Biology subjects

Kostadinov, D.

Publications and source records attributed to Kostadinov, D..

6 recordsLinked to original sources

bioRxiv2026

Early development of cerebellar functional connectivity

The cerebellum makes a critical contribution to cognitive and behavioural function, extending well beyond motor control, and is recognised as an important determinant of neurodevelopmental outcome. Despite this, little is known about the foundations of cerebellar functional organisation in early life. Addressing this gap is essential for establishing typical developmental trajectories against which atypical development can be assessed. Here, we analysed fMRI data from term-born neonates (n=155) scanned between 37 and 44 weeks postmenstrual age and mapped intra-cerebellar and cerebro-cerebellar functional connectivity across multiple levels of organisation. We identified 13 intrinsic connectivity networks (ICNs) within the neonatal cerebellum that recapitulated adult subdivisions, with two ICNs showing age-related increases in coverage and strength. Analysis of cerebro-cerebellar connectivity identified 15 sensory, motor and association cerebral clusters, seven increasing connectivity with age. Functional gradient analysis identified a continuous axis from somatomotor to association regions within intra-cerebellar and cerebro-cerebellar organisation, which became more pronounced with age. Together, these findings suggest that the foundations of adult cerebellar functional organisation are present at birth but undergo rapid and regionally heterogeneous maturation over the first weeks of postnatal life. This developmental framework provides a normative reference for understanding cerebellar development and its implications for later neurodevelopmental variation.

neuroscience↗

bioRxiv2026

Logic of brain-wide connections with the cerebellar cortex

The cerebellum governs movement and cognition through multiple lobules that carry distinct signals and connect to distinct long-range brain networks. How these specialized computations are reconciled with the brain-wide convergence required for coherent behaviour is unknown, in part because the input-output connectivity of functionally distinct lobules has not been mapped. We combined anterograde and retrograde transsynaptic viral tracing with whole-brain two-photon tomography to map the monosynaptic and disynaptic inputs and outputs of Lobule V (LV) and Simplex (LS), two lobules preferentially involved in movement and reward, respectively. Monosynaptic connectivity was strongly segregated: LV and LS received climbing fiber and mossy fiber inputs from largely non-overlapping source regions and projected to distinct targets within the cerebellar nuclei. In contrast, disynaptic inputs to LV- and LS-projecting olivary neurons arose from a shared set of forebrain, midbrain, and hindbrain regions, with the segregation re-emerging at the level of fine-scale spatial topography within these regions. Disynaptic outputs through the cerebellar nuclei likewise overlapped more than the monosynaptic projections to the nuclei themselves. These findings reveal that overlapping brain-wide pathways interface with locally specialized monosynaptic cerebellar pathways. This organizational logic could support distinct movement- and reward-related computations in the cerebellum while distributing outputs to shared downstream networks, providing an architectural substrate for both segregating and integrating cerebellar motor and cognitive functions.

neuroscience↗

bioRxiv2025

Fast and slow learning mediated by distinct climbing fiber signals

Learning on both fast and slow timescales is required to enable us to adapt to a dynamic environment. Whether the mechanisms mediating fast and slow learning are implemented by the same, or different, circuit elements remains an important open question. Learning involving the cerebellum is known to be driven primarily by instructive climbing fiber inputs to Purkinje cells, but the dynamics of climbing fibers across learning on fast and slow timescales are not known. It is unclear if climbing fibers encode the same or different instructive signals at different stages of learning, and whether learning-related changes in climbing fiber encoding properties across timescales are driven by the same or distinct mechanisms. We addressed this problem using longitudinal 2-photon calcium imaging of climbing fiber activity across multiple cerebellar lobules as mice learned to execute a visuomotor integration task - slow learning - and then rapidly adapted to a new sensorimotor coupling - fast learning. Instructive signals are spatially segregated in trained mice: Lobule V preferentially encodes movement predictively, while Lobule simplex preferentially receives reward-related feedback. Moreover, the encoding of sensorimotor and reward-related instructive signals is dynamic on both slow and fast timescales: movement-related activity emerges over the course of training, while reward-related activity progressively diminishes as mice become experts but re-emerges specifically in Lobule simplex when rewards become scarce. Finally, while the same climbing fiber inputs can change on both timescales, the magnitude and spatial organization of slow changes are not related to fast changes. Thus, climbing fiber input can carry distinct instructive signals on different timescales - a multiplexing of teaching signals that may underlie the cerebellums capacity to both acquire and refine motor behaviour.

neuroscience↗

bioRxiv2024

A deep-learning strategy to identify cell types across species from high-density extracellular recordings

High-density probes allow electrophysiological recordings from many neurons simultaneously across entire brain circuits but dont reveal cell type. Here, we develop a strategy to identify cell types from extracellular recordings in awake animals, revealing the computational roles of neurons with distinct functional, molecular, and anatomical properties. We combine optogenetic activation and pharmacology using the cerebellum as a testbed to generate a curated ground-truth library of electrophysiological properties for Purkinje cells, molecular layer interneurons, Golgi cells, and mossy fibers. We train a semi-supervised deep-learning classifier that predicts cell types with greater than 95% accuracy based on waveform, discharge statistics, and layer of the recorded neuron. The classifiers predictions agree with expert classification on recordings using different probes, in different laboratories, from functionally distinct cerebellar regions, and across animal species. Our classifier extends the power of modern dynamical systems analyses by revealing the unique contributions of simultaneously-recorded cell types during behavior.

neuroscience↗

bioRxiv2021

A rapid and efficient learning rule for biological neural circuits

The dominant view in neuroscience is that changes in synaptic weights underlie learning. It is unclear, however, how the brain is able to determine which synapses should change, and by how much. This uncertainty stands in sharp contrast to deep learning, where changes in weights are explicitly engineered to optimize performance. However, the main tool for that, backpropagation, has two problems. One is neuro-science related: it is not biologically plausible. The other is inherent: networks trained with this rule tend to forget old tasks when learning new ones. Here we introduce the Dendritic Gated Network (DGN), a variant of the Gated Linear Network, which offers a biologically plausible alternative to backpropagation. DGNs combine dendritic gating (whereby interneurons target dendrites to shape neuronal responses) with local learning rules to yield provably efficient performance. They are significantly more data efficient than conventional artificial networks, and are highly resistant to forgetting. Consequently, they perform well on a variety of tasks, in some cases better than backpropagation. Importantly, DGNs have structural and functional similarities to the cerebellum, a link that we strengthen by using in vivo two-photon calcium imaging to show that single interneurons suppress activity in individual dendritic branches of Purkinje cells, a key feature of the model. Thus, DGNs leverage targeted dendritic inhibition and local learning - two features ubiquitous in the brain - to achieve fast and efficient learning.

neuroscience↗

bioRxiv2020

Neuropixels 2.0: A miniaturized high-density probe for stable, long-term brain recordings

To study the dynamics of neural processing across timescales, we require the ability to follow the spiking of thousands of individually separable neurons over weeks and months, during unrestrained behavior. To address this need, we introduce the Neuropixels 2.0 probe together with novel analysis algorithms. The new probe has over 5,000 sites and is miniaturized such that two probes plus a headstage, recording 768 sites at once, weigh just over 1 g, suitable for implanting chronically in small mammals. Recordings with high quality signals persisting for at least two months were reliably obtained in two species and six different labs. Improved site density and arrangement combined with new data processing methods enable automatic post-hoc stabilization of data despite brain movements during behavior and across days, allowing recording from the same neurons in the mouse visual cortex for over 2 months. Additionally, an optional configuration allows for recording from multiple sites per available channel, with a penalty to signal-to-noise ratio. These probes and algorithms enable stable recordings from >10,000 sites during free behavior in small animals such as mice.

neuroscience↗