Biology subjects

Sejnowski, T. J.

Publications and source records attributed to Sejnowski, T. J..

35 records · Page 2Linked to original sources

bioRxiv2020

Differential covariance: A new method to estimate functional connectivity in fMRI

Measuring functional connectivity from fMRI recordings is important in understanding processing in cortical networks. However, because the brains connection pattern is complex, currently used methods are prone to producing false functional connections. We introduce differential covariance analysis, a new method that uses derivatives of the signal for estimating functional connectivity. We generated neural activities from Dynamical Causal Modeling and a neural network of Hodgkin-Huxley neurons and then converted them to hemodynamic signals using the forward Balloon model. The simulated fMRI signals together with the ground truth connectivity pattern were used to benchmark our method with other commonly used methods. Differential covariance achieved better results in complex network simulations. This new method opens an alternative way to estimate functional connectivity.

neuroscience↗

bioRxiv2020

Learning the synaptic and intrinsic membrane dynamics underlying working memory in spiking neural network models

Recurrent neural network (RNN) model trained to perform cognitive tasks is a useful computational tool for understanding how cortical circuits execute complex computations. However, these models are often composed of units that interact with one another using continuous signals and overlook parameters intrinsic to spiking neurons. Here, we developed a method to directly train not only synaptic-related variables but also membrane-related parameters of a spiking RNN model. Training our model on a wide range of cognitive tasks resulted in diverse yet task-specific synaptic and membrane parameters. We also show that fast membrane time constants and slow synaptic decay dynamics naturally emerge from our model when it is trained on tasks associated with working memory (WM). Further dissecting the optimized parameters revealed that fast membrane properties and slow synaptic dynamics are important for encoding stimuli and WM maintenance, respectively. This approach offers a unique window into how connectivity patterns and intrinsic neuronal properties contribute to complex dynamics in neural populations.

neuroscience↗

bioRxiv2020

A modeling framework for adaptive lifelong learning with transfer and savings through gating in the prefrontal cortex

The prefrontal cortex encodes and stores numerous, often disparate, schemas and flexibly switches between them. Recent research on artificial neural networks trained by reinforcement learning has made it possible to model fundamental processes underlying schema encoding and storage. Yet how the brain is able to create new schemas while preserving and utilizing old schemas remains unclear. Here we propose a simple neural network framework based on a modification of the mixture of experts architecture to model the prefrontal cortexs ability to flexibly encode and use multiple disparate schemas. We show how incorporation of gating naturally leads to transfer learning and robust memory savings. We then show how phenotypic impairments observed in patients with prefrontal damage are mimicked by lesions of our network. Our architecture, which we call DynaMoE, provides a fundamental framework for how the prefrontal cortex may handle the abundance of schemas necessary to navigate the real world.

neuroscience↗

bioRxiv2020

Strong inhibitory signaling underlies stable temporal dynamics and working memory in spiking neural networks

Cortical neurons process information on multiple timescales, and areas important for working memory (WM) contain neurons capable of integrating information over a long timescale. However, the underlying mechanisms for the emergence of neuronal timescales stable enough to support WM are unclear. By analyzing a spiking recurrent neural network (RNN) model trained on a WM task and activity of single neurons in the primate prefrontal cortex, we show that the temporal properties of our model and the neural data are remarkably similar. Dissecting our RNN model revealed strong inhibitory-to-inhibitory connections underlying a disinhibitory microcircuit as a critical component for long neuronal timescales and WM maintenance. We also found that enhancing inhibitory-to-inhibitory connections led to more stable temporal dynamics and improved task performance. Finally, we show that a network with such microcircuitry can perform other tasks without disrupting its pre-existing timescale architecture, suggesting that strong inhibitory signaling underlies a flexible WM network.

neuroscience↗

bioRxiv2019

Polycomb-mediated repression compensates for loss of postnatal DNA methylation in excitatory neurons

Two epigenetic pathways of repression, DNA methylation and Polycomb repressive complex 2 (PRC2) mediated gene silencing, regulate neuron development and function, but their respective contributions are unknown. We found that conditional loss of the de novo DNA methyltransferase Dnmt3a in mouse excitatory neurons altered expression of synapse-related genes, stunted synapse maturation, and impaired working memory and social interest. Loss of Dnmt3a abolished postnatal accumulation of CG and non-CG DNA methylation, leaving neurons with an unmethylated, fetal-like epigenomic pattern at -140,000 genomic regions. The PRC2-associated histone modification H3K27me3 increased at many of these sites, partially compensating for the loss of DNA methylation. Our data support a dynamic interaction between two fundamental modes of epigenetic repression during postnatal maturation of excitatory neurons, which together confer robustness on neuronal regulation.

neuroscience↗

bioRxiv2019

Spiking Recurrent Networks as a Model to Probe Neuronal Timescales Specific to Working Memory

Cortical neurons process and integrate information on multiple timescales. In addition, these timescales or temporal receptive fields display functional and hierarchical organization. For instance, areas important for working memory (WM), such as prefrontal cortex, utilize neurons with stable temporal receptive fields and long timescales to support reliable representations of stimuli. Despite of the recent advances in experimental techniques, the underlying mechanisms for the emergence of neuronal timescales long enough to support WM are unclear and challenging to investigate experimentally. Here, we demonstrate that spiking recurrent neural networks (RNNs) designed to perform a WM task reproduce previously observed experimental findings and that these models could be utilized in the future to study how neuronal timescales specific to WM emerge.

neuroscience↗

bioRxiv2019

Interactions between calmodulin and neurogranin govern the dynamics of CaMKII as a leaky integrator

Calmodulin-dependent kinase II (CaMKII) has long been known to play an important role in learning and memory as well as long term potentiation (LTP). More recently it has been suggested that it might be involved in the time averaging of synaptic signals, which can then lead to the high precision of information stored at a single synapse. However, the role of the scaffolding molecule, neurogranin (Ng), in governing the dynamics of CaMKII is not yet fully understood. In this work, we adopt a rule-based modeling approach through the Monte Carlo method to study the effect of Ca2+ signals on the dynamics of CaMKII phosphorylation in the postsynaptic density (PSD). Calcium surges are observed in synaptic spines during an EPSP and back-propagating action potential due to the opening of NMDA receptors and voltage dependent calcium channels. We study the differences between the dynamics of phosphorylation of CaMKII monomers and dodecameric holoenzymes. The scaffolding molecule Ng, when present in significant concentration, limits the availability of free calmodulin (CaM), the protein which activates CaMKII in the presence of calcium. We show that it plays an important modulatory role in CaMKII phosphorylation following a surge of high calcium concentration. We find a non-intuitive dependence of this effect on CaM concentration that results from the different affinities of CaM for CaMKII depending on the number of calcium ions bound to the former. It has been shown previously that in the absence of phosphatase CaMKII monomers integrate over Ca2+ signals of certain frequencies through autophosphorylation (Pepke et al, Plos Comp. Bio., 2010). We also study the effect of multiple calcium spikes on CaMKII holoenzyme autophosphorylation, and show that in the presence of phosphatase CaMKII behaves as a leaky integrator of calcium signals, a result that has been recently observed in vivo. Our models predict that the parameters of this leaky integrator are finely tuned through the interactions of Ng, CaM, CaMKII, and PP1. This is a possible mechanism to precisely control the sensitivity of synapses to calcium signals.

neuroscience↗

bioRxiv2019

Predicting the fMRI signal fluctuation with echo-state neural networks trained on vascular network dynamics

Resting-state functional MRI (rs-fMRI) studies have revealed specific low-frequency hemodynamic signal fluctuations (<0.1 Hz) in the brain, which could be related to oscillations in neural activity through several mechanisms. Although the vascular origin of the fMRI signal is well established, the neural correlates of global rs-fMRI signal fluctuations are difficult to separate from other confounding sources. Recently, we reported that single-vessel fMRI slow oscillations are directly coupled to brain state changes. Here, we used an echo-state network (ESN) to predict the future temporal evolution of the rs-fMRI slow oscillatory feature from both rodent and human brains. rs-fMRI signals from individual blood vessels that were strongly correlated with neural calcium oscillations were used to train an ESN to predict brain state-specific rs-fMRI signal fluctuations. The ESN-based prediction model was also applied to recordings from the Human Connectome Project (HCP), which classified variance-independent brain states based on global fluctuations of rs-fMRI features. The ESN revealed brain states with global synchrony and decoupled internal correlations within the default-mode network.

neuroscience↗

bioRxiv2019

Specific nanoscale synaptic reshuffling and control of short-term plasticity following NMDAR- and P2XR-dependent Long-Term Depression

Long-Term Potentiation (LTP) and Long-Term Depression (LTD) of excitatory synaptic transmission are considered as cellular basis of learning and memory. These two forms of synaptic plasticity have been mainly attributed to global changes in the number of synaptic AMPA-type glutamate receptor (AMPAR) through a regulation of the diffusion/trapping balance at the PSD, exocytosis and endocytosis. While the precise molecular mechanisms at the base of LTP have been intensively investigated, the ones involved in LTD remains elusive. Here we combined super-resolution imaging technique, electrophysiology and modeling to describe the various modifications of AMPAR nanoscale organization and their effect on synaptic transmission in response to two different LTD protocols, based on the activation of either NMDA receptors or P2X receptors. While both type of LTD are associated with a decrease in synaptic AMPAR clustering, only NMDAR-dependent LTD is associated with a reorganization of PSD-95 at the nanoscale. This change increases the pool of diffusive AMPAR improving synaptic short-term facilitation through a post-synaptic mechanism. These results demonstrate that specific dynamic reorganization of synapses at the nanoscale during specific LTD paradigm allows to improve the responsiveness of depressed synapses.

neuroscience↗

bioRxiv2019

Mitochondrial morphology provides a mechanism for energy buffering at synapses

Mitochondria as the main energy suppliers of eukaryotic cells are highly dynamic organelles that fuse, divide and are transported along the cytoskeleton to ensure cellular energy homeostasis. While these processes are well established, substantial evidence indicates that the internal structure is also highly variable in dependence on metabolic conditions. However, a quantitative mechanistic understanding of how mitochondrial morphology affects energetic states is still elusive. To address this question, we here present an agent-based dynamic model using three-dimensional morphologies from electron microscopy tomography which considers the molecular dynamics of the main ATP production components. We apply our modeling approach to mitochondria at the synapse which is the largest energy consumer within the brain. Interestingly, comparing the spatiotemporal simulations with a corresponding space-independent approach, we find minor space dependence when the system relaxes toward equilibrium but a qualitative difference in fluctuating environments. These results suggest that internal mitochondrial morphology is not only optimized for ATP production but also provides a mechanism for energy buffering and may represent a mechanism for cellular robustness.

biophysics↗

bioRxiv2019

Simple Framework for Constructing Functional Spiking Recurrent Neural Networks

Cortical microcircuits exhibit complex recurrent architectures that possess dynamically rich properties. The neurons that make up these microcircuits communicate mainly via discrete spikes, and it is not clear how spikes give rise to dynamics that can be used to perform computationally challenging tasks. In contrast, continuous models of rate-coding neurons can be trained to perform complex tasks. Here, we present a simple framework to construct biologically realistic spiking recurrent neural networks (RNNs) capable of learning a wide range of tasks. Our framework involves training a continuous-variable rate RNN with important biophysical constraints and transferring the learned dynamics and constraints to a spiking RNN in a one-to-one manner. The proposed framework introduces only one additional parameter to establish the equivalence between rate and spiking RNN models. We also study other model parameters related to the rate and spiking networks to optimize the one-to-one mapping. By establishing a close relationship between rate and spiking models, we demonstrate that spiking RNNs could be constructed to achieve similar performance as their counterpart continuous rate networks.

neuroscience↗

bioRxiv2019

A multi-state model of the CaMKII dodecamer suggests a role for calmodulin in maintenance of autophosphorylation

Ca2+/calmodulin-dependent protein kinase II (CaMKII) accounts for up to 2 percent of all brain protein and is essential to memory function. CaMKII activity is known to regulate dynamic shifts in the size and signaling strength of neuronal connections, a process known as synaptic plasticity. Increasingly, computational models are used to explore synaptic plasticity and the mechanisms regulating CaMKII activity. Conventional modeling approaches may exclude biophysical detail due to the impractical number of state combinations that arise when explicitly monitoring the conformational changes, ligand binding, and phosphorylation events that occur on each of the CaMKII holoenzymes twelve subunits. To manage the combinatorial explosion without necessitating bias or loss in biological accuracy, we use a specialized syntax in the software MCell to create a rule-based model of the twelve-subunit CaMKII holoenzyme. Here we validate the rule-based model against previous measures of CaMKII activity and investigate molecular mechanisms of CaMKII regulation. Specifically, we explore how Ca2+/CaM-binding may both stabilize CaMKII subunit activation and regulate maintenance of CaMKII autophosphorylation. Noting that Ca2+/CaM and protein phosphatases bind CaMKII at nearby or overlapping sites, we compare model scenarios in which Ca2+/CaM and protein phosphatase do or do not structurally exclude each others binding to CaMKII. Our results suggest a functional mechanism for the so-called \"CaM trapping\" phenomenon, such that Ca2+/CaM structurally excludes phosphatase binding and thereby prolongs CaMKII autophosphorylation. We conclude that structural protection of autophosphorylated CaMKII by Ca2+/CaM may be an important mechanism for regulation of synaptic plasticity.\n\nAuthor summaryIn the hippocampus, the dynamic fluctuation in size and strength of neuronal connections is thought to underlie learning and memory processes. These fluctuations, called synaptic plasticity, are in-part regulated by the protein calcium/calmodulin-dependent kinase II (CaMKII). During synaptic plasticity, CaMKII becomes activated in the presence of calcium ions (Ca2+) and calmodulin (CaM), allowing it to interact enzymatically with downstream binding partners. Interestingly, activated CaMKII can phosphorylate itself, resulting in state changes that allow CaMKII to be functionally active independent of Ca2+/CaM. Phosphorylation of CaMKII at Thr-286/287 has been shown to be a critical component of learning and memory. To explore the molecular mechanisms that regulate the activity of CaMKII holoenzymes, we use a rule-based approach that reduces computational complexity normally associated with representing the wide variety of functional states that a CaMKII holoenzyme can adopt. Using this approach we observe regulatory mechanisms that might be obscured by reductive approaches. Our results newly suggest that CaMKII phosphorylation at Thr-286/287 is stabilized by a mechanism in which CaM structurally excludes phosphatase binding at that site.

neuroscience↗

bioRxiv2018

A conserved principle of glial organization in the paleocortex and neocortex

The organizational principles of glia remain largely unknown despite their vital role in nervous system function. Previous work has shown that the number of glia per unit volume of neocortex is constant across mammalian species. We hypothesize that the conservation of glia volume density within brain regions might be a governing principle of organization across species. To test this hypothesis, we used stereology, light microscopy, and data available in the literature to examine five brain regions: the cerebral cortex and four brain regions that differ from the cerebral cortex and each other - the anterior piriform cortex, the posterior piriform cortex, the entorhinal cortex, and the cerebellum. We discovered two orderly relationships: First, glia volume density within a brain region was constant across species, including humans, although it significantly differed between regions, suggesting that glia density might constitute a region-specific marker. Second, the ratio of glia to neuron increased with brain volume according to a [1/4] power law in the primate frontal cortex and the neocortex, the mammalian paleocortex, and the cerebellum. These relationships show that the development of glia and neurons are coupled, and suggest that what a neural circuit computes depends as much on its glial components as on its neurons. Main PointsO_LIThe volume density of glia (i.e., number of glia per unit volume) within a brain region is con-served across mammalian species including humans. C_LIO_LIThe ratio of glia to neuron increases with bigger brains. C_LIO_LIThe volume density of glia is significantly different across functionally and architecturally dif-ferent brain regions and could function as a region-specific marker. C_LIO_LIGlia obey scaling constraints that are different from scaling constraints for neurons. C_LI

neuroscience↗

bioRxiv2018

Geometric principles of second messenger dynamics in dendritic spines.

Dendritic spines are small, bulbous protrusions along dendrites in neurons and play a critical role in synaptic transmission. Dendritic spines come in a variety of shapes that depend on their developmental state. Additionally, roughly 14-19% of mature spines have a specialized endoplasmic reticulum called the spine apparatus. How does the shape of a postsynaptic spine and its internal organization affect the spatio-temporal dynamics of short timescale signaling? Answers to this question are central to our understanding the initiation of synaptic transmission, learning, and memory formation. In this work, we investigated the effect of spine and spine apparatus size and shape on the spatio-temporal dynamics of second messengers using mathematical modeling using reaction-diffusion equations in idealized geometries (ellipsoids, spheres, and mushroom-shaped). Our analyses and simulations showed that in the short timescale, spine size and shape coupled with the spine apparatus geometries govern the spatiotemporal dynamics of second messengers. We show that the curvature of the geometries gives rise to pseudo-harmonic functions, which predict the locations of maximum and minimum concentrations along the spine head. Furthermore, we showed that the lifetime of the concentration gradient can be fine-tuned by localization of fluxes on the spine head and varying the relative curvatures and distances between the spine apparatus and the spine head. Thus, we have identified several key geometric determinants of how the spine head and spine apparatus may regulate the short timescale chemical dynamics of small molecules that control synaptic plasticity.

biophysics↗

bioRxiv2017

Thalamocortical and Intracortical Laminar Connectivity Determines Sleep Spindle Properties

Spindle oscillations are brief oscillatory activity during non-rapid eye movement (NREM) sleep. Spindle density and synchronization properties are different in MEG versus EEG recordings in humans and also vary with learning performance, suggesting spindle involvement in memory consolidation. Using computational models, we identified network mechanisms that may explain differences in spindle properties across cortical structures. First, we report that differences in spindle occurrence between MEG and EEG data may arise from the properties of the core vs. matrix thalamocortical systems. The matrix system, projecting superficially, has wider thalamocortical fanout compared to the core system, projecting to the middle layers, and requires the recruitment of a larger population of neurons to initiate a spindle. Our model demonstrates that this property is sufficient to explain lower spindle density and higher spatial synchrony of spindles in the superficial cortical layers, as observed in the EEG signal. In contrast, spindles in the core system occurred more frequently but less synchronously, as observed in the MEG recordings. Futhermore, consistent with human recordings, in the model, spindles occurred independently in the core system but matrix system spindles commonly co-occurred with core one. We found that the intracortical excitatory connections from layer III/IV to layer V promote spindle propagation from the core to the matrix system, leading to widespread spindle activity. Our study predicts that plasticity of the intra and inter cortical connectivity can potentially be a mechanism for increasing in spindle density as observed during learning.\n\nAuthor summaryThe density of sleep spindles has been shown to correlate with memory consolidation. Further, sleep spindles occur more often in human MEG than EEG. We developed thalamocortical network model that is capable of spontaneous generation of spindles across cortical layers and that captures the essential statistical features of spindles observed in experiments. We predict that differences in thalamo-cortical connectivity, known from anatomical studies, lead to the differences in the spindle properties between EEG and MEG as observed in human recordings. Further, we predict that the intracortical connectivity between cortical layers, a property influenced by sleep preceding learning, increases spindle density. Results from our study highlight the role of cortical and thalamic projections on the occurrence and properties of spindles.

neuroscience↗

bioRxiv2017

Pre-post synaptic alignment through neuroligin tunes synaptic transmission efficiency

The nanoscale organization of neurotransmitter receptors relative to pre-synaptic release sites is a fundamental determinant of both the amplitude and reliability of synaptic transmission. How modifications in the alignment between pre- and post-synaptic machineries affect synaptic current properties has only been addressed with computer modeling, and therefore remains hypothetical. Using dual-color single molecule based super-resolution microscopy, we found a strong spatial correlation between AMPA receptor (AMPAR) nanodomains and the post-synaptic adhesion protein neuroligin-1 (NLG1). Expression of a C-terminal truncated form of NLG1 disrupted this correlation without affecting the intrinsic organization of AMPAR nanodomains. Moreover, this NLG1 dominant-negative mutant significantly shifted the pre-synaptic release machinery from AMPAR synaptic clusters. Electrophysiology and computer modeling show that this physical shift is sufficient to induce a significant decrease in synaptic transmission. Thus, our results suggest the necessity for synapses to release glutamate in front of AMPAR nanodomains, to maintain a high efficiency of synaptic responses.

neuroscience↗

bioRxiv2016

Multidimensional imbalances in cortical circuit activity in Fragile-X Syndrome mice

A leading theory for multiple brain disorders, such as schizophrenia and autism, is that they arise from developmental imbalances in excitatory and inhibitory (E/I) brain circuitry. However, it is unclear whether this simple 1-dimensional model is sufficiently rich to capture the set of key alterations of neural circuit processing in brain disorders. Here we used a combination of computational modeling and large-scale analysis of in vivo 2-photon Ca2+ imaging data from somatosensory cortex of wild-type and Fmr1 knock-out (KO) mice, a model of Fragile X syndrome, to test the E/I imbalance model. Our main findings were: 1) the E/I imbalance model was not rich enough to capture the alterations in neural activity statistics in Fmr1 KO mice; 2) in a computational model we found greatly varying magnitudes and directions of effects of synaptic and cellular properties on network activity; 3) evidence for opposite changes in circuit properties of Fmr1 KO mice at different stages of development; 4) a reduction in the entropy of circuit activity in young Fmr1 KO mice compared to wild-type, but an opposite increase in adult Fmr1 KO mice. These findings suggest qualitatively new strategies for developing treatments for Fragile-X Syndrome and related disorders.

neuroscience↗