Tootfinder

A model of the locust visual system under diverse stimuli highlights functionality of various mechanisms and suggests a parsimonious structure https://www.biorxiv.org/content/10.64898/2026.09.14.751446v1

Long-term stability of cellular-resolution brain-computer interface recordings after stroke #BCI

Long-term stability of cellular-resolution brain-computer interface recordings after stroke
Implantable brain-computer interfaces (iBCIs) with single-neuron resolution are showing great promise for restoring mobility and communication in individuals with spinal cord injury or motor neuron disease. Stroke is the most common cause of acquired brain injury and a major contributor to long-term disability, making chronic stroke a highly relevant indication for iBCIs. However, whether stable intracortical recordings can be obtained from the structurally lesioned human brain is unknown. We r…

cintestinalis: Tadpole larva brain (C. intestinalis)
Entire connectivity matrix for the complete brain of a larva of Ciona intestinalis. Each directed edge represents a synaptic connection from pre-synaptic cell i to post-synaptic cell j (may not be a neuron). Edge weights represent the cumulative depth of presynaptic contacts in µm.
This network has 205 nodes and 2903 edges.
Tags: Biological, Connectome, Weighted

STSBench: A Large-Scale Dataset for Modeling Neuronal Activity in the Dorsal Stream of Primate Visual Cortex
Ethan B. Trepka, Ruobing Xia, Shude Zhu, Sharif Saleki, Danielle Abreu Lopes, Stephen J. Ni\~no Cital, Konstantin F. Willeke, Mindy Kim, Tirin Moore
https://arxiv.org/abs/2607.15631 https://arxiv.org/pdf/2607.15631 https://arxiv.org/html/2607.15631
arXiv:2607.15631v1 Announce Type: new
Abstract: The primate visual system is typically divided into two streams - the ventral stream, responsible for object recognition, and the dorsal stream, responsible for encoding spatial relations and motion. Recent studies have shown that convolutional neural networks (CNNs) pretrained on object recognition tasks are remarkably effective at predicting neuronal responses in the ventral stream, shedding light on the neural mechanisms underlying object recognition. However, similar models of the dorsal stream remain underdeveloped due to the lack of large scale datasets encompassing dorsal stream areas. To address this gap, we present STSBench, a dataset of large-scale, single neuron recordings from over 2,000 neurons in the superior temporal sulcus (STS), a nearly 50-fold increase over existing dorsal stream datasets, collected while Rhesus macaques viewed thousands of unique, natural videos. We show that our dataset can be used for benchmarking encoding models of dorsal stream neuronal responses and reconstructing visual input from neural activity.
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cintestinalis: Tadpole larva brain (C. intestinalis)
Entire connectivity matrix for the complete brain of a larva of Ciona intestinalis. Each directed edge represents a synaptic connection from pre-synaptic cell i to post-synaptic cell j (may not be a neuron). Edge weights represent the cumulative depth of presynaptic contacts in µm.
This network has 205 nodes and 2903 edges.
Tags: Biological, Connectome, Weighted

Critical scaling of cortical ensembles without cooperative amplification https://www.biorxiv.org/content/10.64898/2026.10.07.757392v1 "coordinated firing increases representational specificity without escaping the critical dynamics governing propagation"
Thi…

Neur(o)N – Ghost Frequencies
https://petroglyphmusic.bandcamp.com/album/749-ghost-frequencies
https://…

cintestinalis: Tadpole larva brain (C. intestinalis)
Entire connectivity matrix for the complete brain of a larva of Ciona intestinalis. Each directed edge represents a synaptic connection from pre-synaptic cell i to post-synaptic cell j (may not be a neuron). Edge weights represent the cumulative depth of presynaptic contacts in µm.
This network has 205 nodes and 2903 edges.
Tags: Biological, Connectome, Weighted

(2025) Spatiotemporal properties of cortical excitatory and inhibitory neuron activation by sustained and bursting electrical microstimulation https://www.cell.com/iscience/fulltext/S2589-0042(25)00968-X "Electrodes in visual cortex disrupted circuits, but

Sensory and association networks in mental imagery https://www.cell.com/neuron/abstract/S0896-6273(26)00529-5 "new questions about the role of sensory reinstatement in mental imagery"

cintestinalis: Tadpole larva brain (C. intestinalis)
Entire connectivity matrix for the complete brain of a larva of Ciona intestinalis. Each directed edge represents a synaptic connection from pre-synaptic cell i to post-synaptic cell j (may not be a neuron). Edge weights represent the cumulative depth of presynaptic contacts in µm.
This network has 205 nodes and 2903 edges.
Tags: Biological, Connectome, Weighted

What happens during the 1st millisecond of electrical stimulation of the brain? https://bioniclab.substack.com/p/1st-millisecond-of-electrical-stimulation by TK Kozai in the "Why BCIs Fail" series; "We put the charge in one place and the brain l…

1st millisecond of electrical stimulation
🧠 We know how to read a neuron, and we are learning how to write to one, but we cannot watch the millisecond that decides everything.

cintestinalis: Tadpole larva brain (C. intestinalis)
Entire connectivity matrix for the complete brain of a larva of Ciona intestinalis. Each directed edge represents a synaptic connection from pre-synaptic cell i to post-synaptic cell j (may not be a neuron). Edge weights represent the cumulative depth of presynaptic contacts in µm.
This network has 205 nodes and 2903 edges.
Tags: Biological, Connectome, Weighted

Optotagging in primate cortex uncovers excitatory and inhibitory neuron specializations in form vision #optogenetics

RE: https://mas.to/@seeingwithsound/115274808367581677
Deep learning-based control of electrically evoked activity in human visual cortex

fly_larva: Drosophila larva brain (2023)
A complete synaptic map of the brain connectome of the larva of the fruit fly Drosophila melanogaster. Nodes are neurons, and edges are synaptic connections, traced individually from brain image sections using three-dimensional electron microscopy–based reconstruction. Node metadata include the neuron hempisphere, hemispherical homologue, cell type, annotations, and inferred cluster. Edge metadata include the type of interaction (`'aa'`,…

Analysis of inter-spike interval statistics in neuronal networks with depolarizing and hyperpolarizing threshold potentials
Oliver Gambrell, Abhyudai Singh
https://arxiv.org/abs/2607.18428 https://arxiv.org/pdf/2607.18428 https://arxiv.org/html/2607.18428
arXiv:2607.18428v1 Announce Type: new
Abstract: Neuronal communication is mediated in part by changes in neuronal firing rates. The time interval between successive neuronal firings is referred to as the inter-spike interval (ISI), and quantifying its statistics is important for understanding neuronal communication. This paper studies the ISI statistics of a postsynaptic neuron receiving independent excitatory and inhibitory presynaptic action potentials (EI circuit). This circuit is modeled as a classical integrate-and-fire neuron, and the ISI statistics are investigated for both fixed and adaptive threshold potentials. First, a depolarizing adaptive threshold model is studied, where the threshold potential increases with the postsynaptic membrane potential. Our analysis shows that the ISI noise, quantified as the coefficient of variation, is larger in the adaptive threshold model compared to the fixed threshold model for the same mean ISI. Additionally, simulations reveal that the ISI noise can be either hypo- or hyper-exponential (defined as ISI noise smaller or larger than one, respectively) depending on the frequencies of excitatory and inhibitory inputs. Next, a hyperpolarizing adaptive threshold potential is studied, where the threshold decreases as the membrane potential hyperpolarizes. Interestingly, this model shows that the postsynaptic neuron can generate action potentials (APs) when driven solely by inhibitory inputs. Furthermore, mean and noise signatures are characterized across model parameters for both excitatory and inhibitory inputs. In summary, this work provides a systematic stochastic analysis of adaptive threshold models for AP generation to understand their role in interneuronal information processing.
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cintestinalis: Tadpole larva brain (C. intestinalis)
Entire connectivity matrix for the complete brain of a larva of Ciona intestinalis. Each directed edge represents a synaptic connection from pre-synaptic cell i to post-synaptic cell j (may not be a neuron). Edge weights represent the cumulative depth of presynaptic contacts in µm.
This network has 205 nodes and 2903 edges.
Tags: Biological, Connectome, Weighted

Butterfly Network conference call: "There is this thing in DCI called the butcher factor, which is, you know, how many neurons do I have to kill before I get to the neuron I want to talk to?" https://www.fool.com/earnings/call-transcr

Butterfly (BFLY) Q4 2025 Earnings Call Transcript
Butterfly (BFLY) Q4 2025 Earnings Call Transcript

cintestinalis: Tadpole larva brain (C. intestinalis)
Entire connectivity matrix for the complete brain of a larva of Ciona intestinalis. Each directed edge represents a synaptic connection from pre-synaptic cell i to post-synaptic cell j (may not be a neuron). Edge weights represent the cumulative depth of presynaptic contacts in µm.
This network has 205 nodes and 2903 edges.
Tags: Biological, Connectome, Weighted

Development of a primary visual cortex model to investigate cortical visual prosthesis stimulation #BCI

Development of a Primary Visual Cortex Model to Investigate Cortical Visual Prosthesis Stimulation
A new electrical stimulation focused computational model of the visual cortex had been created to aid in the development of cortical visual prosthesis. The model consists of 10,666 biophysical neurons representing 0.13mm3 of a layer 2/3 of the primary visual cortex and was calibrated to match the baseline activity of rat brain recordings. A novel model of electrical stimulation was developed to allow for selective activation of specific neuron types, and matched the single cell stimulation resp…

cintestinalis: Tadpole larva brain (C. intestinalis)
Entire connectivity matrix for the complete brain of a larva of Ciona intestinalis. Each directed edge represents a synaptic connection from pre-synaptic cell i to post-synaptic cell j (may not be a neuron). Edge weights represent the cumulative depth of presynaptic contacts in µm.
This network has 205 nodes and 2903 edges.
Tags: Biological, Connectome, Weighted

fly_larva: Drosophila larva brain (2023)
A complete synaptic map of the brain connectome of the larva of the fruit fly Drosophila melanogaster. Nodes are neurons, and edges are synaptic connections, traced individually from brain image sections using three-dimensional electron microscopy–based reconstruction. Node metadata include the neuron hempisphere, hemispherical homologue, cell type, annotations, and inferred cluster. Edge metadata include the type of interaction (`'aa'`,…

How the fly holds a single goal: normalization, not selection, in Drosophila FC2
Gioele Nanni, Christopher Lee
https://arxiv.org/abs/2607.18969 https://arxiv.org/pdf/2607.18969 https://arxiv.org/html/2607.18969
arXiv:2607.18969v1 Announce Type: new
Abstract: A walking fly steers toward a goal direction, held as a bump of activity across the FC2 neurons of the fan-shaped body. These neurons also inhibit one another over distance, more strongly the farther apart they are, a feedback proposed to keep the fly on a single goal. We asked, from the connectome, what circuit produces this inhibition, and whether it lets FC2 actively choose one goal among competitors (a winner-take-all) or simply keeps a goal set elsewhere as one clean bump. Tracing the wiring in a single FlyWire brain, we find the inhibition is almost entirely global: four FB5A cells inhibit every FC2 neuron roughly equally, with a smaller, distance-dependent contribution from hDelta interneurons and a negligible direct component. A ring-attractor winner-take-all (the kind the compass uses) requires local recurrent excitation that the FC2 wiring lacks, so this geometry cannot build one; and across a range of dynamical models, including a spiking network, no version of the circuit locks onto a winner at the connectome-scaled reference coupling. FC2 therefore normalizes an externally set goal rather than selecting it, with FB5A likely acting as the global normalizer, much as the APL neuron does in the mushroom body. We are explicit about two open points: a different mechanism, mutual inhibition between two competing goals (which hDelta supplies), could in principle select at very strong coupling, and we bound rather than exclude it; and FB5A's inhibitory identity is a low-confidence prediction of the connectome's transmitter classifier, not yet measured, and likely not GABAergic. We then ask where the goal is actually set: the connectome nominates an upstream hDelta network and rules out the leading proposed alternative, whose neurons supply under 0.2% of FC2's input. Finally, we propose a direct experiment, silencing FB5A while imaging FC2, that would test the account.
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cintestinalis: Tadpole larva brain (C. intestinalis)
Entire connectivity matrix for the complete brain of a larva of Ciona intestinalis. Each directed edge represents a synaptic connection from pre-synaptic cell i to post-synaptic cell j (may not be a neuron). Edge weights represent the cumulative depth of presynaptic contacts in µm.
This network has 205 nodes and 2903 edges.
Tags: Biological, Connectome, Weighted