Biology subjects

Strock, A.

Publications and source records attributed to Strock, A..

4 recordsLinked to original sources

bioRxiv2025

Recurrent neural network models reveal unified mechanisms generating event-related potentials from MMN to P300

The brain must continuously extract salient cues from an immense sensory stream and represent them across regions. Multiregional responses to salient stimuli, notably mismatch negativity (MMN) in sensory and P300 in frontal areas, are among the best-characterized signals in human electrophysiology, yet the mechanisms relating them are unclear, and whether one mechanism accounts for both is unknown. We develop a hierarchical, multiregional recurrent neural network of sensory and frontal cortex to test whether short-term synaptic depression (STD) can explain both. In sensory regions, STD produces stimulus-specific adaptation, generating MMN-like responses; propagation to frontal regions produces the amplification and delay characteristic of the P300, while enhancing noise robustness, with rare stimuli occupying expanded regions of neural state space. When STD acts on frontal feedback predicting upcoming stimuli, it further amplifies responses to unexpected stimuli, suggesting attention sharpens predictions. Our results bridge synaptic-scale mechanisms and brain-wide representations, with relevance to precision psychiatry.

neuroscience↗

bioRxiv2025

Explainable AI techniques for dynamic functional brain imaging: validation and analysis of E/I imbalance in autism

Deep neural networks have revolutionized functional neuroimaging analysis but remain "black boxes," concealing which brain mechanisms and regions drive their predictions--a critical limitation for clinical neuroscience. Here we develop and validate an explainable AI (xAI) framework to test whether feature attribution techniques can reliably recover brain regions affected by excitation/inhibition (E/I) imbalance--a fundamental dysregulation implicated in autism, schizophrenia, and other neuropsychiatric disorders. We employed complementary simulation approaches: recurrent neural networks for controlled parameter exploration, and The Virtual Brain simulator incorporating empirically-derived human and mouse connectomes to model E/I balance alterations with unprecedented biological realism. Through systematic validation, we demonstrate that Integrated Gradients and DeepLIFT methods reliably identify brain regions affected by E/I imbalance across challenging conditions, including high noise, low prevalence, and subtle neurophysiological alterations. This performance remains robust across species and anatomical scales, from 68-region human to 426-region mouse connectomes. Application to the ABIDE autism dataset (N=834) reveal convergence between our biophysically grounded simulations and empirical findings, providing computational support for E/I imbalance mechanisms in autism. This work establishes essential tools and data for interpretation of deep learning models in functional neuroimaging, and enables hypothesis-driven analysis of cellular mechanisms across neuropsychiatric disorders.

neuroscience↗

bioRxiv2024

Digital twins for understanding mechanisms of learning disabilities: Personalized deep neural networks reveal impact of neuronal hyperexcitability

Learning disabilities affect a significant proportion of children worldwide, with far-reaching consequences for their academic, professional, and personal lives. Here we develop digital twins - biologically plausible personalized Deep Neural Networks (pDNNs) - to investigate the neurophysiological mechanisms underlying learning disabilities in children. Our pDNN reproduces behavioral and neural activity patterns observed in affected children, including lower performance accuracy, slower learning rates, neural hyper-excitability, and reduced neural differentiation of numerical problems. Crucially, pDNN models reveal aberrancies in the geometry of manifold structure, providing a comprehensive view of how neural excitability influences both learning performance and the internal structure of neural representations. Our findings not only advance knowledge of the neurophysiological underpinnings of learning differences but also open avenues for targeted, personalized strategies designed to bridge cognitive gaps in affected children. This work reveals the power of digital twins integrating AI and neuroscience to uncover mechanisms underlying neurodevelopmental disorders.

neuroscience↗

bioRxiv2019

A Robust Model of Gated Working Memory

Gated working memory is defined as the capacity of holding arbitrary information at any time in order to be used at a later time. Based on electrophysiological recordings, several computational models have tackled the problem using dedicated and explicit mechanisms. We propose instead to consider an implicit mechanism based on a random recurrent neural network. We introduce a robust yet simple reservoir model of gated working memory with instantaneous updates. The model is able to store an arbitrary real value at random time over an extended period of time. The dynamics of the model is a line attractor that learns to exploit reentry and a non-linearity during the training phase using only a few representative values. A deeper study of the model shows that there is actually a large range of hyper parameters for which the results hold (number of neurons, sparsity, global weight scaling, etc.) such that any large enough population, mixing excitatory and inhibitory neurons can quickly learn to realize such gated working memory. In a nutshell, with a minimal set of hypotheses, we show that we can have a robust model of working memory. This suggests this property could be an implicit property of any random population, that can be acquired through learning. Furthermore, considering working memory to be a physically open but functionally closed system, we give account on some counter-intuitive electrophysiological recordings.

neuroscience↗