Biology subjects

Tolley, N.

Publications and source records attributed to Tolley, N..

5 recordsLinked to original sources

bioRxiv2026

Next generation Human Neocortical Neurosolver model with biophysically realistic somatic, dendritic and synaptic dynamics

The Human Neocortical Neurosolver (HNN) is an open-source biophysical modeling tool for multiscale interpretation of human electroencephalography (EEG) and magnetoencephalography (MEG) signals. HNN's foundation is a pre-tuned canonical neocortical circuit model consisting of excitatory pyramidal neurons and inhibitory interneurons in supra- (L2/3) and infragranular (L5) layers. This model was originally developed based on studies in rodent and non-human primate cortices when dynamics of human neurons were poorly characterized. Yet recent advances in recordings from human tissue now provide unprecedented detail to update the model. Here, we present a significantly updated model for use in the HNN software, with electrophysiological properties and synaptic connections tuned to human tissue. Beyond reproducing biologically realistic somatic dynamics, the pyramidal cells simulate dendritic calcium (Ca2+) spikes in the L5 extratelencephalic-projecting (ET) neurons, and shorter dendritic spikes in the L2/3 pyramidal neurons. We demonstrate how the updated circuitry and new features in the HNN software can be used to disentangle somatic, synaptic, and dendritic contributions to human E-/MEG signatures. The model is distributed with workflows to simulate event-related responses and oscillations, and tutorials demonstrating how membrane potentials, Ca2+ dynamics, and synaptic currents can be recorded and visualized. These enhancements allow extensive model validations based on in vitro and in vivo electrophysiology, voltage and Ca2+ imaging, and provide users with novel insights on the mechanisms underlying E-/MEG signals.

neuroscience↗

bioRxiv2026

Uncovering putative neural mechanisms of neurotherapeutic impacts on EEG using the Human Neocortical Neurosolver

SHORT ABSTRACTA key barrier to developing effective drugs for disorders of the central nervous system (CNS) is understanding their impact on neural circuits. This protocol demonstrates how physics-based neural simulations can be used to interpret electrophysiological biomarkers of neurotherapeutics, providing a mechanistically grounded approach to the development of neurotherapeutics. LONG ABSTRACTElectroencephalography (EEG) and electrophysiology methods provide millisecond resolution biomarkers for central nervous system disorders and are used to assess treatment-related effects. However, lack of understanding about the neural mechanisms generating such biomarkers impedes the development of diagnostics and therapeutics based on these signals. The Human Neocortical Neurosolver (HNN) is an open-source biophysical modeling software that connects localized EEG biomarkers to their multi-scale neural generators. This protocol demonstrates a hypothesis-driven workflow using HNN to test possible neural mechanisms of neurotherapy-induced EEG biomarkers by optimizing parameters to achieve a fit between simulated and empirical current source waveforms. Corresponding multi-scale cell- and circuit-level activity can then be visualized and quantified, providing validation targets for model predictions in follow up empirical studies. An example is provided which shows how to examine the generating mechanisms of the early event-related potential (ERP) components of an auditory evoked response (P1, N1 and P2) and to assess changes following neural circuit modification due to neurotherapeutic administration. This protocol demonstration enables scientists to design simulation experiments to develop testable predictions on how EEG biomarkers reflect neural circuit mechanisms of example therapeutics. A similar protocol can be applied to study disease mechanisms or other therapies.

neuroscience↗

bioRxiv2026

A novel framework for expanding RNNs with biophysical detail to solve cognitive tasks

Recurrent neural networks (RNNs) have proven to be highly successful in emulating human-like cognitive functions such as working memory. In recent years, RNNs are evolving to incorporate more biophysical realism to produce more plausible predictions on how cognitive tasks are solved in real neural circuits. However, there are major challenges in constructing and training networks with the complex and nonlinear properties of real neurons. A major component of the success of RNNs is that they share the same mathematical base as deep neural networks, permitting highly efficient optimization of model parameters using standard deep learning techniques. To do so, they use abstract representations of neurons which fail to capture the impact of cell-level biophysical and morphologic properties that may benefit network-level function. Expanding task-trained RNNs with biophysical properties such as dendrites and active ionic currents poses substantial challenges, as it moves these models away from the validated training regimes known to be highly effective for RNNs. To address this gap, we developed a biophysically detailed reservoir computing (BRC) framework with the goal of extracting mechanistic insights from biophysical neural models, and propose that these insights can be used to guide model choices that will work for specific categories of cognitive tasks. The BRC network was constructed with synaptically coupled excitatory and inhibitory cells, in which the excitatory cells include multicompartment biophysically active dendrites; motivated by empirical studies suggesting dendrites have desirable computational benefits (e.g. pattern classification and coincidence detection). We trained the BRC network to do a simplified working memory task where it had to maintain the representation of an extrinsic "cue" input. We studied the impact of extrinsic input time constants (fast AMPA vs slow NMDA) and location (dendrite vs soma) on the ability of a network to solve the task. Our results revealed that cue inputs through NMDA receptors are particularly efficient for solving the working memory task. Further, the properties of NMDA receptors are uniquely suited for cue inputs delivered at the dendrite, as networks trained with dendritic AMPA cue inputs failed to solve the task. Detailed examination of the cell and network dynamics that solve the task reveals distinct local network configurations and computing principles for the different types of extrinsic input. Overall, much like the body of mechanistic insights that have underpinned the success of training RNNs, this study lays the groundwork for applying the BRC framework to train biophysically detailed neural models to solve complex human-like cognitive tasks.

neuroscience↗

bioRxiv2024

Heterozygous Eif4nif1 Stop Gain Mice Replicate the Primary Ovarian Insufficiency Phenotype in Women

We created the c.1286C>G stop-gain mutation found in a family with primary ovarian insufficiency (POI) at age 30 years. The Eif4enif1 C57/Bl6 transgenic mouse model contained a floxed exon 10-19 cassette with a conditional knock-in cassette containing the c.1286C>G stop-gain mutation in exon 10. The hybrid offspring of CMV-Cre mice with Eif4enif1WT/flx mice were designated Eif4enif1WT/{Delta} for simplicity. A subset of female heterozygotes (Eif4enif1WT/{Delta}) had no litters. In those with litters, the final litter was earlier (5.4{+/-}2.6 vs. 10.5{+/-}0.7 months; p=0.02). Heterozygous breeding pair (Eif4enif1WT/{Delta} x Eif4enif1WT/{Delta}) litter size was 60% of WT litter size (3.9{+/-}2.0 vs. 6.5{+/-}3.0 pups/litter; p<0.001). The genotypes were 35% Eif4enif1WT/flx and 65% Eif4enif1WT/{Delta}, with no homozygotes. Homozygote embryos did not develop beyond the 4-8 cell stage. The number of follicles in ovaries from Eif4enif1WT/{Delta} mice was lower starting at the primordial (499{+/-}290 vs. 1445{+/-}381) and primary follicle stage (1069{+/-}346 vs. 1450{+/-}193) on day 10 (p<0.05). The preantral follicle number was lower starting on day 21 (213{+/-}86 vs. 522{+/-}227; p<0.01). Examination of ribosome protected mRNAs (RPR) demonstrated altered mRNA expression. The Eif4enif1 stop-gain mice replicate the POI phenotype in women. The unique mouse model provides a platform to study regulation of protein translation across oocyte and embryo development in mammals.

developmental biology↗

bioRxiv2023

Methods and considerations for estimating parameters in biophysically detailed neural models with simulation based inference

Biophysically detailed neural models are a powerful technique to study neural dynamics in health and disease with a growing number of established and openly available models. A major challenge in the use of such models is that parameter inference is an inherently difficult and unsolved problem. Identifying unique parameter distributions that can account for observed neural dynamics, and differences across experimental conditions, is essential to their meaningful use. Recently, simulation based inference (SBI) has been proposed as an approach to perform Bayesian inference to estimate parameters in detailed neural models. SBI overcomes the challenge of not having access to a likelihood function, which has severely limited inference methods in such models, by leveraging advances in deep learning to perform density estimation. While the substantial methodological advancements offered by SBI are promising, their use in large scale biophysically detailed models is challenging and methods for doing so have not been established, particularly when inferring parameters that can account for time series waveforms. We provide guidelines and considerations on how SBI can be applied to estimate time series waveforms in biophysically detailed neural models starting with a simplified example and extending to specific applications to common MEG/EEG waveforms using the the large scale neural modeling framework of the Human Neocortical Neurosolver. Specifically, we describe how to estimate and compare results from example oscillatory and event related potential simulations. We also describe how diagnostics can be used to assess the quality and uniqueness of the posterior estimates. The methods described provide a principled foundation to guide future applications of SBI in a wide variety of applications that use detailed models to study neural dynamics. Author summaryA central problem in computational neural modeling is estimating model parameters that can account for observed activity patterns. While several techniques exist to perform parameter inference in special classes of abstract neural models, there are comparatively few approaches for large scale biophysically detailed neural models. In this work, we describe challenges and solutions in applying a deep learning based statistical framework to estimate parameters in a biophysically detailed large scale neural model, and emphasize the particular difficulties in estimating parameters for time series data. Our example uses a multi-scale model designed to connect human MEG/EEG recordings to the underlying cell and circuit level generators. Our approach allows for crucial insight into how cell-level properties interact to produce measured neural activity, and provides guidelines for diagnosing the quality of the estimate and uniqueness of predictions for different MEG/EEG biomarkers.

neuroscience↗