortex (LPFC) flexibly adapt their activity to support a wide range of cognitive tasks. Whether and how the topography of LPFC neural activity changes as a function of task is unclear. In the present study, we address this issue by characterizing the functional topography of LPFC neural activity in awake behaving macaques performing three distinct cognitive tasks. We recorded from chronically implanted multi-electrode arrays and show that the topography of LPFC activity is stable within a task, but adaptive across tasks. The topography of neural activity exhibits a spatial scale compatible with prior anatomical tracing work on a!erent LPFC inputs. Our findings show that LPFC maps of neural population activity are stable for a specific task, providing robust neural codes that support task specialization. Moreover, the variability in functional topographies across tasks indicates activity landscapes can adapt, providing flexibility to LPFC neural codes.

neuroscience↗

bioRxiv2023

Retinal waves in adaptive rewiring networks orchestrate convergence and divergence in the visual system

Spontaneous retinal wave activity shaping the visual system is a complex neurodevelopmental phenomenon. Retinal ganglion cells are the hubs through which activity diverges throughout the visual system. We consider how these divergent hubs emerge, using an adaptively rewiring neural network model. Adaptive rewiring models show in a principled way how brains could achieve their complex topologies. Modular small-world structures with rich club effects and circuits of convergent-divergent units emerge as networks evolve, driven by their own spontaneous activity. Arbitrary nodes of an initially random model network were designated as retinal ganglion cells. They were intermittently exposed to the retinal waveform, as the network evolved through adaptive rewiring. A significant proportion of these nodes developed into divergent hubs within the characteristic complex network architecture. The proportion depends parametrically on the wave incidence rate. Higher rates increase the likelihood of hub formation, while increasing the potential of ganglion cell death. In addition, direct neigbours of designated ganglion cells differentiate like amacrine cells. The divergence observed in ganglion cells resulted in enhanced convergence downstream, suggesting that retinal waves control the formation of convergence in LGN. We conclude that retinal waves stochastically control the distribution of converging and diverging activity in evolving complex networks.

neuroscience↗

bioRxiv2022

State-of-the-art image and video quality assessment with a metric based on an intrinsically nonlinear neural summation model

The development of automatic methods for image and video quality assessment that correlate well with the perception of human observers is a very challenging open problem in vision science, with numerous practical applications in disciplines such as image processing and computer vision, as well as in the media industry. In the past two decades, the goal of image quality research has been to improve upon classical metrics by developing models that emulate some aspects of the visual system, and while the progress has been considerable, state-of-the-art quality assessment methods still share a number of shortcomings, like their performance dropping considerably when they are tested on a database that is quite different from the one used to train them, or their significant limitations in predicting observer scores for high framerate videos. In this work we propose a novel objective method for image and video quality assessment that is based on the recently introduced Intrinsically Non-linear Receptive Field (INRF) formulation, a neural summation model that has been shown to be better at predicting neural activity and visual perception phenomena than the classical linear receptive field. Here we start by optimizing, on a classic image quality database, the four parameters of a very simple INRF-based metric, and proceed to test this metric on three other databases, showing that its performance equals or surpasses that of the state-of-the-art methods, some of them having millions of parameters. Next, we extend to the temporal domain this INRF image quality metric, and test it on several popular video quality datasets; again, the results of our proposed INRF-based video quality metric are shown to be very competitive.

neuroscience↗

bioRxiv2022

Model-free decision making resists improved instructions and is enhanced by stimulus-response associations

Human behaviour may be thought of as supported by two different computational-learning mechanisms, model-free and model-based respectively. In model-free strategies, stimulus-response associations are strengthened when actions are followed by a reward and weakened otherwise. In model-based learning, previous to selecting an action, the current values of the different possible actions are computed based on a detailed model of the environment. Previous research with the two-stage task suggests that participants behaviour usually shows a mixture of both strategies. But, interestingly, a recent study by da Silva and Hare (2020) found that participants primarily deploy model-based behaviour when they are given detailed instructions about the structure of the task. In the present study, we reproduce this essential experiment. Our results confirm that improved instructions give rise to a stronger model-based component. Crucially, we also found a significant effect of reward that became stronger under conditions that favoured the development of strong stimulus-response associations. This suggests that the effect of reward, often taken as indicator of a model-free component, is related to stimulus-response learning.

neuroscience↗

bioRxiv2022

Neural sequences in primate prefrontal cortex encode working memory in naturalistic environments

Working memory is the ability to briefly remember and manipulate information after it becomes unavailable to the senses. The mechanisms supporting working memory coding in the primate brain remain controversial. Here we demonstrate that microcircuits in layers 2/3 of the primate lateral prefrontal cortex dynamically represent memory content in a naturalistic task through sequential activation of single neurons. We simultaneously recorded the activity of hundreds of neurons in the lateral prefrontal cortex of macaque monkeys during a naturalistic visuospatial working memory task set in a virtual environment. We found that the sequential activation of single neurons encoded trajectories to target locations held in working memory. Neural sequences were not a mere successive activation of cells with memory fields at specific spatial locations, but an abstract representation of the subjects trajectory to the target. Neural sequences were less correlated to target trajectories during perception and were not found during working memory tasks lacking the spatiotemporal structure of the naturalistic task. Finally, ketamine administration distorted neural sequences, selectively decreasing working memory performance. Our results indicate that neurons in the lateral prefrontal cortex causally encode working memory in naturalistic conditions via complex and temporally precise activation patterns.

neuroscience↗

bioRxiv2022

Stable working memory and perceptual representations in macaque lateral prefrontal cortex during naturalistic vision in virtual environments

Primates use perceptual and mnemonic visuospatial representations to perform everyday functions. Neurons in the lateral prefrontal cortex (LPFC) have been shown to encode both of these representations during tasks where eye movements are strictly controlled and visual stimuli are reduced in complexity. This raises the question of whether perceptual and mnemonic representations encoded by LPFC neurons remain robust during naturalistic vision -- in the presence of a rich visual scenery and during eye movements. Here we investigate this issue by training macaque monkeys to perform working memory and perception tasks in a visually complex virtual environment that requires navigation using a joystick and allows for free visual exploration of the scene. We recorded the activity of 3950 neurons in the LPFC (areas 8a and 9/46) of two rhesus macaques using multi-electrode arrays, and measured eye movements using video tracking. We found that navigation trajectories to target locations and eye movement behavior differed between the perception and working memory tasks suggesting that animals employed different behavioral strategies. Single neurons were tuned to target location during cue encoding and working memory delay and neural ensemble activity was predictive of the animals behavior. Neural decoding of target location was stable throughout the working memory delay epoch. However, neural representations of similar target locations differed between the working memory and perception tasks. These findings indicate that during naturalistic vision, LPFC neurons maintain robust and distinct neural codes for mnemonic and perceptual visuospatial representations. Significance StatementWe show that LPFC neurons encode working memory and perceptual representations during a naturalistic task set in a virtual environment. We show that despite eye movement and complex visual input, neurons maintain robust working memory representations of space which are distinct from neuronal representations for perception. We further provide novel insight on the use of virtual environments to construct behavioral tasks for electrophysiological experiments.

neuroscience↗

bioRxiv2021

Different neural codes serve long and short-term memory functions in primate Hippocampus and Lateral Prefrontal Cortex during virtual navigation

The hippocampus (HPC) and the lateral prefrontal cortex (LPFC) are two cortical areas of the primate brain deemed essential to cognition. Here we hypothesize that the codes mediating neuronal communication in HPC and LPFC microcircuits have distinctively evolved to serve plasticity and memory function at different spatiotemporal scales. We used a virtual reality task in which animals navigated through a maze using a joystick and selected one of two targets in the arms of the maze according to a learned context-color rule. We found that neurons and neuronal populations in both regions encode similar information about the task. Moreover, we demonstrate that many HPC neurons concentrate spikes into bursts, whereas most layer II/III LPFC neurons sparsely distribute spikes over time. As the animals learned the task HPC neurons, but not LPFC neurons, increased their burst rate as a function of performance. When integrating spike rates over short intervals, HPC neuronal ensembles reached maximum decoded information with fewer neurons than LPFC ensembles. Our results show that during associative learning HPC principal cells concentrate spikes in bursts enabling temporal summation and fast synaptic plasticity in small populations of neurons and ultimately facilitating rapid encoding of associative memories. On the other hand, layers II/III LPFC pyramidal cells fire spikes more sparsely distributed in time and over a larger number of neurons. The latter would facilitate broadcasting of signals loaded in short term memory across neuronal populations without necessarily triggering fast synaptic plasticity.

neuroscience↗

bioRxiv2020

Naturalistic coding of working memory in primate prefrontal cortex

The primate lateral prefrontal cortex (LPFC) is considered fundamental for temporarily maintaining and manipulating mental representations that serve behavior, a cognitive function known as working memory1. Studies in non-human primates have shown that LPFC lesions impair working memory2 and that LPFC neuronal activity encodes working memory representations3. However, such studies have used simple displays and constrained gaze while subjects held information in working memory3, which put into question their ethological validity4,5. Currently, it remains unclear whether LPFC microcircuits can support working memory function during natural behavior. We tested macaque monkeys in a working memory navigation task in a life-like virtual environment while their gaze was unconstrained. We show that LPFC neuronal populations robustly encode working memory representations in these conditions. Furthermore, low doses of the NMDA receptor antagonist, ketamine, impaired working memory performance while sparing perceptual and motor skills. Ketamine decreased the firing of narrow spiking inhibitory interneurons and increased the firing of broad spiking cells reducing population decoding accuracy for remembered locations. Our results show that primate LPFC generates robust neural codes for working memory in naturalistic settings and that such codes rely upon a fine balance between the activation of excitatory and inhibitory neurons.

neuroscience↗