Biology subjects

Diana, G.

Publications and source records attributed to Diana, G..

11 recordsLinked to original sources

bioRxiv2026

Synaptic dynamics as a tunable substrate shaping neuronal activity sequences

A central tenet of cerebellar computation posits that granule cells generate sparse spatiotemporal activity patterns that support precisely timed motor and cognitive outputs. Using high-speed in vivo calcium and glutamate imaging combined with slice electrophysiology, we show that heterogeneous synaptic dynamics transform mossy fiber inputs into temporally sparse, sequential patterns of GC activity. Region-specific differences in MF glutamate release shape GC response duration and sparsity, tuning the temporal statistics of population sequences to match sensorimotor associative learning demands. These findings provide direct evidence for temporally sparse GC sequences and establish synaptic dynamics as a ubiquitous, tunable mechanism structuring neural activity in time.

neuroscience↗

bioRxiv2025

Precise calcium-to-spike inference using biophysical generative models

The intramolecular dynamics of fluorescent calcium indicators distort the relationship between calcium signals and action potentials ("spikes"), hampering efficient spike inference from calcium imaging. To address this problem, we characterized the calcium response kinetics of three widely used indicators, GCaMP6f, jGCaMP7f, and jGCaMP8f, using in vitro stopped-flow measurements and brain slice recordings. We identify previously unreported kinetic features, including use-dependent slowing of fluorescence decay, that introduce systematic errors in linear model-based inference methods. Using these observations, we developed a multistate model of GCaMP and used it to create biophysically-inspired Bayesian Sequential Monte Carlo and machine learning inference models trained on synthetic datasets. These methods outperform existing methods on spike timing accuracy and correlation benchmarks derived from diverse cell types. Our results show that using synthetic data derived from our biophysical model yields a decoder that outperforms even those trained on extensive experimental data. By separating indicator characterization from inference, our framework, Calcium Spike Processing using Integrated Kinetic Estimation and Simulation (C-SPIKES), provides a generalizable strategy applicable to existing and future calcium indicators.

neuroscience↗

bioRxiv2023

Spatial subdomains in the Optic Tectum for the encoding of visual information

1Neurons across the visual system provide estimates of the visual features they encode. However, the reliability of those estimates can vary across the neuronal population. Here, we use information theory to provide a spatial map of how well neurons can distinguish ethologically-relevant visual stimuli across the entire larval zebrafish optic tectum, a brain region responsible for driving visually guided behaviour. We find that the ability of neurons to discriminate between stimuli is non-uniformly distributed across the tectum. Specifically, we show that information about local motion is preferentially encoded in the posterior tectum, whilst information about whole-field motion is preferentially encoded in the anterior tectum. This is achieved through two systematic changes along the anterior-posterior axis of the tectum: (i) a change in the number of neurons that discriminate between stimuli and (ii) a change in how well each neuron can discriminate between stimuli. By classifying neurons into distinct subtypes based on their response properties we uncovered a small group of neurons that are spatially localised to specific regions of the tectum and are able to discriminate between visual stimuli in a highly reliable manner. Our results highlight the importance of implementing information theoretic approaches to assess visual responses and provide a novel description of regional specialisation in the zebrafish optic tectum.

neuroscience↗

bioRxiv2022

High frequency spike inference with particle Gibbs sampling

Calcium-sensitive fluorescent indicators enable monitoring of spiking activity in large neuronal populations in animal models. Despite the plethora of algorithms developed over the past decades, accurate spike-time inference methods for spike rates exceeding 20 Hz are lacking. More importantly, little attention has been devoted to the quantification of statistical uncertainties in spike time estimation, which is essential for assigning confidence levels to inferred spike patterns. To address these challenges, we introduce (1) a statistical model that accounts for bursting neuronal activity and baseline fluorescence modulation and (2) apply a Monte Carlo strategy (particle Gibbs with ancestor sampling) to estimate the joint posterior distribution of spike times and model parameters. Our method is competitive with state-of-the-art supervised and unsupervised algorithms, as evaluated on the CASCADE benchmark datasets. Analysis of fluorescence transients recorded with the ultrafast genetically encoded calcium indicator GCaMP8f demonstrates that our method can resolve interspike intervals as short as 5 ms. Overall, our study describes a Bayesian inference method for detecting neuronal spiking patterns and quantifying their uncertainty. The use of particle Gibbs samplers enables unbiased estimates of spike times and all model parameters, and provides a flexible statistical framework for testing more specific models of calcium indicators.

neuroscience↗

bioRxiv2021

Topographically localised modulation of tectal cell spatial tuning by natural scenes

Visual neurons can have their tuning properties contextually modulated by the presence of visual stimuli in the area surrounding their receptive field, especially when that stimuli contains natural features. However, stimuli presented in specific egocentric locations may have greater behavioural relevance, raising the possibility that the extent of contextual modulation may vary with position in visual space. To explore this possibility we utilised the small size and optical transparency of the larval zebrafish to describe the form and spatial arrangement of contextually modulated cells throughout an entire tectal hemisphere. We found that the spatial tuning of tectal neurons to a prey-like stimulus sharpens when the stimulus is presented in the context of a naturalistic visual scene. These neurons are confined to a spatially restricted region of the tectum and have receptive fields centred within a region of visual space in which the presence of prey preferentially triggers hunting behaviour. Our results demonstrate that circuits that support behaviourally relevant modulation of tectal neurons are not uniformly distributed. These findings add to the growing body of evidence that the tectum shows regional adaptations for behaviour.

neuroscience↗

bioRxiv2021

Single-cell Networks Reorganise to Facilitate Whole-brain Supercritical Dynamics During Epileptic Seizures

Excitation-inhibition (EI) balance may be required for the organisation of brain dynamics to a phase transition, criticality, which confers computational benefits. Brain pathology associated with EI imbalance may therefore occur due to a deviation from criticality. However, evidence linking critical dynamics with EI imbalance-induced pathology is lacking. Here, we studied the effect of EI imbalance-induced epileptic seizures on brain dynamics, using in vivo whole-brain 2-photon imaging of GCaMP6s larval zebrafish at single-neuron resolution. We demonstrate the importance of EI balance for criticality, with EI imbalance causing a loss of whole-brain critical statistics. Using network models we show that a reorganisation of network topology drives this loss of criticality. Seizure dynamics match theoretical predictions for networks driven away from a phase transition into disorder, with the emergence of chaos and a loss of network-mediated separation, dynamic range and metastability. These results demonstrate that EI imbalance drives a pathological deviation from criticality.

neuroscience↗

bioRxiv2020

Cadherins regulate nuclear topography and function of developing ocular motor circuitry

In the vertebrate central nervous system, groups of functionally-related neurons, including cranial motor neurons of the brainstem, are frequently organised as nuclei. The molecular mechanisms governing the emergence of nuclear topography and circuit function are poorly understood. Here we investigate the role of cadherin-mediated adhesion in the development of zebrafish ocular motor (sub)nuclei. We find that developing ocular motor (sub)nuclei differentially express classical cadherins. Perturbing cadherin function in these neurons results in distinct defects in neuronal positioning, including scattering of dorsal cells and defective contralateral migration of ventral subnuclei. In addition, we show that cadherin-mediated interactions between adjacent subnuclei are critical for subnucleus position. We also find that disrupting cadherin adhesivity in dorsal oculomotor neurons impairs the larval optokinetic reflex, suggesting that neuronal clustering is important for co-ordinating circuit function. Our findings reveal that cadherins regulate distinct aspects of cranial motor neuron positioning and establish subnuclear topography and motor function.

developmental biology↗

bioRxiv2019

Learning steers the ontogeny of an efficient hunting sequence in zebrafish larvae.

The success of goal-directed behaviours relies on the coordinated execution of a sequence of component actions. In young animals, such sequences may be poorly coordinated, but with age and experience, behaviour progressively adapts to efficiently exploit the animals ecological niche. How experience impinges on the developing neural circuits of behaviour is an open question. As a model system, larval zebrafish (Danio rerio) hold enormous potential for studying both the development of behaviour and the underlying circuits, but no relevant experience-dependent learning paradigm has yet been characterized. To address this, we have conducted a detailed study of the effects of experience on the ontogeny of hunting behaviour in larval zebrafish. We report that larvae with prior prey experience consume considerably more prey than naive larvae. This is mainly due to increased capture success that is also accompanied by a modest increase in hunt rate. We identified two components of the hunting sequence that are jointly modified by experience. At the onset of the hunting sequence, the orientation strategy of the turn towards prey is modified such that experienced larvae undershoot prey azimuth. Near the end of the hunt sequence, we find that experienced larvae are more likely to employ high-speed capture swims initiated from longer distances to prey. Combined, these modified turn and capture manoeuvrers can be used to predict the probability of capture success and suggest that their development provides advantages specific to larvae feeding on live-prey. Our findings establish an ethologically relevant paradigm in zebrafish for studying how the brain is shaped by experience to drive the ontogeny of efficient behaviour.

animal behavior and cognition↗

bioRxiv2019

A Multicellular Network Mechanism for Temperature-Robust Food Sensing

Responsiveness to external cues is a hallmark of biological systems. In complex environments, organisms must remain responsive to specific inputs even as other internal or external factors fluctuate. Here we show how Caenorhabditis elegans can discriminate between food levels to modulate lifespan despite temperature perturbations. While robustness of fixed outputs has been described, our findings uncover a more complex robustness process that maintains food-responsiveness. This end-to-end robustness from environment to physiology is mediated by food-sensing neurons that communicate via TGF-{beta} and serotonin signals to form a multicellular gene network. Mechanistically, specific regulations in this network change with temperature to maintain similar food-responsiveness in the lifespan output. Together, our findings provide a basis for gene-environment interactions and unveil computations that integrate environmental cues to govern physiology.

systems biology↗

bioRxiv2018

Heterogeneous progenitor cell behaviors underlie the assembly of neocortical cytoarchitecture

The cerebral cortex contains multiple hierarchically organized areas with distinctive cytoarchitectonical patterns, but the cellular mechanisms underlying the emergence of this diversity remain unclear. Here, we have quantitatively investigated the neuronal output of individual progenitor cells in the ventricular zone of the developing mouse neocortex using a combination of methods that together circumvent the biases and limitations of individual approaches. We found that individual cortical progenitor cells show a high degree of stochasticity and generate pyramidal cell lineages that adopt a wide range of laminar configurations. Mathematical modelling these lineage data suggests that a small number of progenitor cell populations, each generating pyramidal cells following different stochastic developmental programs, suffice to generate the heterogenous complement of pyramidal cell lineages that collectively build the complex cytoarchitecture of the neocortex.

neuroscience↗

bioRxiv2018

Bayesian inference of neuronal ensembles

In many areas of the brain, both spontaneous and stimulus-evoked activity can manifest as synchronous activation of neuronal ensembles. The characterization of ensemble structure and dynamics provides important insights into how brain computations are distributed across neural networks. The proliferation of experimental techniques for recording the activity of neuronal ensembles calls for a comprehensive statistical method to describe, analyze and characterize these high dimensional datasets. Here we introduce a generative model of synchronous activity to describe spontaneously active neural ensembles. Unlike existing methods, our analysis provides a simultaneous estimation of ensemble composition, dynamics and statistical features of these neural populations, including ensemble noise and activity rate. We also introduce ensemble \"coherence\" as a measure of within-ensemble synchrony. We have used our method to characterize population activity throughout the tectum of larval zebrafish, allowing us to make statistical inference on the spatiotemporal organization of tectal ensembles, their composition and the logic of their interactions. We have also applied our method to functional imaging and neuropixels recordings from the mouse, allowing us to relate the activity of identified ensembles to specific behaviours such as running or changes in pupil diameter.

neuroscience↗