Topological richness grows with network size, and second homology emerges significantly above the null only in the larger networks, and results show that persistent homology resolves structured topology in neural recordings at the scale experiments actually deliver.
Abstract
Neural activity is widely held to organize on low-dimensional structure embedded in a high-dimensional state space. Persistent homology reads such structure directly from the pattern of pairwise correlations, without assuming in advance which variables are relevant. We apply persistent homology to microelectrode-array (MEA) recordings of spontaneous activity from human (Lancaster) and mouse (Paşca) cortical organoids, spanning 26–234 simultaneously sorted units, and ask whether topological data analysis resolves structure at the node counts that neural recordings actually deliver. Building weighted networks in correlation space and characterizing them by Vietoris–Rips filtration, we find that the first homology (H1, loops) rises significantly above a rate- and population-preserving null in 14 of 18 datasets. This loop structure occupies a non-redundant core: it is robust to random removal of units yet disrupted by targeted removal of the units that carry it. Topological richness grows with network size, and second homology (H2) emerges significantly above the null only in the larger networks. These results show that persistent homology resolves structured topology in neural recordings at the scale experiments actually deliver.
The brain can rapidly adjust sensory processing according to behavioral context from moment to moment without altering its underlying anatomical wiring. Such flexibility is thought to arise from dynamic reconfiguration of the effective network through which signals propagate, yet the principles governing such reconfiguration and its consequences for coding remain unclear. Here, we exploited the distinct stationary and locomotion states within the same recording sessions and addressed this question by inferring directed, millisecond-scale signal-transmission networks at single-neuron resolution in behaving mice. Locomotion was accompanied by a counterintuitive structured sparsification of the inferred network: interactions became fewer but more temporally precise, while the remaining interactions were more local, modular, feature-specific, and feedforward. We then used theoretical analysis and controlled perturbations of multi-area rate models to systematically determine how each empirically observed component of this reorganization affects population coding. We found that sparsification reduced shared variability; local organization reduced signal–noise alignment; feature-specific interactions sharpened selectivity; and a more feedforward architecture accelerated decoding. These results provide an experimentally grounded mechanism by which behavioral state reorganizes neuronal interactions given the same sensory inputs, and suggest structured sparsification as an underlying principle of network reconfiguration that supports accurate and faster sensory coding.
A graph-computational framework to quantify stimulus-evoked propagation and revealed a control-validated phenomenon, that repeated stimulation reshapes organoid networks is established, but longitudinal designs in which every preparation is stimulated cannot separate this from developmental maturation.
E. Nadimi, V. C. Gogineni, Jan-Matthias Braun et al.· arXiv.org· 0 citations
In 1949, Donald Hebb proposed that neuronal assemblies with temporally specific patterns of activity form the building blocks of perception, cognition, and behavior. Finding the structural underpinning of such assemblies has been technically challenging due to a lack of large-scale structure-activity maps. Here, we combine in vivo optical physiology with postmortem electron microscopy (EM) in the same tissue volume. Using higher-order correlations in fluorescence traces, we extract neuronal assemblies. Physiologically, we show that these assemblies respond more reliably to repeated natural movies than size-matched control ensembles and decode such stimuli more accurately. Structurally, we find that over a quarter of the pyramidal neurons do not participate in any assembly and are significantly less integrated into the connectome than those that do. We do not observe a marked increase in the strength of monosynaptic excitatory connections between neurons sharing assembly assignment, but instead find significantly stronger indirect inhibitory connections targeting cells in other assemblies. These results show that assemblies can serve as functional units of perception and suggest they may be structurally delineated by mutual inhibition.
J. Wagner-Carena, Sai Kate, Trevor Riordan et al.· Cell Reports· 0 citations
Human cortical activity reflects interactions between local recurrent circuits and distributed brain-wide inputs, but how these contributions shape cortical dynamics remains unclear. We compared oscillatory and single unit activity in laminar recordings from the same human cortical regions in eight patients across wakefulness, NREM sleep, and acute slices after surgical isolation. Gamma-band spike-field synchronization increased from wakefulness to sleep and isolated cortex, whereas population coupling, putative connectivity, network integration, and dynamical dimensionality declined. Laminar gamma sink-source organization persisted in isolation, indicating the presence of local gamma-generating mechanisms. Integration with histological data showed preserved tissue architecture, while neuronal density predicted population integration in vivo but not after isolation. Removing long-range inputs thus did not suppress cortical activity but shifted its organization toward stronger rhythmic coordination and reduced integration. Our findings suggest that neocortical microcircuits intrinsically generate coherent gamma activity, whereas network embedding supports the diverse, high-dimensional dynamics of the intact cortex.
This work introduces an alternative criterion based on the persistent topological cycles in which each node participates---a measure of mesoscale integration that captures features beyond local connectivity---and demonstrates that persistent topology captures information about brain network control that scalar energy summaries miss.
Carter Sale, Marco Coraggio, Mengsen Zhang et al.· 0 citations
How the brain’s physical geometry gives rise to its flexible functional repertoire remains a central question in neuroscience. Here, we trained three classes of recurrent neural networks (RNNs) on a working-memory task, forming a graded hierarchy of spatial constraints: Vanilla RNNs (no spatial constraints), Masked RNNs (projection constraints limiting where information enters and leaves the network), and biophysical RNNs (bioRNNs; projection constraints and spatial embedding of the networks’ connectivity using the brain’s inter-regional Euclidean geometry). We assessed how well each RNN class predicted empirical fMRI activity without exposing them to it during training. Our results showed that bioRNNs were the only networks to successfully predict empirical brain activity and to organize their dynamics into a spatial pattern that recapitulated the brain’s principal hierarchy (the sensorimotor–association axis). Additionally, bioRNNs’ ability to predict empirical brain activity emerged along a trajectory in which geometry was laid down first, then partly traded back as the task was mastered. Importantly, brain-like topological features emerged in bioRNNs as they increased their task proficiency while maintaining their ability to predict brain activity. Taken together, our results indicate that physical geometry and cognitive inputs play distinct, complementary roles: while geometry constrains the space of possible brain dynamics, cognitive inputs determine which dynamics are expressed. They also situate topology as the scaffold through which the physically embedded brain reconciles wiring costs and computational demands.
Ahmad Beyh, Jason Z. Kim, W. Bajwa et al.· bioRxiv· 0 citations
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