Biology subjects

Papadimitriou, C.

Publications and source records attributed to Papadimitriou, C..

6 recordsLinked to original sources

bioRxiv2025

Simulated Language Acquisition in a Biologically Realistic Model of the Brain

Despite tremendous progress in neuroscience, we do not have a compelling narrative for the precise way whereby the spiking of neurons in our brain results in high-level cognitive phenomena such as planning and language. NO_SCPLOWEMOC_SCPLOW is a simple mathematical formulation of six basic and broadly accepted principles of neuroscience: excitatory neurons, brain areas, random synapses, Hebbian plasticity, local inhibition, and inter-area inhibition. We implement with NO_SCPLOWEMOC_SCPLOW a simulated neuromorphic system, which is capable of basic language acquisition: starting from a tabula rasa, the system learns, in any language, the semantics of words, their syntactic role (verb versus noun), the word order of the language, and the ability to generate novel sentences, all through the exposure to a number of grounded sentences in the same language that is similar to language acquisition by humans. We discuss several possible extensions and implications of this result.

neuroscience↗

bioRxiv2019

Brain computation by assemblies of neurons

Assemblies are large populations of neurons believed to imprint memories, concepts, words and other cognitive information. We identify a repertoire of operations on assemblies. These operations correspond to properties of assemblies observed in experiments, and can be shown, analytically and through simulations, to be realizable by generic, randomly connected populations of neurons with Hebbian plasticity and inhibition. Operations on assemblies include: projection (duplicating an assembly by creating a new assembly in a downstream brain area); reciprocal projection (a variant of projection also entailing synaptic connectivity from the newly created assembly to the original one); association (increasing the overlap of two assemblies in the same brain area to reflect cooccurrence or similarity of the corresponding concepts); merge (creating a new assembly with ample synaptic connectivity to and from two existing ones); and pattern-completion (firing of an assembly, with some probability, in response to the firing of some but not all of its neurons). Our analytical results establishing the plausibility of these operations are proved in a simplified mathematical model of cortex: a finite set of brain areas each containing n excitatory neurons, with random connectivity that is both recurrent (within an area) and afferent (between areas). Within one area and at any time, only k of the n neurons fire -- an assumption that models inhibition and serves to define both assemblies and areas -- while synaptic weights are modified by Hebbian plasticity, as well as homeostasis. Importantly, all neural apparatus needed for the functionality of the assembly operations is created on the fly through the randomness of the synaptic network, the selection of the k neurons with the highest synaptic input, and Hebbian plasticity, without any special neural circuits assumed to be in place. Assemblies and their operations constitute a computational model of the brain which we call the Assembly Calculus, occupying a level of detail intermediate between the level of spiking neurons and synapses, and that of the whole brain. As with high-level programming languages, a computation in the Assembly Calculus (that is, a coherent sequence of assembly operations accomplishing a task) can ultimately be reduced -- "compiled down" -- to computation by neurons and synapses; however, it would be far more cumbersome and opaque to represent the same computation that way. The resulting computational system can be shown, under assumptions, to be in principle capable of carrying out arbitrary computations. We hypothesize that something like it may underlie higher human cognitive functions such as reasoning, planning, and language. In particular, we propose a plausible brain architecture based on assemblies for implementing the syntactic processing of language in cortex, which is consistent with recent experimental results.

neuroscience↗

bioRxiv2018

Detection of arterial wall abnormalities via Bayesian model selection

Patient-specific modeling of hemodynamics in arterial networks has so far relied on parameter estimation for inexpensive or small-scale models. We describe here a Bayesian uncertainty quantification framework which makes two major advances: an efficient parallel implementation, allowing parameter estimation for more complex forward models, and a system for practical model selection, allowing evidence-based comparison between distinct physical models. We demonstrate the proposed methodology by generating simulated noisy flow velocity data from a branching arterial tree model in which a structural defect is introduced at an unknown location; our approach is shown to accurately locate the abnormality and estimate its physical properties even in the presence of significant observational and systemic error. As the method readily admits real data, it shows great potential in patient-specific parameter fitting for hemodynamical flow models.

bioengineering↗

bioRxiv2017

Interleukin-4 restores neurogenic plasticity of the primary human neural stem cells through suppression of Kynurenic acid production upon Amyloid-β42 toxicity

The immune response is an important determinant of the plasticity and neurogenic capacity of neural stem cells (NSCs) upon amyloid-beta42 (A{beta}42) toxicity in Alzheimers disease (AD). However, the direct effects of individual immuno-modulatory effectors on NSC plasticity remain to be elucidated and are the motivation for reductionist tissue-mimetic culture experiments. Using starPEG-Heparin hydrogel system that provides a defined 3D cell-instructive neuro-microenvironment culture system, sustains high levels of proliferative and neurogenic activity of human NSCs, and recapitulates the fundamental pathological consequences of Amyloid toxicity upon A{beta}42 administration, we found that the anti-inflammatory cytokine interleukin-4 (IL4) restores the plasticity and neurogenic capacity of NSCs by suppressing the A{beta}42-induced kynurenic acid-producing enzyme kynurenine aminotransferase 2 (KAT2), which we also found to be upregulated in the brains of the AD model, APP/PS1dE9 mouse. Our transcriptome analyses showed that IL4 treatment restores the expression levels of NSC and cortical subtype markers. Thus, our dissective neuro-microenvironment culture revealed IL4-mediated neuroinflammatory crosstalk for human NSC plasticity and predicted a new mechanistic target for therapeutic intervention in AD.

neuroscience↗

bioRxiv2017

Instructive starPEG-Heparin biohybrid 3D cultures for modeling human neural stem cell plasticity, neurogenesis, and neurodegeneration

Three-dimensional models of human neural development and neurodegeneration are crucial when exploring stem-cell-based regenerative therapies in a tissue-mimetic manner. However, existing 3D culture systems are not sufficient to model the inherent plasticity of NSCs due to their ill-defined composition and lack of controllability of the physical properties. Adapting a glycosaminoglycan-based, cell-responsive hydrogel platform, we stimulated primary and induced human neural stem cells (NSCs) to manifest neurogenic plasticity and form extensive neuronal networks in vitro. The 3D cultures exhibited neurotransmitter responsiveness, electrophysiological activity, and tissue-specific extracellular matrix (ECM) deposition. By whole transcriptome sequencing, we identified that 3D cultures express mature neuronal markers, and reflect the in vivo make-up of mature cortical neurons compared to 2D cultures. Thus, our data suggest that our established 3D hydrogel culture supports the tissue-mimetic maturation of human neurons. We also exemplarily modeled neurodegenerative conditions by treating the cultures with A{beta}42 peptide and observed the known human pathological effects of Alzheimers disease including reduced NSC proliferation, impaired neuronal network formation, synaptic loss and failure in ECM deposition as well as elevated Tau hyperphosphorylation and formation of neurofibrillary tangles. We determined the changes in transcriptomes of primary and induced NSC-derived neurons after A{beta}42, providing a useful resource for further studies. Thus, our hydrogel-based human cortical 3D cell culture is a powerful platform for studying various aspects of neural development and neurodegeneration, as exemplified for A{beta}42 toxicity and neurogenic stem cell plasticity.\n\nSignificanceNeural stem cells (NSC) are reservoir for new neurons in human brains, yet they fail to form neurons after neurodegeneration. Therefore, understanding the potential use of NSCs for stem cell-based regenerative therapies requires tissue-mimetic humanized experimental systems. We report the adaptation of a 3D bio-instructive hydrogel culture system where human NSCs form neurons that later form networks in a controlled microenvironment. We also modeled neurodegenerative toxicity by using Amyloid-beta4 peptide, a hallmark of Alzheimers disease, observed phenotypes reminiscent of human brains, and determined the global gene expression changes during development and degeneration of neurons. Thus, our reductionist humanized culture model will be an important tool to address NSC plasticity, neurogenicity, and network formation in health and disease.

neuroscience↗

bioRxiv2017

Associations between memory traces emerge in a generic neural circuit model through STDP

Memory traces and associations between them are fundamental for cognitive brain function. Neuron recordings suggest that distributed assemblies of neurons in the brain serve as memory traces for spatial information, real-world items, and concepts. How-ever, there is conflicting evidence regarding neural codes for associated memory traces. Some studies suggest the emergence of overlaps between assemblies during an association, while others suggest that the assemblies themselves remain largely unchanged and new assemblies emerge as neural codes for associated memory items. Here we study the emergence of neural codes for associated memory items in a generic computational model of recurrent networks of spiking neurons with a data-constrained rule for spike-timing-dependent plasticity (STDP). The model depends critically on two parameters, which control the excitability of neurons and the scale of initial synaptic weights. By modifying these two parameters, the model can reproduce both experimental data from the human brain on the fast formation of associations through emergent overlaps between assemblies, and rodent data where new neurons are recruited to encode the associated memories. Hence our findings suggest that the brain can use both of these two neural codes for associations, and dynamically switch between them during consolidation.

neuroscience↗