Biology subjects

Gjorgjieva, J.

Publications and source records attributed to Gjorgjieva, J..

35 records · Page 2Linked to original sources

bioRxiv2022

Walking elicits global brain activity in Drosophila

Movement-correlated brain activity has been found across species and brain regions. Here, we used fast whole-brain lightfield imaging in adult Drosophila to investigate the relationship between walk and neuronal activity. We observed brainwide activity that tightly correlated with spontaneous bouts of walk. While imaging specific sets of excitatory, inhibitory, and neuromodulatory neurons highlighted their joint contribution, spatial heterogeneity in forward walk- and turning-induced activity allowed parsing unique responses from subregions and sometimes individual neurons. For example, previously uncharacterized serotonergic neurons were inhibited during walk. While activity onset in some areas preceded walk onset exclusively in spontaneously walking animals, spontaneous and forced walk elicited highly similar activity in most brain regions. These data suggest a major contribution of walk and walk-related sensory or proprioceptive information to brain state. We conclude that walk-related signals induce global but heterogeneous patterns of activity, allowing for local and brain-wide integration of behavioral state into other processes in the brain.

neuroscience↗

bioRxiv2021

Sequential propagation and routing of activity in a cortical network

Single spikes can trigger repeatable firing sequences in cortical networks. The mechanisms that support reliable propagation of activity from such small events and their functional consequences remain unclear. By constraining a recurrent network model with experimental statistics from turtle cortex, we generate reliable and temporally precise sequences from single spike triggers. We find that rare strong connections support sequence propagation, while dense weak connections modulate propagation reliability. We identify sections of sequences corresponding to divergent branches of strongly connected neurons which can be selectively gated. Applying eternal inputs to specific neurons in the sparse backbone of strong connections can effectively control propagation and route activity within the network. Finally, we demonstrate that concurrent sequences interact reliably, generating a highly combinatorial space of sequence activations. Our results reveal the impact of individual spikes in cortical circuits, detailing how repeatable sequences of activity can be triggered, sustained, and controlled during cortical computations.

neuroscience↗

bioRxiv2021

Adaptation of Drosophila larva foraging in response to changes in food distribution

When foraging, animals combine internal cues and sensory input from their environment to guide sequences of behavioral actions. Drosophila larva executes crawls, turns, and pauses to explore the substrate and find food sources. This exploration has to be flexible in the face of changes in the quality of food so that larvae feed in patches with favorable food and look for another source when the current location does not fulfill their nutritional needs. But which behavioral elements adapt, and what triggers those changes remain elusive. Using experiments and modeling, we investigate the foraging behavior of larvae in homogeneous environments with different food types and in environments where the food sources are patchy. Our work indicates that the speed of larval crawling and frequency of pauses is modulated by the food quality. Interestingly, we found that the genetic dimorphism in the foraging gene influences the exploratory behavior only when larvae crawl on yeast patches. While in a homogeneous substrate larvae maintain a turning bias in a specific orientation, in a patchy substrate larvae orient themselves towards the food when the patch border is reached. Therefore, by adapting different elements in their foraging behavior, larvae either increase the time inside nutritious food patches or continue exploring the substrate in less nutritious environments.

animal behavior and cognition↗

bioRxiv2021

Interneuron subtypes enable independent modulation of excitatory and inhibitory firing rates after sensory deprivation

Diverse interneuron subtypes determine how cortical circuits process sensory information depending on their connectivity. Sensory deprivation experiments are ideally suited to unravel the plasticity mechanisms which shape circuit connectivity, but have yet to consider the role of different inhibitory subtypes. We investigate how synaptic changes due to monocular deprivation affect the firing rate dynamics in a microcircuit network model of the visual cortex. We demonstrate that, in highly recurrent networks, deprivation-induced plasticity generates fundamentally different activity changes depending on interneuron composition. Considering parvalbumin-positive (PV+) and somatostatin-positive (SST+) interneuron subtypes can capture the experimentally observed independent modulation of excitatory and inhibitory activity during sensory deprivation when SST+ feedback is sufficiently strong. Our model also applies to whisker deprivation in the somatosensory cortex revealing that these mechanisms are general across sensory cortices. Therefore, we provide a mechanistic explanation for the differential role of interneuron subtypes in regulating cortical dynamics during deprivation-induced plasticity.

neuroscience↗

bioRxiv2020

The generation of cortical novelty responses through inhibitory plasticity

Animals depend on fast and reliable detection of novel stimuli in their environment. Indeed, neurons in multiple sensory areas respond more strongly to novel in comparison to familiar stimuli. Yet, it remains unclear which circuit, cellular and synaptic mechanisms underlie those responses. Here, we show that inhibitory synaptic plasticity readily generates novelty responses in a recurrent spiking network model. Inhibitory plasticity increases the inhibition onto excitatory neurons tuned to familiar stimuli, while inhibition for novel stimuli remains low, leading to a network novelty response. Generated novelty responses do not depend on the exact temporal structure but rather on the distribution of presented stimuli. By including tuning of inhibitory neurons, the network further captures stimulus-specific adaptation. Finally, we suggest that disinhibition can control the amplification of novelty responses. Therefore, inhibitory plasticity provides a flexible, biologically-plausible mechanism to detect the novelty of bottom-up stimuli, enabling us to make numerous experimentally testable predictions.

neuroscience↗

bioRxiv2020

Adaptation of spontaneous activity in the developing visual cortex

Spontaneous activity drives the establishment of appropriate connectivity in different circuits during brain development. In the mouse primary visual cortex, two distinct patterns of spontaneous activity occur before vision onset: local low-synchronicity events originating in the retina, and global high-synchronicity events originating in the cortex. We sought to determine the contribution of these activity patterns to jointly organize network connectivity through different activity-dependent plasticity rules. We found that local events shape cortical input selectivity and topography, while global events have a homeostatic role regulating connection strength. To generate robust selectivity, we predicted that global events should adapt their amplitude to the history of preceding cortical activation, and confirmed by analyzing in vivo spontaneous cortical activity. This adaptation led to the sparsification of spontaneous activity on a slower timescale during development, demonstrating the remarkable capacity of the developing sensory cortex to acquire sensitivity to visual inputs after eye-opening.

neuroscience↗

bioRxiv2020

Efficient population coding depends on stimulus convergence and source of noise

Sensory organs transmit information to downstream brain circuits using a neural code comprised of spikes from multiple neurons. According to the prominent efficient coding framework, the properties of sensory populations have evolved to encode maximum information about stimuli given biophysical constraints. How information coding depends on the way sensory signals from multiple channels converge downstream is still unknown, especially in the presence of noise which corrupts the signal at different points along the pathway. Here, we calculated the optimal information transfer of a population of nonlinear neurons under two scenarios. First, a lumped-coding channel where the information from different inputs converges to a single channel, thus reducing the number of neurons. Second, an independent-coding channel when different inputs contribute independent information without convergence. In each case, we investigated information loss when the sensory signal was corrupted by two sources of noise. We determined critical noise levels at which the optimal number of distinct thresholds of individual neurons in the population changes. Comparing our system to classical physical systems, these changes correspond to first- or second-order phase transitions for the lumped- or the independent-coding channel, respectively. We relate our theoretical predictions to coding in a population of auditory nerve fibers recorded experimentally, and find signatures of efficient coding. Our results yield important insights into the diverse coding strategies used by neural populations to optimally integrate sensory stimuli in the presence of distinct sources of noise.

neuroscience↗

bioRxiv2019

Oxytocin shapes spontaneous activity patterns in the developing visual cortex by activating somatostatin interneurons

Spontaneous network activity shapes emerging neuronal circuits during early brain development, however how neuromodulation influences this activity is not fully understood. Here, we report that the neuromodulator oxytocin powerfully shapes spontaneous activity patterns. In vivo, oxytocin strongly decreased the frequency and pairwise correlations of spontaneous activity events in visual cortex (V1), but not in somatosensory cortex (S1). This differential effect was a consequence of oxytocin only increasing inhibition in V1 and increasing both inhibition and excitation in S1. The increase in inhibition was mediated by the depolarization and increase in excitability of somatostatin+ (SST) interneurons specifically. Accordingly, silencing SST+ neurons pharmacogenetically fully blocked oxytocins effect on inhibition in vitro as well its effect on spontaneous activity patterns in vivo. Thus, oxytocin decreases the excitatory/inhibitory ratio and modulates specific features of V1 spontaneous activity patterns that are crucial for refining developing synaptic connections and sensory processing later in life.

neuroscience↗

bioRxiv2019

Valence and state-dependent population coding in dopaminergic neurons in the fly mushroom body

Neuromodulation permits flexibility of synapses, neural circuits and ultimately behavior. One neuromodulator, dopamine, has been studied extensively in its role as reward signal during learning and memory across animal species. Newer evidence suggests that dopaminergic neurons (DANs) can modulate sensory perception acutely, thereby allowing an animal to adapt its behavior and decision-making to its internal and behavioral state. In addition, some data indicate that DANs are heterogeneous and convey different types of information as a population. We have investigated DAN population activity and how it could encode relevant information about sensory stimuli and state by taking advantage of the confined anatomy of DANs innervating the mushroom body (MB) of the fly Drosophila melanogaster. Using in vivo calcium imaging and a custom 3D image registration method, we find that the activity of the population of MB DANs is predictive of the innate valence of an odor as well as the metabolic and mating state of the animal. Furthermore, DAN population activity is strongly correlated with walking or running, consistent with a role of dopamine in conveying behavioral state to the MB. Together our data and analysis suggest that distinct DAN population activities encode innate odor valence, movement and physiological state in a MB-compartment specific manner. We propose that dopamine shapes innate odor perception through combinatorial population coding of sensory valence, physiological and behavioral context.

neuroscience↗

bioRxiv2019

Homeostatic mechanisms regulate distinct aspects of cortical circuit dynamics

Homeostasis is indispensable to counteract the destabilizing effects of Hebbian plasticity. Although it is commonly assumed that homeostasis modulates synaptic strength, membrane excitability and firing rates, its role at the neural circuit and network level is unknown. Here, we identify changes in higher-order network properties of freely behaving rodents during prolonged visual deprivation. Strikingly, our data reveal that pairwise functional correlations and their structure are subject to homeostatic regulation. Using a computational model, we demonstrate that the interplay of different plasticity and homeostatic mechanisms can capture the initial drop and delayed recovery of firing rates and correlations observed experimentally. Moreover, our model indicates that synaptic scaling is crucial for the recovery of correlations and network structure, while intrinsic plasticity is essential for the rebound of firing rates, suggesting that synaptic scaling and intrinsic plasticity can serve distinct functions in homeostatically regulating network dynamics.

neuroscience↗

bioRxiv2019

A unifying framework for synaptic organization on cortical dendrites

Dendritic synaptic inputs are organized into functional clusters with remarkable subcellular precision at the micron level. This organization emerges during early postnatal development through patterned spontaneous activity and manifests both locally where nearby synapses are significantly correlated, and globally with distance to the soma. We propose a biophysically motivated synaptic plasticity model to dissect the mechanistic origins of this organization during development, and elucidate synaptic clustering of different stimulus features in the adult. Our model captures local clustering of orientation in ferret vs. receptive field overlap in mouse visual cortex based on the cortical magnification of visual space. Including a back-propagating action potential explains branch clustering heterogeneity in the ferret, and produces a global retinotopy gradient from soma to dendrite in the mouse. Therefore, our framework suggests that sub-cellular precision in connectivity can already be established in development, and unifies different aspects of synaptic organization across species and scales.

neuroscience↗

bioRxiv2019

Autonomous emergence of connectivity assemblies via spike triplet interactions

Non-random connectivity can emerge without structured external input driven by activity-dependent mechanisms of synaptic plasticity based on precise spiking patterns. Here we analyze the emergence of global structures in recurrent networks based on a triplet model of spike timing dependent plasticity (STDP) which depends on the interactions of three precisely-timed spikes and can describe plasticity experiments with varying spike frequency better than the classical pair-based STDP rule. We describe synaptic changes arising from emergent higher-order correlations, and investigate their influence on different connectivity motifs in the network. Our motif expansion framework reveals novel motif structures under the triplet STDP rule, which support the formation of bidirectional connections and loops in contrast to the classical pair-based STDP rule. Therefore, triplet STDP drives the spontaneous emergence of self-connected groups of neurons, or assemblies, proposed to represent functional units in neural circuits. Assembly formation has often been associated with plasticity driven by firing rates or external stimuli. We propose that assembly structure can emerge without the need for externally patterned inputs or assuming a symmetric pair-based STDP rule commonly assumed in previous studies. The emergence of non-random network structure under triplet STDP occurs through internally-generated higher-order correlations, which are ubiquitous in natural stimuli and neuronal spiking activity, and important for coding. We further demonstrate how neuromodulatory mechanisms that modulate the shape of triplet STDP or the synaptic transmission function differentially promote connectivity motifs underlying the emergence of assemblies, and quantify the differences using graph theoretic measures.

neuroscience↗

bioRxiv2019

Rapid and Active Stabilization of Visual Cortical Firing Rates Across Light-Dark Transitions

The dynamics of neuronal firing during natural vision are poorly understood. Surprisingly, mean firing rates of neurons in primary visual cortex (V1) of freely behaving rodents are similar during prolonged periods of light and darkness, but it is unknown whether this reflects a slow adaptation to changes in natural visual input, or insensitivity to rapid changes in visual drive. Here we use chronic electrophysiology in freely behaving rats of either sex to follow individual V1 neurons across many dark-light (D-L) and light-dark (L-D) transitions. We show that, even on rapid timescales (1s to 10 min), neuronal activity was only weakly modulated by transitions that coincided with the expected 12h/12h light-dark cycle. In contrast, a larger subset of V1 neurons consistently responded to unexpected L-D and D-L transitions, and disruption of the regular L-D cycle with 60 hours of complete darkness induced a robust increase in V1 firing upon re-introduction of visual input. Thus, V1 neurons fire at similar rates in the presence or absence of natural stimuli, and significant changes in activity arise only transiently in response to unexpected changes in the visual environment. Further, although mean rates were similar in L and D, pairwise correlations were significantly stronger during natural vision, suggesting that information about natural scenes in V1 is more readily extractable from correlations than from individual firing rates. Together, our findings show that V1 firing rates are rapidly and actively stabilized during expected changes in visual input, and are remarkably stable at both short and long timescales.

neuroscience↗

bioRxiv2018

Sensory Deprivation Independently Regulates Neocortical Feedforward and Feedback Excitation-Inhibition Ratio

Brief (2-3d) monocular deprivation (MD) during the critical period induces a profound loss of responsiveness within layer 4 of primary visual cortex (V1). This has largely been ascribed to long-term depression (LTD) at thalamocortical synapses onto pyramidal neurons, while a contribution from intracortical inhibition has been controversial. Here we used optogenetics to probe feedforward thalamocortical and feedback intracortical excitation-inhibition (E-I) ratios following brief MD. While thalamocortical inputs onto pyramidal neurons were depressed, there was stronger depression onto PV+ interneurons, which shifted the thalamocortical-evoked E-I ratio toward excitation. In contrast, feedback intracortical E-I ratio was shifted toward inhibition, and a computational model of layer 4 demonstrated that these opposing shifts produced an overall suppression of layer 4 excitability. Thus, feedforward and feedback E-I ratios onto the same postsynaptic target can be independently regulated by visual experience, and enhanced feedback inhibition is the primary driving force behind loss of visual responsiveness.

neuroscience↗

bioRxiv2018

Specific octopaminergic neurons arbitrate between perseverance and reward in hungry Drosophila

In pursuit of palatable food, hungry animals mobilize significant energy resources and overcome obstacles, exhaustion and fear. Their perseverance depends on metabolic state, internal motivation and the expected benefit. Sustained commitment to a trying task is crucial, however, disengagement from one behavior to engage into another can be essential for optimal adaptation and survival. How neural circuits allow prioritizing perseverance over withdrawal based on the animals need is not understood. Using a single fly spherical treadmill, we show that hungry flies display increasing perseverance to track a food odor in the repeated absence of the predicted food reward. While this perseverance is mediated by a group of dopaminergic neurons, a subset of neurons expressing octopamine, the invertebrate counterpart of noradrenaline, provide reward feedback and counteract dopamine-motivated food seeking. Our data and modeling suggest that two important neuromodulators tally internal and external signals to coordinate motivation-dependent antagonistic behavioral drives: perseverance vs. change of behavior.\n\n\n\nO_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=87 SRC=\"FIGDIR/small/259119_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (21K):\norg.highwire.dtl.DTLVardef@9833e1org.highwire.dtl.DTLVardef@7616d4org.highwire.dtl.DTLVardef@d4e8bforg.highwire.dtl.DTLVardef@1fc615c_HPS_FORMAT_FIGEXP M_FIG C_FIG HighlightsO_LILack of reward stimulates perseverance, and not quitting.\nC_LIO_LIDopaminergic neurons previously implicated in aversive learning promote perseverance.\nC_LIO_LISugar responsive octopaminergic neurons directly counteract perseverant odor tracking through a downstream inhibitory neuron.\nC_LIO_LIComputational modeling supports a simple neural circuit featuring antagonistic functions for dopamine and octopamine as tallies of expense and gain.\nC_LI

neuroscience↗

bioRxiv2017

Optimal Sensory Coding By Populations Of ON And OFF Neurons

In many sensory systems the neural signal is coded by the coordinated response of heterogeneous populations of neurons. What computational benefit does this diversity confer on information processing? We derive an efficient coding framework assuming that neurons have evolved to communicate signals optimally given natural stimulus statistics and metabolic constraints. Incorporating nonlinearities and realistic noise, we study optimal population coding of the same sensory variable using two measures: maximizing the mutual information between stimuli and responses, and minimizing the error incurred by the optimal linear decoder of responses. Our theory is applied to a commonly observed splitting of sensory neurons into ON and OFF that signal stimulus increases or decreases, and to populations of monotonically increasing responses of the same type, ON. Depending on the optimality measure, we make different predictions about how to optimally split a population into ON and OFF, and how to allocate the firing thresholds of individual neurons given realistic stimulus distributions and noise, which accord with certain biases observed experimentally.

neuroscience↗

bioRxiv2017

Understanding Neural Circuit Development Through Theory And Models

How are neural circuits organized and tuned to achieve stable function and produce robust behavior? The organization process begins early in development and involves a diversity of mechanisms unique to this period. We summarize recent progress in theoretical neuroscience that has substantially contributed to our understanding of development at the single neuron, synaptic and network level. We go beyond classical models of topographic map formation, and focus on the generation of complex spatiotemporal activity patterns, their role in refinements of particular circuit features, and the emergence of functional computations. Aided by the development of novel quantitative methods for data analysis, theoretical and computational models have enabled us to test the adequacy of specific assumptions, explain experimental data and propose testable hypotheses. With the accumulation of larger data sets, theory and models will likely play an even more important role in understanding the development of neural circuits.

neuroscience↗