Biology subjects

Papadopouli, M.

Publications and source records attributed to Papadopouli, M..

5 recordsLinked to original sources

bioRxiv2025

On Temporal Robustness & Brain-State Stability of Functional Connectivity in Mouse Primary Visual Area V1 compared to Higher Visual Area AL

Understanding how the structure of functional connectivity in the visual cortex changes over time and across brain states is crucial for elucidating the mechanisms by which neurons coordinate to process information and support behavior. Higher-order visual areas in mice are known to exhibit more distinct, segregated functional roles compared to the primary visual cortex (V1) [1], and they maintain stimulus representations over extended time scales [2]. However, the stability of the architecture of their functional connectivity across time and brain states remains less understood. In vivo mesoscopic two-photon calcium imaging was used to simultaneously record activity from thousands of neurons across V1 and the extra-striate anterolateral area (AL) in mice, during both visual stimulation (optical-flow/motion) and at resting state (i.e., absence of stimulus). We then applied the spike time-tiling (STTC) coefficient [3] to estimate the pairwise correlations of the neuronal firing and form the functional connectivity at the cell resolution. We then comparatively analyzed the functional connections within area AL and V1 under both stimulus-driven and resting-state conditions. The functional connectivity within AL remains consistently more robust over time than in area V1. Moreover, the structure of the functional connectivity in AL exhibits a smaller change between these two conditions compared to V1, indicating that functional connectivity derived from spontaneous activity more faithfully reflects the functional network architecture elicited by visual stimulation in this higher-order area. Finally, during the resting state, AL activity and functional connectivity are less dependent on pupil size than those of V1, indicating that arousal exerts a weaker modulatory effect on AL compared to V1.

neuroscience↗

bioRxiv2025

Disentangling Stimulus & Population Dynamics in Mouse V1: Orthogonal Subspace Decomposition for Neural Representation

Understanding how the primary visual cortex of mice represents the external sensory input separately from the internal states is a fundamental challenge in systems neuroscience. Our work contributes to the problem of decoupling the stimulus-driven and internally generated components of neural activity in the primary visual cortex. Internally generated (or intrinsic) activity refers to neural dynamics that are not directly driven by sensory stimuli, reflecting the brains ongoing, endogenous processes. Neuronal activity encodes both external stimuli and internal cortical states. The internally modulated activity, though not directly observable, can be inferred from the shared structure in population responses, and thus, serves as a proxy for the internal cortical state. We developed a two-phase Partial Least Squares Regression (PLSR) framework that decomposes neural activity into two orthogonal low-dimensional subspaces: (1) a "population" sub-space capturing global variability shared across neurons, and (2) a "stimulus" subspace containing dimensions that discriminate between stimulus conditions while being linearly uncorrelated with the population subspace. We focus on the granular (L4) and supragranular (L2/3) layers of awake mice exposed to visual stimuli consisting of optical flow directions, using mesoscopic two-photon calcium imaging. In both L4 and L2/3 layers, many components individually yield above-chance decoding accuracy, yet a small low-dimensional subspace preserves nearly the full decoding performance of the high-dimensional population. Stimulus-driven components exhibit strong cross-mouse correlations, indicating a conserved coding scheme present in both L4 and L2/3. These components are stable across the entire recording session, reflecting robustness of the underlying representation over time. Removing the global modulation did not abolish stimulus discriminability in either layer, suggesting that information about stimulus direction is not dependent on this global signal. Both L4 and L2/3 stimulus components exhibit comparable decoding performance as well as similar tuning representations, suggesting common encoding of stimulus direction across layers.

neuroscience↗

bioRxiv2025

Direction of motion decoding in mouse V1: Neuron predictive power relates to functional connectivity organization

Variability in single neuron responses presents a challenge in establishing reliable representations of visual stimuli essential for driving behavior. To enhance accuracy, integration of responses from multiple neurons is imperative. This study leverages simultaneous recordings from a large population (tens of hundreds) of neurons, achieved through in vivo mesoscopic 2-photon calcium imaging of the primary visual cortex (V1) in mice, under visual stimulus conditions as well as in resting state (absence of stimulus). The visual stimulus consisted of 16 distinct randomly shuffled directions of motion presented to the mice. We employed mutual information to identify neurons that contain the most significant information about the stimulus direction. As expected, neurons displaying high predictive power (HPP) in stimulus decoding exhibit elevated firing event rates during stimulus presentation. Furthermore, functional connectivity among HPP neurons during visual stimulation is denser and stronger compared to functional connectivity among other visually responsive neurons. Functional connections among HPP neurons appear to form independently of distance, suggesting a distributed yet highly coordinated network. In contrast, HPP neuronal activity and functional connectivity differed significantly at resting state. Specifically, during the resting state, HPP neurons exhibited lower event rates and functional connectivity structure that was not significantly different from that of other visually responsive neurons. This suggests that HPP neurons are less susceptible to being driven simultaneously by internal brain states in the absence of a stimulus. Finally, the tuning properties of HPP neurons were unexpectedly diverse: while some were sharply tuned, others conveyed a similar amount of mutual information, despite exhibiting much weaker tuning. This study sheds light on the organization of neuronal ensembles important for decoding visual motion direction in mouse area V1, contributing to the understanding of information processing in mouse visual cortex.

neuroscience↗

bioRxiv2024

Trial-by-trial inter-areal interactions in visual cortex in the presence or absence of visual stimulation

State-of-the-art computational models of vision largely focus on fitting trial-averaged spike counts to visual stimuli using overparameterized neural networks. However, a computational model of the visual cortex should predict the dynamic responses of neurons in single trials across different experimental conditions. In this study, we investigated trial-by-trial inter-areal interactions in the visual cortex by predicting neuronal activity in one area based on activity in another, distinguishing between stimulus-driven and non-stimulus-driven shared variability. We analyzed two datasets: calcium imaging from mouse V1 layers 2/3 and 4, and extracellular neurophysiological recordings from macaque V1 and V4. Our results show that neuronal activity can be predicted bidirectionally between L2/3 and L4 in mice, and between V1 and V4 in macaque monkeys, with the latter interaction exhibiting directional asymmetry. The predictability of neuronal responses varied with the type of visual stimulus, yet responses could also be predicted in the absence of visual stimulation. In mice, we observed a bimodal distribution of neurons, with some neurons primarily driven by visual inputs and others showing predictable activity during spontaneous activity despite lacking consistent visually evoked responses. Predictability also depended on intrinsic neuronal properties, receptive field overlap, and the relative timing of activity across areas. Our findings highlight the presence of both stimulus- and non-stimulus-related components in interactions between visual areas across diverse contexts and underscore the importance of non-visual shared variability between visual regions in both mice and macaques.

neuroscience↗

bioRxiv2024

Brain orchestra under spontaneous conditions: Identifying communication modules from the functional architecture of area V1

While single-neuron responses in mouse V1 are well characterized, less is known about how functional ensembles-- groups of neurons that co-activate more frequently than expected by chance--emerge as computational units within laminar V1 circuits. Even with increasingly detailed knowledge of structural connectivity, the rules governing ensemble organization and interactions remain unclear. We imaged pyramidal neurons across granular (L4) and supragranular (L2/3) layers of mouse V1 and applied pairwise functional connectivity analysis to identify multi-neuronal ensembles as putative information-processing modules. In the absence of visual stimulation, 19-34% of pyramidal pairs within 300{micro}m were functionally connected, declining to 10% at 1 mm. Layer 2 to 4 laminar networks exhibited a small-world architecture, L4 displaying slightly denser connectivity and a near-uniform degree-of-connectivity distribution. We propose that neurons together with their first-order functionally connected (1FC) partners constitute putative elementary units of cortical computation. The firing probability of layer 2/3 neurons exhibits a ReLU-like nonlinearity, emerging when [≥] 13% of L4-1FC "putative inputs" co-fire, yielding sparse yet reliable responses. Moreover, L2/3 neuronal responses depend on the count (N), not the identity, of co-active L4-1FC partners, with response sensitivity scaling as a power law in N. These properties persist during visual stimulation and across different states of alertness. Interestingly, L2/3 neurons with L4-1FC modules of different sizes exhibit distinct coupling to brain-state and different computational signatures. This framework yields mechanistic insight into cortical circuit organization, complementary to structural connectivity, helping to link biological circuitry to deep-learning models of artificial intelligence.

neuroscience↗