Biology subjects

Palagina, G.

Publications and source records attributed to Palagina, G..

5 recordsLinked to original sources

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

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↗

bioRxiv2022

Tug-of-peace: Visual Rivalry and Atypical Visual Motion Processing in MECP2 Duplication Syndrome of Autism

Extracting common patterns of neural circuit computations in the autism spectrum and confirming them as a cause of specific core traits of autism is the first step towards identifying cell- and circuit-level targets for effective clinical intervention. Studies in human subjects with autism have identified functional links and common anatomical substrates between core restricted behavioral repertoire, cognitive rigidity, and over-stability of visual percepts during visual rivalry. To be able to study these processes with single-cell precision and comprehensive neuronal population coverage, we developed the visual bi-stable perception paradigm for mice. Our task is based on plaid patterns consisting of two transparent gratings drifting at an angle of 120{degrees} relative to each other. This results in spontaneous reversals of the perception between local component motion (motion of the plaid perceived as two separate moving grating components) and integrated global pattern motion (motion of the plaid perceived as a fused moving texture). Furthermore, this robust paradigm does not depend on the explicit report of the mouse, since the direction of the optokinetic nystagmus (OKN, rapid eye movements driven by either pattern or component motion) is used to infer the dominant percept. Using this paradigm, we found that the rate of perceptual reversals between global and local motion interpretations of the stimulus is reduced in the MECP2 duplication mouse model of autism. Moreover, the stability of local motion percepts is greatly increased in MECP2 duplication mice at the expense of global motion percepts. Thus, our model reproduces a subclass of the core features in human autism (reduced rate of visual rivalry and atypical perception of visual motion). This further offers a well-controlled approach for dissecting neuronal circuits underlying these core features.

neuroscience↗

bioRxiv2022

Increased reliability of visually-evoked activity in area V1 of the MECP2-duplication mouse model of autism

Atypical sensory processing is now thought to be a core feature of the autism spectrum. Influential theories have proposed that both increased and decreased neural response reliability within sensory systems could underlie altered sensory processing in autism. Here, we report evidence for abnormally increased reliability of visual-evoked responses in layer 2/3 neurons of adult primary visual cortex in the MECP2-duplication syndrome animal model of autism. Increased response reliability was due in part to decreased response amplitude, decreased fluctuations in endogenous activity, and decreased neuronal coupling to endogenous activity. Similarly to what was observed neuronally, the optokinetic reflex occurred more reliably at low contrasts in mutant mice compared to controls. Retinal responses did not explain our observations. These data suggest that the circuit mechanisms for convolution of sensory-evoked and endogenous signal and noise may be altered in this form of syndromic autism.

neuroscience↗

bioRxiv2018

Inhibitory Units: An Organizing Nidus for Feature-Selective Sub-Networks in Area V1.

Sensory stimuli are encoded by the joint firing of neuronal groups composed of pyramidal cells and interneurons, rather than single isolated neurons (Uhlhaas et al, 2009, Buzsaki, 2010). However, the principles by which these groups are organized to encode information remain poorly understood. A leading hypothesis is that similarly tuned pyramidal cells that preferentially connect to each other may form multi-cellular encoding units yoked to a similar purpose. The existence of such groups would be reflected on the profile of spontaneous events observed in neocortical networks. We used 2-photon calcium imaging to study spontaneous population-burst events in layer 2/3 of mouse area V1 during postnatal maturation (postnatal day 8-52). Throughout the period examined both size and duration of spontaneously occurring population-bursts formed scale-free distributions obeying a power law. The same was true for the degree of \"functional connectivity,\" a measure of pairwise synchrony across cells. These observations are consistent with a hierarchical small-world-net architecture, characterized by groups of cells with high local connectivity (\"small worlds\", cliques) connected to each other via a restricted number of \"hub\" cells\" (Bonifazi et al., 2009, Sporns, 2011, Luce & Perry, 1949). To identify candidate \"small world\" groups we searched for cells whose calcium events had a consistent temporal relationship to events recorded from local inhibitory interneurons. This was guided by the intuition that groups of neurons whose synchronous firing represents a \"temporally coherent computational unit\" (or feature) ought to be inhibited together. This strategy allowed us to identify clusters of pyramidal neurons whose firing is temporally \"linked\" to one or more local interneurons. These \"small-world\" clusters did not remain static, during postnatal development: both cluster size and overlap with other clusters decreased over time as pyramidal neurons became progressively more selective, \"linking\" to fewer neighboring interneurons. Notably, pyramidal neurons in a cluster show higher tuning function similarity than expected with each other and with their \"linked\" interneurons. Our findings suggest that spontaneous population events in the visual cortex are shaped by \"small-world\" networks of pyramidal neurons that share functional properties and work in concert with one or more local interneurons. We argue that such groups represent a fundamental neocortical unit of computation at the population level.

neuroscience↗