14 papers · ranked by Valyu relevance
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…
Matteo De Matola, Giorgio Arcara
Convolutional neural networks (CNNs) are a class of artificial neural networks (ANNs). Since the early 2010s, they have been widely adopted as models of primate vision and classifiers of neuroimaging data, becoming relevant for a wealth of neuroscientific fields. However, the majority of neuroscience researchers come…
Birgit Kriener, Rishidev Chaudhuri, Ila R. Fiete
Identifying the maximal element (max, argmax) in a set is a core computational element in inference, decision making, optimization, action selection, consensus, and foraging. Running sequentially through a list of N fluctuating items takes N log(N) time to accurately find the max, prohibitively slow for large N. The…
Yuma Osako, Aineias Arango, Toshitake Asabuki
Animals flexibly combine learned behaviors into novel actions without practicing their combinations, yet the computational mechanisms that enable independently acquired computations to be expressed in parallel remain unclear. Here we show that feedback geometry during learning determines whether recurrent dynamics can…
Emerson F. Harkin, Peter R. Shen, Anish Goel, Blake A. Richards + 1 more
Neurons are very complicated computational devices, incorporating numerous non-linear processes, particularly in their dendrites. Biophysical models capture these processes directly by explicitly modelling physiological variables, such as ion channels, current flow, membrane capacitance, etc. However, another option…
Viktor Janos Olah, Nigel P Pedersen, Matthew JM Rowan
Understanding the activity of the mammalian brain requires an integrative knowledge of circuits at distinct scales, ranging from ion channel time constants to synaptic connection probabilities. To understand how multiple parameters contribute synergistically to circuit behavior as a whole, neuronal computational models…
Santiago Galella, Salva Ardid
Our brain can filter and integrate external information with internal representations to accomplish goal-directed behavior. The ability to switch between tasks effectively in response to context and external stimuli is a hallmark of cognitive control. Task switching occurs rapidly and efficiently, allowing us to…
Alexis M. Dubreuil
A functional role of the cerebral cortex is to form and hold representations of the sensory world for behavioral purposes. This is achieved by a sheet of neurons, organized in modules called cortical columns, that receives inputs in a peculiar manner, with only a few neurons driven by sensory inputs through thalamic…
Brett W. Larsen, Shaul Druckmann
Lateral and recurrent connections are ubiquitous in biological neural circuits. Yet while the strong computational abilities of feedforward networks have been extensively studied, our understanding of the role and advantages of recurrent computations that might explain their prevalence remains an important open…
Nikolaus Kriegeskorte
Recent advances in neural network modelling have enabled major strides in computer vision and other artificial intelligence applications. Human-level visual recognition abilities are coming within reach of artificial systems. Artificial neural networks are inspired by the brain and their computations could be…
G. Kandemir, D. H. Duncan, D. van Moorselaar, J. Theeuwes
For almost half a century, target-distractor similarity has been known to induce different visual search modes. When a target is highly salient, it can pop out, suggesting parallel processing of all items irrespective of set size. By contrast, high similarity among items requires item-by-item comparison with an…
David Rotermund, Klaus R. Pawelzik
Artificial deep convolutional networks (DCNs) meanwhile beat even human performance in challenging tasks. Recently DCNs were shown to also predict real neuronal responses. Their relevance for understanding the neuronal networks in the brain, however, remains questionable. In contrast to the unidirectional architecture…
Laura E. Suárez, Agoston Mihalik, Filip Milisav, Kenji Marshall + 4 more
The connection patterns of neural circuits form a complex network. How signaling in these circuits manifests as complex cognition and adaptive behaviour remains the central question in neuroscience. Concomitant advances in connectomics and artificial intelligence open fundamentally new opportunities to understand how…
Alexandros Goulas, Fabrizio Damicelli, Claus C Hilgetag
Biological neuronal networks (BNNs) constitute a niche for inspiration and analogy making for researchers that focus on artificial neuronal networks (ANNs). Moreover, neuroscientists increasingly use ANNs as a model for the brain. However, apart from certain similarities and analogies that can be drawn between ANNs and…