17 papers · ranked by Valyu relevance
Fei Zhu, Mélissa Cizeron, Zhen Qiu, Ruth Benavides-Piccione + 7 more
'Maksym V. Kopanitsa' 'Nathan G. Skene' 'Babis Koniaris' 'Javier DeFelipe' 'Erik Fransén' 'Noboru H. Komiyama' 'Seth G.N. Grant'] Title: Summary Synapses are found in vast numbers in the brain and contain complex proteomes. We developed genetic labeling and imaging methods to examine synaptic proteins in individual…
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…
Rounak Chatterjee, Janet L. Paluh, Souradeep Chowdhury, Soham Mondal + 2 more
'Arnab Raha' 'Amitava Mukherjee'] Synaptic function and experience-dependent plasticity across multiple synapses are dependent on the types of neurons interacting as well as the intricate mechanisms that operate at the molecular level of the synapse. To understand the complexity of information processing at synaptic…
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…
Yuru Song, Marcus K. Benna
Cortical neurons often establish multiple synaptic contacts with the same postsynaptic neuron. To avoid functional redundancy of these parallel synapses, it is crucial that each synapse exhibits distinct computational properties. Here we model the current to the soma contributed by each synapse as a sigmoidal…
Terry Elliott
Models of associative memory with discrete state synapses learn new memories by forgetting old ones. In the simplest models, memories are forgotten exponentially quickly. Sparse population coding ameliorates this problem, as do complex models of synaptic plasticity that posit internal synaptic states, giving rise to…
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…
Zubaer Ibna Mannan, Sami Azam, Ram Kaji Budhathoki, MD Nur Alam + 1 more
Title: Graphical abstract A neuromemristive synapse is developed that can emulate both homo- and heterosynaptic plasticity, showcasing the homeostatic and modular input-dependent roles of heterosynaptic plasticity, along with homosynaptic long-term and short-term potentiation and depression, and functioning effectively…
Jochen Triesch, Anh Duong Vo, Anne-Sophie Hafner
Changes in the efficacies of synapses are thought to be the neurobiological basis of learning and memory. The efficacy of a synapse depends on its current number of neurotransmitter receptors. Recent experiments have shown that these receptors are highly dynamic, moving back and forth between synapses on time scales of…
Luis El Srouji, Mahmoud Abdelghany, Hari Rakul Ambethkar, Yun-Jhu Lee + 2 more
'Yun-Jhu Lee' 'Mehmet Berkay On' 'S. J. Ben Yoo'] With the increasing number of applications reliant on large neural network models, the pursuit of more suitable computing architectures is becoming increasingly relevant. Progress toward co-integrated silicon photonic and CMOS circuits provides new opportunities for…
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…
Alex Roxin, Stefano Fusi, Jeff Beck
Long-term memories are likely stored in the synaptic weights of neuronal networks in the brain. The storage capacity of such networks depends on the degree of plasticity of their synapses. Highly plastic synapses allow for strong memories, but these are quickly overwritten. On the other hand, less labile synapses…
Venkatakrishnan Ramaswamy, Arunava Banerjee
Certain types of neurons in the brain seem to possess richer computational ability at the single-neuron level than others due to anatomical as well as physiological factors such as elaborate dendritic arbors and a larger variety of ion channels respectively. While, clearly, such features seem to endow the neurons…
Danny J. J. Wang, Kay Jann, Chang Fan, Yang Qiao + 3 more
'Hanbing Lu' 'Yihong Yang'] Recently, non-linear statistical measures such as multi-scale entropy (MSE) have been introduced as indices of the complexity of electrophysiology and fMRI time-series across multiple time scales. In this work, we investigated the neurophysiological underpinnings of complexity (MSE) of…
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…