Suliann Ben Hamed

Individual variability of neural computations underlying flexible decisions - Nature Behavioural experiments to study decision-making in response to context-dependent accumulation of evidence provide testable models that are consistent with the heterogeneity in neural signatures among...

Individual variability of neuronal computations underlying flexible décisions
www.nature.com/articles/s41...

Expectation-driven sensory adaptations support enhanced acuity during categorical perception - Nature Neuroscience Bayesian models explain how context biases perceptual behavior toward expected categories, but sensory neurons do not reflect this bias. Instead, expectation sharpens sensory acuity, independent of do...

Expectation-driven sensory adaptations support enhanced acuity during categorical perception
www.nature.com/articles/s41...

On the responsibilities of intellectuals and the rise of bullshit jobs in universities You may never have considered yourself to be one. Why would you? But if you’re reading this, there is more than a likelihood that you are one. If you’re a

On the fundamental responsibilities of intellectuals in the 21st centuray:
academic.oup.com/bra...
Unforetunately, the most recent international events seem to suggest a disconnection between modern societies and their intellectuals.

Latent circuit inference from heterogeneous neural responses during cognitive tasks Nature Neuroscience - The latent circuit model identifies low-dimensional mechanisms of task execution from heterogenous neural responses. This approach reveals a latent inhibitory mechanism for...

Latent circuit inference from heterogeneous neural responses during cognitive tasks
nature.com/articles/...
#neuroscience

In pyramidal neuron we trust 🧠💡
New research shows that dendritic #ANNs inspired by #brain connectivity—reduce overfitting, use fewer parameters, and outperform traditional ANNs in image classification!
Is this going towards #AI or towards cognitive #neuroscience? 🔥👇
www.nature.com/articles/s41...

"Together, [the] study establishes an anatomical foundation from which accounts of the broad role the DMN plays in human brain function and cognition can be developed."

The architecture of the human default mode network explored through cytoarchitecture, wiring and signal flow Nature Neuroscience - The default mode network (DMN) is implicated in cognition and behavior. Here, the authors show that the DMN is cytoarchitecturally heterogeneous, it contains regions receptive...

The architecture of the human default mode network explored through cytoarchitecture, wiring and signal flow: www.nature.com/artic...

💡 Stay tuned! I’ll soon be hiring motivated postdocs—reach out or stay on the watch if you’re interested!

🚀 Exciting news: FOCUS is moving forward with fully individualized neurofeedback protocols! I’m honored to announce that I’ve been awarded an #ERCPoC grant by @ERC_research and @european-research-council, hosted by @CNRSbiologie @CNRS_dr07 www.cnrs.fr/en/updat...

Universality of representation in biological and artificial neural networks Many artificial neural networks (ANNs) trained with ecologically plausible objectives on naturalistic data align with behavior and neural representations in biological systems. Here, we show that this...

Why do diverse ANNs resemble brain representations? Check out our new paper with Colton Casto, @nogazs.bsky.social , Colin Conwell, Mark Richardson, & @evfedorenko.bsky.social on “Universality of representation in biological and artificial neural networks.” 🧠🤖
tinyurl.com/yckndmjt

NeuroTorch: A Python library for neuroscience-oriented machine learning Machine learning (ML) has become a powerful tool for data analysis, leading to significant advances in neuroscience research. While ML algorithms are proficient in general-purpose tasks, their highly technical nature often hinders their compatibility with the observed biological principles and constraints in the brain, thereby limiting their suitability for neuroscience applications. In this work, we introduce NeuroTorch, a comprehensive ML pipeline specifically designed to assist neuroscientists in leveraging ML techniques using biologically inspired neural network models. NeuroTorch enables the training of recurrent neural networks equipped with either spiking or firing-rate dynamics, incorporating additional biological constraints such as Dale's law and synaptic excitatory-inhibitory balance. The pipeline offers various learning methods, including backpropagation through time and eligibility trace forward propagation, aiming to allow neuroscientists to effectively employ ML approaches. To evaluate the performance of NeuroTorch, we conducted experiments on well-established public datasets for classification tasks, namely MNIST, Fashion-MNIST, and Heidelberg. Notably, NeuroTorch achieved accuracies that replicated the results obtained using the Norse and SpyTorch packages. Additionally, we tested NeuroTorch on real neuronal activity data obtained through volumetric calcium imaging in larval zebrafish. On training sets representing 9.3 minutes of activity under darkflash stimuli from 522 neurons, the mean proportion of variance explained for the spiking and firing-rate neural network models, subject to Dale's law, exceeded 0.97 and 0.96, respectively. Our analysis of networks trained on these datasets indicates that both Dale's law and spiking dynamics have a beneficial impact on the resilience of network models when subjected to connection ablations. NeuroTorch provides an accessible and well-performing tool for neuroscientists, granting them access to state-of-the-art ML models used in the field without requiring in-depth expertise in computer science. ### Competing Interest Statement The authors have declared no competing interest.

Playing around with PyTorch for neuroscience made easy : NeuroTorch: A Python library for neuroscience-oriented machine learning biorxiv.org/cgi/cont... #biorxiv_neursci #neuroscience #neuroAI

NeuroTorch: A Python library for neuroscience-oriented machine learning https://www.biorxiv.org/content/10.1101/2024.12.29.630683v1

Neuronal travelling waves explain rotational dynamics in experimental datasets and modelling - Scientific Reports Scientific Reports - Neuronal travelling waves explain rotational dynamics in experimental datasets and modelling

Scientific Reports

Neuronal travelling waves explain rotational dynamics in experimental datasets and modelling

www.nature.com/articles/s41...

PNAS

Bayesian inference in ring attractor networks

www.pnas.org/doi/10.1073/...

New preprint by PhD students Genevieve Moat & Maxime Gaudet-Trafit, collaborator Jaume Bacardit under the efficient coordination of Colline Poirier #neuroscience #neuroAI: MacqD for automated detection of socially housed macaques