iiinova — where your digital self keeps evolving

Reposted by Nicholas Tolley

Ahmad Beyh @ahmadbeyh.bsky.social · 24/08/2026

We trained 3 RNN classes on a working memory task. (1) Vanilla RNNs were only task-optimized. (2) Masked RNNs had brain-like input/output constraints. (3) bioRNNs were further constrained such that their connections were shaped by the brain’s Euclidean geometry. (2/8)

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Thomas Serre @thomasserre.bsky.social · 13/08/2026

We're recruiting postdoc fellows in computational neuroscience for Brown's NIH T32 Training Program — brain & cognitive modeling from biophysics to AI, with connections to mental health & psychiatry. US citizens/PRs. Review begins Sept 1. @carneyinstitute @browncopsy apply.interfolio.com/190111

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Mehdi Senoussi @cogsenoussi.bsky.social · 31/07/2026

📢 PhD opening in comp. cog. neuro. in Toulouse: thalamo-frontal model of cognitive control. Modelling/EEG experience is welcome but not required. Application deadline: 8 Sept. 2026 Details: sdrive.cnrs.fr/s/eDrkeXfjdW... #CognitiveScience #Neuroscience #EEG #neuroskyence #compneurosky

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Reposted by Nicholas Tolley

bioRxiv Neuroscience @biorxiv-neursci.bsky.social · 16/06/2026

Spectral decompositions of neural voltage recordings are susceptible to model misspecifications that cause meaningful estimation error www.biorxiv.org/content/10.64898/20…

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Reposted by Nicholas Tolley

bioRxiv Neuroscience @biorxiv-neursci.bsky.social · 09/06/2026

HSSM: A Widely Applicable Toolbox for Hierarchical Bayesian Neuro-cognitive Modeling www.biorxiv.org/content/10.64898/20…

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Prof Rick Adams @drrickadams.bsky.social · 18/05/2026

📢PhD job alert! A exciting PhD project that involves using MEG and biophysical modelling to try to predict effects of a glutamatergic treatment for psychosis... plus you can develop your own side project. Join our great team! www.ucl.ac.uk/work-at-ucl/...

ucl.ac.uk

UCL – University College London

UCL is consistently ranked as one of the top ten universities in the world (QS World University Rankings 2010-2022) and is No.2 in the UK for research power (Research Excellence Framework 2021).

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DurstewitzLab @durstewitzlab.bsky.social · 10/05/2026

In a #ICML2026 position paper we argue a dynamical systems perspective is needed to drive time series models forward: arxiv.org/abs/2602.16864 For TS, we need to move away from transformers that do not respect a system’s dynamical structure, esp. if out-of-domain generalization & insight is sought.

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Aperture Neuro @apertureohbm.bsky.social · 08/05/2026

Donoghue et al. introduce the Human Single-Neuron Pipeline, an open source processing pipeline for collecting and analyzing human single-neuron data: doi.org/10.52294/001... @ohbmossig.bsky.social @ohbmofficial.bsky.social #OpenDatasets #SpecialIssues

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Nicholas Tolley @ntolley.bsky.social · 05/05/2026

Super glad to have our team's work on clinical applications of neural simulations highlighted in this article! Trying to bring academic work to the real-world has been a great experience and I'm quite excited to continue these efforts with @dvwz.bsky.social and @jonescompneurolab.bsky.social

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Reposted by Nicholas Tolley

bioRxiv Neuroscience @biorxiv-neursci.bsky.social · 01/05/2026

Layer-specific wide-field calcium imaging of neocortical activity www.biorxiv.org/content/10.64898/20…

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Imaging Neuroscience @imagingneurosci.bsky.social · 01/05/2026

New paper in Imaging Neuroscience by Lindsey Power, Sylvain Baillet, et al: A neuroscientist’s guide to neural burst detection doi.org/10.1162/IMAG...

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Reposted by Nicholas Tolley

Debbie Yee @debyee.bsky.social · 29/04/2026

Headed to #SOBP2026? Come visit our posters on stress and mental effort! F325: Investigating the Role of Serotonin in Stressor Controllability and Mental Effort Allocation (me!) S297: Learning and Generalization of Stressor Controllability for Mental Effort allocation (Tony El Nemer)

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Nicholas Tolley @ntolley.bsky.social · 27/04/2026

Really excited for this conference! We’ve all been digging deep into different ways to use EEG to better understand and treat psychiatric diseases If you’re at SOBP definitely stop by our posters if you want to chat biophysical mechanisms, biomarkers, and deep learning approaches to EEG analysis

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Reposted by Nicholas Tolley

Jan R. Wessel @janw.bsky.social · 24/04/2026

New work, now out in PLoS Biology. Using intracranial recordings & EEG, we find that the human STN rapidly engages / releases motor inhibition depending on task context, does so in tight accord w/ frontal cortex, and shows little to no lateralization while doing so. journals.plos.org/plosbiology/...

journals.plos.org

A cortico-subthalamic circuit rapidly engages and releases inhibition of specific movements depending on the environmental context

How does the human brain stop movements under realistic conditions? Using intracranial recordings and multi-variate EEG decoding, this study shows that a fronto-basal ganglia circuit featuring the sub...

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Reposted by Nicholas Tolley

Ann Huang @annhuang42.bsky.social · 23/04/2026

[ #ICLR2026 ] How do we know if two systems are performing the same computation when they are constantly driven by different external inputs? 🧠🤖 I’ll be presenting our novel method InputDSA tomorrow April 23 (2:15pm-4:45pm EDT in Pavilion 3 P3-#1614)📍 Come swing by our poster! I’d love to chat!

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Reposted by Nicholas Tolley

bioRxiv Neuroscience @biorxiv-neursci.bsky.social · 13/04/2026

Uncovering putative neural mechanisms of neurotherapeutic impacts on EEG using the Human Neocortical Neurosolver www.biorxiv.org/content/10.64898/20…

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Nicholas Tolley @ntolley.bsky.social · 17/04/2026

Special thanks to the team for helping put this together!✨✨ Stephanie Jones @jonescompneurolab.bsky.social , David Zhou @dvwz.bsky.social, Austin Soplata @asoplata.bsky.social, Katharina Duecker @katduecker.bsky.social, Carolina Fernandez Pujol, Joyce Gao, Dylan Daniels @dylansdaniels.bsky.social

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Nicholas Tolley @ntolley.bsky.social · 17/04/2026

This work represents a new research direction the team is taking. If you study EEG biomarkers of neurotherapeutics we'd love to start a conversation!

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Nicholas Tolley @ntolley.bsky.social · 17/04/2026

Insights gained from biophysical modeling with the @hnnsolver.bsky.social software can help at many decision points in the neurotherapeutic development pipeline, including building evidence of target engagement, and for target selection and dosing

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Nicholas Tolley @ntolley.bsky.social · 17/04/2026

In this new preprint from the @hnnsolver.bsky.social team, we lay out a protocol for how biophysical modeling can link treatment-induced changes in EEG to cell- and circuit-level mechanisms of action

The default HNN model is used as a starting point to test hypotheses by either manually altering the values of the chosen model parameters, or using automated optimization and inference algorithms. Differences in parameter values pre-to post-treatment correspond to model-based predictions.

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Nicholas Tolley @ntolley.bsky.social · 17/04/2026

Measuring pre- to post/treatment EEG biomarkers has the potential to help with this goal, but current methods fail to link these biomarkers to concrete mechanisms of actions that causally alter the EEG. This missing link hinders the utility of EEG in advancing effective neurotherapeutics

Biophysical modeling allows for testing mechanistic hypotheses that explain how EEG biomarkers emerge and change with drugs. Hypotheses about which drug mechanisms lead to distinct brain activity patterns must be constructed, and corresponding model parameters identified.

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Nicholas Tolley @ntolley.bsky.social · 17/04/2026

One of the major challenges in neurotherapeutic development is understanding how treatments are impacting individual brain networks

Modeling EEG biomarkers begins with picking a specific brain signal that is reliably different between patient populations. An example of a hypothetical EEG biomarker is an auditory event related potential (ERP) that is suppressed in post-treatment (red) relative to pre-treatment (blue).

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Nicholas Tolley @ntolley.bsky.social · 17/04/2026

Happy to share a new preprint from the @hnnsolver.bsky.social team!🧠💻🎉 "Uncovering putative neural mechanisms of neurotherapeutic impacts on EEG using the Human Neocortical Neurosolver" 📝 www.biorxiv.org/content/10.6...

Biophysical modeling to develop and test mechanistic hypotheses underlying pharmacological EEG biomarkers.

Top: Modeling EEG biomarkers begins with picking a specific brain signal that is reliably different between patient populations. An example of a hypothetical EEG biomarker is an auditory event related potential (ERP) that is suppressed in post-treatment (red) relative to pre-treatment (blue). Middle: Biophysical modeling allows for testing mechanistic hypotheses that explain how EEG biomarkers emerge and change with drugs. Hypotheses about which drug mechanisms lead to distinct brain activity patterns must be constructed, and corresponding model parameters identified. Bottom: The default HNN model is used as a starting point to test hypotheses by either manually altering the values of the chosen model parameters, or using automated optimization and inference algorithms. Differences in parameter values pre-to post-treatment correspond to model-based predictions.

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Reposted by Nicholas Tolley

Athena Akrami @athenaakrami.bsky.social · 13/04/2026

New preprint! 🧠 How do RNNs learn abstract rules from sequences, independent of specific stimuli? By Vezha Boboeva, with Alberto Pezzotta & George Dimitriadis "From sequences to schemas: low-rank recurrent dynamics underlie abstract relational representations" www.biorxiv.org/content/10.6...

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Reposted by Nicholas Tolley

CMC Unit @cmc-unit.bsky.social · 07/04/2026

Thrilled to see the second paper of the lab out 🤩 Check it out if you need a method to infer causality from neural data, even when the signal is short! Paper: joss.theoj.org/papers/10.21... Code: github.com/CMC-lab/Tran...

joss.theoj.org

TranCIT: Transient Causal Interaction Toolbox

Nouri et al., (2025). TranCIT: Transient Causal Interaction Toolbox. Journal of Open Source Software, 10(116), 9302, https://doi.org/10.21105/joss.09302

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Reposted by Nicholas Tolley

Rudebeck Lab @rudebecklab.bsky.social · 04/04/2026

Want a dataset to test ideas on neural basis of decision making or how areas interact as we make choices? Check out our data published today @rudebecklab.bsky.social. >16,000 single neurons from 22 anatomically confirmed areas in macaques performing a decision task. www.nature.com/articles/s41...

nature.com

Dataset of cortical and subcortical single neuron activity during value-based tasks in macaque monkey - Scientific Data

Scientific Data - Dataset of cortical and subcortical single neuron activity during value-based tasks in macaque monkey

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Reposted by Nicholas Tolley

James McAllister @jajmca.bsky.social · 02/04/2026

Really excited to share a new preprint! 𝗛𝗼𝘄 𝗱𝗼 𝗯𝗿𝗮𝗶𝗻𝘀 𝘀𝘁𝗮𝘆 𝗿𝗼𝗯𝘂𝘀𝘁 & 𝗲𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝘁 𝘄𝗵𝗶𝗹𝗲 𝗯𝗲𝗶𝗻𝗴 𝗶𝗻𝗰𝗿𝗲𝗱𝗶𝗯𝗹𝘆 𝘀𝗽𝗮𝗿𝘀𝗲? We explore how! www.biorxiv.org/content/10.6... We built Connectome-based Neural Networks (CoNNs) using Drosophila wiring (larva&adult) & compared with random networks with same sparsity.

Constructing connectome-based neural networks

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Konrad Kording @kordinglab.bsky.social · 31/03/2026

Preprint out arguing that we should build the techology to translate (compile) molecularly annotated connectomes into dynamics. I think this is incredibly important. arxiv.org/abs/2603.25713

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Todd Morrill @toddmorrill.bsky.social · 17/03/2026

This work started when Christian Pehle, @tonyzador.bsky.social, and I found that training sequentially (consuming/producing one spike at a time) in JAX was prohibitively slow. You certainly can consume multiple spikes in parallel—TLDR; just use an associative scan—but... 2/N

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John Tuthill @tuthill.bsky.social · 24/03/2026

🧵 New preprint led by @bingbrunton.bsky.social, @elliottabe.bsky.social, @lawrencehu.bsky.social We gave a worm brain control of a fly body and it walked What did we learn? Nothing, other than deep reinforcement learning is effective We call it the digital sphinx www.biorxiv.org/content/10.6...

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Nastya Krouglova @anastasiakrouglova.bsky.social · 22/03/2026

Our paper “Multifidelity Simulation-based Inference for Computationally Expensive Simulators” has been accepted at ICLR 2026! 🥳 We hope this can be a practical solution for anyone analysing and doing inference on computationally expensive simulators. Paper: openreview.net/pdf?id=bj0dc...

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Reposted by Nicholas Tolley

Joel I Berger @joelberger.bsky.social · 21/03/2026

We have a new preprint examining single neuron responses in humans undergoing TMS (www.biorxiv.org/content/10.6...). The excellent Charlie Dickey led the way, and I had the privilege to co-supervise this work with @coreykeller.bsky.social and @aaronboes.bsky.social 🧵 (1/7) 🧠🟦 🧠💻

Image showing the paradigm for recording single neurons in a variety of brain regions in humans, while single-pulse TMS was applied to dlPFC. One subpanel shows that single neurons were able to be resolved very early (8ms) after the single-pulse stimulation. Another subpanel shows two example neuron waveforms, along with their inter-spike interval distributions. The final subpanels show spike density functions overlaid on raster plots for the same neuron, separately for active and sham stimulation, in order to demonstrate that active (but not sham) stimulation increased activity in this example neuron.

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Reposted by Nicholas Tolley

bioRxiv Neuroscience @biorxiv-neursci.bsky.social · 21/03/2026

A Spatially Structured Spiking Network Model of Beta Traveling Waves and Their Attenuation in Motor Cortex www.biorxiv.org/content/10.64898/20…

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Nicholas Tolley @ntolley.bsky.social · 18/03/2026

In the future we hope to apply this framework to biophysical models of EEG generation like @hnnsolver.bsky.social providing a link between biomarkers and behaviorally relevant neural dynamics Special thanks to my amazing advisor Stephanie Jones @jonescompneurolab.bsky.social for all your support!

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Nicholas Tolley @ntolley.bsky.social · 18/03/2026

We also went deeper into what aspects of the NMDA receptor promote the emergence of attractor dynamics and found that the unique combination of slow receptor time constants and a magnesium block are both critical for task performance

Figure showing neural trajectories to modified versions of the NMDA receptor arriving at the dendrite where either 1) the receptor just exhibits slow time constants, or 2) the receptor just exhibits a magnesium block. Neither variant was successfully trained to solve the task, suggesting that the unique properties of NMDA receptors are necessary for the emergence of fixed point attractors.

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Nicholas Tolley @ntolley.bsky.social · 18/03/2026

In line with previous literature, slow time constant synapses (i.e. NMDA receptors) were shown to be extremely important for the emergence of attractor dynamics What’s surprising is that this result holds even when the network’s connectivity and active ion channels are freely changed

Figure of loss curves during training for different network variants where extrinsic inputs were fixed. Limiting extrinsic inputs to signal through NMDA receptors produced the best trained networks. AMPA inputs at the dendrite failed to solve the task.

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Nicholas Tolley @ntolley.bsky.social · 18/03/2026

We do this by selectively constraining certain biophysical properties, and allowing all others to be freely optimized

Schematic figure illustrating how different biophysical properties are evaluated for their importance to generate fixed point attractors, 1) fix extrinsic inputs (i.e. NMDA or AMPA inputs to the dendrite or soma), 2) train cell and network parameter (i.e. all other parameters of the model are freely modified), 3) evaluate task performance (the emergence of distinct fixed point attractors for each input is considered a successfully trained network)

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Nicholas Tolley @ntolley.bsky.social · 18/03/2026

Using the new modeling framework Jaxley, we attempt to characterize how fixed point attractors emerge in RNNs composed of biophysically detailed neurons

Visualization of the different scales of the biophysical model used in the paper. 1) Subcellular where voltage traces of a dendritic action potential are shown alongside the morphology of a multicompartment neuron, 2) cell-level where a spike raster shows how a single neuron exhibits preferential spiking to different inputs to the network, 3) network-level where a spike raster and neural trajectory plot both show that the network produces sustained changes in activity to inputs.

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Nicholas Tolley @ntolley.bsky.social · 18/03/2026

Currently there’s a lot of work understanding attractor dynamics in RNNs, as they are readily linked to cognitive tasks and behaviors Since RNNs typically contain simplified neurons, it is unclear how biophysical properties of realistic neurons impact the emergence of attractor dynamics

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Nicholas Tolley @ntolley.bsky.social · 18/03/2026

Happy to share a new preprint from my PhD thesis! “A novel framework for expanding RNNs with biophysical detail to solve cognitive tasks” 🧠💻 📝 www.biorxiv.org/content/10.6... @jonescompneurolab.bsky.social

Multipanel figure illustrating the key components of the paper: 1) biophysical reservoir computing where a network of biophysically detailed excitatory and inhibitory neurons are randomly connected, receive a brief input, and produce a sustained spiking pattern in response, 2) an illustration of the task the biophysical reservoir computer is trained on: a simplified working memory task where the network must produce distinct fixed point attractors in response to different inputs.

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bioRxiv Neuroscience @biorxiv-neursci.bsky.social · 17/03/2026

Deep-learning-assisted simulation of a cortical circuit: integrating anatomy, physiology and function www.biorxiv.org/content/10.64898/20…

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Reposted by Nicholas Tolley

Decision, Action, and Neural Computation (DANC) lab @danclab.bsky.social · 09/03/2026

We've been saying for a while now that beta bursts might be functionally heterogeneous. Check out @quentinmoreau.bsky.social's paper where we show that different types of bursts have different relationships to behavior in a sensorimotor adaptation task 👇👇👇

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DANDI Archive @dandiarchive.org · 27/02/2026

Mission: record hippocampal place cells in zero gravity Crew: 3 rats with electrode arrays Vehicle: Space Shuttle Columbia Status: data now publicly available on DANDI, 28 years later The ratstronauts' mission is finally complete. 🐀🚀 h/t NASA about.dandiarchive.org/blog/2026/02...

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Natalie Schaworonkow @nschawor.bsky.social · 20/02/2026

underappreciated concept 🙂 for more dipoles: neighboring dipoles have different orientations & can have different stimulus preferences. so even though topographies look similar, subtle differences remain. maybe that's why it is possible to decode information from relatively few sensors?

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Reposted by Nicholas Tolley

Alina Studenova @studenova.bsky.social · 20/02/2026

The angle of the dipole obviously matters. If active dipoles in two conditions are slightly misaligned, a difference can be observed. However, this difference is not driven by differences in amplitudes, but only by differences in orientations. Here, I simulated such a scenario. #brainmovies

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Tian Wang @tianw0318.bsky.social · 05/02/2026

Excited to share our new findings: distinct neural dynamics in prefrontal and premotor cortex during flexible decision making, preprinted on biorxiv. www.biorxiv.org/content/10.6...

biorxiv.org

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