21 papers · ranked by Valyu relevance
Li Ji-An, Fabio Stefanini, Marcus K. Benna, Stefano Fusi
Synaptic plasticity is a complex phenomenon involving multiple biochemical processes that operate on different timescales. We recently showed that this complexity can greatly increase the memory capacity of neural networks when the variables that characterize the synaptic dynamics have limited precision, as in…
Heike Blockus, Franck Polleux
Synaptic connectivity within neural circuits is characterized by high degrees of cellular and subcellular specificity. This precision arises from the combined action of several classes of molecular cues, transmembrane receptors, secreted cues and extracellular matrix components, coordinating transitions between axon…
Friedemann Zenke, Axel Laborieux
continual learning Authors: ['Friedemann Zenke' 'Axel Laborieux'] Humans and animals learn throughout life. Such continual learning is crucial for intelligence. In this chapter, we examine the pivotal role plasticity mechanisms with complex internal synaptic dynamics could play in enabling this ability in neural…
Ido Aizenbud, Daniela Yoeli, David Beniaguev, Christiaan PJ de Kock + 2 more
Humans exhibit unique cognitive abilities within the animal kingdom, but the neural mechanisms driving these advanced capabilities remain poorly understood. Human cortical neurons differ from those of other species, such as rodents, in both their morphological and physiological characteristics. Could the distinct…
Marcus K. Benna, Stefano Fusi
Memories are stored, retained, and recollected through complex, coupled processes operating on multiple timescales. To understand the computational principles behind these intricate networks of interactions we construct a broad class of synaptic models that efficiently harnesses biological complexity to preserve…
Gabriel Moreno Cunha, Gillberto Corso, Matheus Phellipe Brasil de Sousa, Gustavo Zampier dos Santos Lima
'Matheus Phellipe Brasil de Sousa' 'Gustavo Zampier dos Santos Lima'] The inquiry into the origin of brain complexity remains a pivotal question in neuroscience. While synaptic stimuli are acknowledged as significant, their efficacy often falls short in elucidating the extensive interconnections of the brain and…
Margot Wagner, Thomas M. Bartol, Terrence J. Sejnowski, Gert Cauwenberghs
Progress in computational neuroscience toward understanding brain function is challenged both by the complexity of molecular-scale electrochemical interactions at the level of individual neurons and synapses and the dimensionality of network dynamics across the brain covering a vast range of spatial and temporal…
Gabriel Moreno Cunha, Gilberto Corso, Matheus Phellipe Brasil de Sousa, Gustavo Zampier dos Santos Lima + 1 more
'Matheus Phellipe Brasil de Sousa' 'Gustavo Zampier dos Santos Lima' 'Michele Giugliano'] The inquiry into the origin of brain complexity remains a pivotal question in neuroscience. While synaptic stimuli are acknowledged as significant, their efficacy often falls short in elucidating the extensive interconnections of…
Stefano Fusi
Memory is a complex phenomenon that involves several distinct mechanisms. These mechanisms operate at different spatial and temporal levels. This chapter focuses on the theoretical framework and the mathematical models that have been developed to understand how these mechanisms are orchestrated to store, preserve and…
Xundong Wu, Gabriel C. Mel, D. J. Strouse, Bartlett W. Mel + 1 more
In order to record the stream of autobiographical information that defines our unique personal history, our brains must form durable memories from single brief exposures to the patterned stimuli that impinge on them continuously throughout life. However, little is known about the computational strategies or neural…
Sapir Shapira, Ido Aizenbud, Daniela Yoeli, Yoni Leibner + 4 more
'Huibert D. Mansvelder' 'Christiaan P. J. de Kock' 'Michael London' 'Idan Segev'] The human brain’s remarkable computational power enables parallel processing of vast information, integrating sensory inputs, memories, and emotions for rapid learning, adaptability, and creativity - far surpassing present-day artificial…
Anamika Agrawal, Michael A. Buice
The simple linear threshold units used in many artificial neural networks have a limited computational capacity. Famously, a single unit cannot handle non-linearly separable problems like XOR. In contrast, real neurons exhibit complex morphologies as well as active dendritic integration, suggesting that their…
Ido Aizenbud, David Beniaguev, Noam Pnueli, Idan Segev + 1 more
Cortical pyramidal neurons possess elaborate dendritic trees with diverse nonlinear membrane conductances and thousands of plastic synapses, suggesting substantial computational capabilities at the single-cell level. Yet, what can a neuron compute remains an open question, largely due to the lack of a systematic…
W. Tecumseh Fitch
Contemporary neural network models often overlook a central biological fact about neural processing: that single neurons are themselves complex, semi-autonomous computing systems. Both the information processing and information storage abilities of actual biological neurons vastly exceed the simple weighted sum of…
Renato Duarte, Abigail Morrison
Complexity and heterogeneity are intrinsic to neurobiological systems, manifest in every process, at every scale, and are inextricably linked to the systems’ emergent collective behaviours and function. However, the majority of studies addressing the dynamics and computational properties of biologically inspired…
Afsaneh Taheri Kal-Koshvandi
We present the first light-free, genetically minimal platform for spatiotemporally resolved proximity labeling across five neuronal compartments—synaptic, nuclear, endosomal, mitochondrial, and activity-dependent microdomains. A covalent "catalyst–luciferase sandwich" architecture site-specifically conjugates five…
Yuanmo Wang, Ajay Pradhan, Pankaj Gupta, Jörg Hanrieder + 2 more
Acetylcholine (ACh) is a critical neurotransmitter influencing various neurophysiological functions. Despite its significance, there is a lack of quantitative methods with adequate spatiotemporal resolution for recording single exocytotic efflux of ACh. In this study, we present an ultrafast amperometric ACh biosensor…
Sofie S. Kristensen, Kaan Kesgin, Henrik Jörntell
Complexity is important for flexibility of natural behavior and for the remarkably efficient learning of the brain. Here we assessed the signal complexity among neuron populations in somatosensory cortex (S1). To maximize our chances of capturing population level signal complexity, we used highly repeatable resolvable…
Nastaran Lotfi, Thaís Feliciano, Leandro A. A. Aguiar, Thais Priscila Lima Silva + 5 more
'Thais Priscila Lima Silva' 'Tawan T. A. Carvalho' 'Osvaldo A. Rosso' 'Mauro Copelli' 'Fernanda S. Matias' 'Pedro V. Carelli'] Complex systems are typically characterized as an intermediate situation between a complete regular structure and a random system. Brain signals can be studied as a striking example of such…
Daniela Egas Santander, Christoph Pokorny, András Ecker, Jānis Lazovskis + 5 more
'Jānis Lazovskis' 'Matteo Santoro' 'Jason P. Smith' 'Kathryn Hess' 'Ran Levi' 'Michael W. Reimann'] Title: Summary We hypothesized that the heterogeneous architecture of biological neural networks provides a substrate to regulate the well-known tradeoff between robustness and efficiency, thereby allowing different…
Authors not listed
Acetylcholine (ACh) is a key neurotransmitter involved in cognitive function, motor control, and synaptic modulation, yet its electrochemical inactivity and the rapid kinetics of exocytosis have hindered real-time quantal detection. Previous micrometer-scale enzymatic ACh biosensors enabled sub-millisecond…