@article{14887, abstract = {Episodic memories are encoded by experience-activated neuronal ensembles that remain necessary and sufficient for recall. However, the temporal evolution of memory engrams after initial encoding is unclear. In this study, we employed computational and experimental approaches to examine how the neural composition and selectivity of engrams change with memory consolidation. Our spiking neural network model yielded testable predictions: memories transition from unselective to selective as neurons drop out of and drop into engrams; inhibitory activity during recall is essential for memory selectivity; and inhibitory synaptic plasticity during memory consolidation is critical for engrams to become selective. Using activity-dependent labeling, longitudinal calcium imaging and a combination of optogenetic and chemogenetic manipulations in mouse dentate gyrus, we conducted contextual fear conditioning experiments that supported our model’s predictions. Our results reveal that memory engrams are dynamic and that changes in engram composition mediated by inhibitory plasticity are crucial for the emergence of memory selectivity.}, author = {Feitosa Tomé, Douglas and Zhang, Ying and Aida, Tomomi and Mosto, Olivia and Lu, Yifeng and Chen, Mandy and Sadeh, Sadra and Roy, Dheeraj S. and Clopath, Claudia}, issn = {1546-1726}, journal = {Nature Neuroscience}, publisher = {Springer Nature}, title = {{Dynamic and selective engrams emerge with memory consolidation}}, doi = {10.1038/s41593-023-01551-w}, year = {2024}, } @article{15171, abstract = {The brain’s functionality is developed and maintained through synaptic plasticity. As synapses undergo plasticity, they also affect each other. The nature of such ‘co-dependency’ is difficult to disentangle experimentally, because multiple synapses must be monitored simultaneously. To help understand the experimentally observed phenomena, we introduce a framework that formalizes synaptic co-dependency between different connection types. The resulting model explains how inhibition can gate excitatory plasticity while neighboring excitatory–excitatory interactions determine the strength of long-term potentiation. Furthermore, we show how the interplay between excitatory and inhibitory synapses can account for the quick rise and long-term stability of a variety of synaptic weight profiles, such as orientation tuning and dendritic clustering of co-active synapses. In recurrent neuronal networks, co-dependent plasticity produces rich and stable motor cortex-like dynamics with high input sensitivity. Our results suggest an essential role for the neighborly synaptic interaction during learning, connecting micro-level physiology with network-wide phenomena.}, author = {Agnes, Everton J. and Vogels, Tim P}, issn = {1546-1726}, journal = {Nature Neuroscience}, publisher = {Springer Nature}, title = {{Co-dependent excitatory and inhibitory plasticity accounts for quick, stable and long-lasting memories in biological networks}}, doi = {10.1038/s41593-024-01597-4}, year = {2024}, } @article{12349, abstract = {Statistics of natural scenes are not uniform - their structure varies dramatically from ground to sky. It remains unknown whether these non-uniformities are reflected in the large-scale organization of the early visual system and what benefits such adaptations would confer. Here, by relying on the efficient coding hypothesis, we predict that changes in the structure of receptive fields across visual space increase the efficiency of sensory coding. We show experimentally that, in agreement with our predictions, receptive fields of retinal ganglion cells change their shape along the dorsoventral retinal axis, with a marked surround asymmetry at the visual horizon. Our work demonstrates that, according to principles of efficient coding, the panoramic structure of natural scenes is exploited by the retina across space and cell-types.}, author = {Gupta, Divyansh and Mlynarski, Wiktor F and Sumser, Anton L and Symonova, Olga and Svaton, Jan and Jösch, Maximilian A}, issn = {1546-1726}, journal = {Nature Neuroscience}, pages = {606--614}, publisher = {Springer Nature}, title = {{Panoramic visual statistics shape retina-wide organization of receptive fields}}, doi = {10.1038/s41593-023-01280-0}, volume = {26}, year = {2023}, } @article{12244, abstract = {Environmental cues influence the highly dynamic morphology of microglia. Strategies to characterize these changes usually involve user-selected morphometric features, which preclude the identification of a spectrum of context-dependent morphological phenotypes. Here we develop MorphOMICs, a topological data analysis approach, which enables semiautomatic mapping of microglial morphology into an atlas of cue-dependent phenotypes and overcomes feature-selection biases and biological variability. We extract spatially heterogeneous and sexually dimorphic morphological phenotypes for seven adult mouse brain regions. This sex-specific phenotype declines with maturation but increases over the disease trajectories in two neurodegeneration mouse models, with females showing a faster morphological shift in affected brain regions. Remarkably, microglia morphologies reflect an adaptation upon repeated exposure to ketamine anesthesia and do not recover to control morphologies. Finally, we demonstrate that both long primary processes and short terminal processes provide distinct insights to morphological phenotypes. MorphOMICs opens a new perspective to characterize microglial morphology.}, author = {Colombo, Gloria and Cubero, Ryan J and Kanari, Lida and Venturino, Alessandro and Schulz, Rouven and Scolamiero, Martina and Agerberg, Jens and Mathys, Hansruedi and Tsai, Li-Huei and Chachólski, Wojciech and Hess, Kathryn and Siegert, Sandra}, issn = {1546-1726}, journal = {Nature Neuroscience}, keywords = {General Neuroscience}, number = {10}, pages = {1379--1393}, publisher = {Springer Nature}, title = {{A tool for mapping microglial morphology, morphOMICs, reveals brain-region and sex-dependent phenotypes}}, doi = {10.1038/s41593-022-01167-6}, volume = {25}, year = {2022}, } @misc{6995, abstract = {Human brain organoids represent a powerful tool for the study of human neurological diseases particularly those that impact brain growth and structure. However, many neurological diseases lack obvious anatomical abnormalities, yet significantly impact neural network functions, raising the question of whether organoids possess sufficient neural network architecture and complexity to model these conditions. Here, we explore the network level functions of brain organoids using calcium sensor imaging and extracellular recording approaches that together reveal the existence of complex oscillatory network behaviors reminiscent of intact brain preparations. We further demonstrate strikingly abnormal epileptiform network activity in organoids derived from a Rett Syndrome patient despite only modest anatomical differences from isogenically matched controls, and rescue with an unconventional neuromodulatory drug Pifithrin-α. Together, these findings provide an essential foundation for the utilization of human brain organoids to study intact and disordered human brain network formation and illustrate their utility in therapeutic discovery.}, author = {Samarasinghe, Ranmal A. and Miranda, Osvaldo and Buth, Jessie E. and Mitchell, Simon and Ferando, Isabella and Watanabe, Momoko and Kurdian, Arinnae and Golshani, Peyman and Plath, Kathrin and Lowry, William E. and Parent, Jack M. and Mody, Istvan and Novitch, Bennett G.}, issn = {1546-1726}, pages = {32}, publisher = {Springer Nature}, title = {{Identification of neural oscillations and epileptiform changes in human brain organoids}}, doi = {10.1038/s41593-021-00906-5}, volume = {24}, year = {2021}, } @article{9439, abstract = {The ability to adapt to changes in stimulus statistics is a hallmark of sensory systems. Here, we developed a theoretical framework that can account for the dynamics of adaptation from an information processing perspective. We use this framework to optimize and analyze adaptive sensory codes, and we show that codes optimized for stationary environments can suffer from prolonged periods of poor performance when the environment changes. To mitigate the adversarial effects of these environmental changes, sensory systems must navigate tradeoffs between the ability to accurately encode incoming stimuli and the ability to rapidly detect and adapt to changes in the distribution of these stimuli. We derive families of codes that balance these objectives, and we demonstrate their close match to experimentally observed neural dynamics during mean and variance adaptation. Our results provide a unifying perspective on adaptation across a range of sensory systems, environments, and sensory tasks.}, author = {Mlynarski, Wiktor F and Hermundstad, Ann M.}, issn = {1546-1726}, journal = {Nature Neuroscience}, pages = {998--1009}, publisher = {Springer Nature}, title = {{Efficient and adaptive sensory codes}}, doi = {10.1038/s41593-021-00846-0}, volume = {24}, year = {2021}, }