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Search · four archives
25 papers · ranked by Valyu relevance
Nicolas Y. Masse, Matthew C. Rosen, Doris Y. Tsao, David J. Freedman
The brains of all animals are plastic, allowing us to form new memories, adapt to new environments, and to learn new tasks. What is less clear is how much plasticity is required to perform these cognitive functions: does learning require widespread plasticity across the brain, or can learning occur with more rigid…
Alexandra R. van den Berg, Pieter R. Roelfsema, Sander M. Bohte, Shenbing Kuang
'Shenbing Kuang'] The acquisition of knowledge and skills does not occur in isolation but learning experiences amalgamate within and across domains. The process through which learning can accelerate over time is referred to as learning-to-learn or meta-learning. While meta-learning can be implemented in recurrent…
Alexandra R. van den Berg, Pieter R. Roelfsema, Sander M. Bohte
The acquisition of knowledge does not occur in isolation; rather, learning experiences in the same or similar domains amalgamate. This process through which learning can accelerate over time is referred to as learning-to-learn or meta-learning. While meta-learning can be implemented in recurrent neural networks, these…
Markus Meister
Animals can learn efficiently from a single experience and change their future behavior in response. However, in other instances, animals learn very slowly, requiring thousands of experiences. Here I survey tasks involving fast and slow learning and consider some hypotheses for what differentiates the underlying neural…
Yoshimasa Kubo, Eric Chalmers, Artur Luczak
Backpropagation has been used to train neural networks for many years, allowing them to solve a wide variety of tasks like image classification, speech recognition, and reinforcement learning tasks. But the biological plausibility of backpropagation as a mechanism of neural learning has been questioned. Equilibrium…
Max Dabagia, Christos H. Papadimitriou, Santosh Vempala
An assembly is a large population of neurons whose synchronous firing is hypothesized to represent a memory, concept, word, and other cognitive categories. Assemblies are believed to provide a bridge between high-level cognitive phenomena and low-level neural activity. Recently, a computational system called the…
Geunbo Yang, Wongyu Lee, Youjung Seo, Choongseop Lee + 5 more
'Jongkil Park' 'Donggyu Sim' 'Cheolsoo Park' 'Richard J. Povinelli'] A spiking neural network (SNN) is a type of artificial neural network that operates based on discrete spikes to process timing information, similar to the manner in which the human brain processes real-world problems. In this paper, we propose a new…
Gaston Sivori, Tomoki Fukai
Neurons are thought to detect salient patterns amidst noise in continuous information streams, but their rapidity tends to be overlooked. Consequently, theoretical neuron models lack key mechanistic features that are suggested to underlie biological neuron rapid learning of input patterns. To unravel these features, we…
Magdalena Kozielska, Franz J. Weissing
The ability to learn from past experience is an important adaptation, but how natural selection shapes learning is not well understood. Here, we present a novel way of modelling learning using small neural networks and a simple, biology-inspired learning algorithm. Learning affects only part of the network, and it is…
Yuhao Sun, Wantong Liao, Jinhao Li, Xinche Zhang + 3 more
'Zhiyuan Ma' 'Sen Song'] Synaptic plasticity underlies adaptive learning in neural systems, offering a biologically plausible framework for reward-driven learning. However, a question remains: how can plasticity rules achieve robustness and effectiveness comparable to error backpropagation? In this study, we introduce…
Birgitta Dresp
This selective review explores biologically inspired learning as a model for intelligent robot control and sensing technology on the basis of specific examples. Hebbian synaptic learning is discussed as a functionally relevant model for machine learning and intelligence, as explained on the basis of examples from the…
Yash Mehta, Danil Tyulmankov, Adithya E. Rajagopalan, Glenn C. Turner + 2 more
Inferring the synaptic plasticity rules that govern learning in the brain is a key challenge in neuroscience. We present a novel computational method to infer these rules from experimental data, applicable to both neural and behavioral data. Our approach approximates plasticity rules using a parameterized function…
Yash Mehta, Danil Tyulmankov, Adithya E. Rajagopalan, Glenn C. Turner + 2 more
'Glenn C. Turner' 'James E. Fitzgerald' 'Jan Funke'] Inferring the synaptic plasticity rules that govern learning in the brain is a key challenge in neuroscience. We present a novel computational method to infer these rules from experimental data, applicable to both neural and behavioral data. Our approach approximates…
Guillaume Etter
Inspired by key neuroscience principles, deep learning has driven exponential breakthroughs in developing functional models of perception and other cognitive processes. A key to this success has been the implementation of crucial features found in biological neural networks: neurons as units of information transfer…
Navid Shervani-Tabar, Robert Rosenbaum
Backpropagation is widely used to train artificial neural networks, but its relationship to synaptic plasticity in the brain is unknown. Some biological models of backpropagation rely on feedback projections that are symmetric with feedforward connections, but experiments do not corroborate the existence of such…
Manu Srinath Halvagal, Friedemann Zenke
Recognition of objects from sensory stimuli is essential for survival. To that end, sensory networks in the brain must form object representations invariant to stimulus changes, such as size, orientation and context. Although Hebbian plasticity is known to shape sensory networks, it fails to create invariant object…
Samuel J. Gershman
The most widely accepted view of memory in the brain holds that synapses are the storage sites of memory, and that memories are formed through associative modification of synapses. This view has been challenged on conceptual and empirical grounds. As an alternative, it has been proposed that molecules within the cell…
Jayani Hewavitharana, Kathleen Steinhofel, Karl Peter Giese, Carolina Moretti Ierardi + 1 more
'Carolina Moretti Ierardi' 'Amida Anand'] Learning and memory are fundamental processes of the brain which are essential for acquiring and storing information. However, with ageing the brain undergoes significant changes leading to age-related cognitive decline. Although there are numerous studies on computational…
Lina Eckert, Maria Sol Vidal-Saez, Ziyuan Zhao, Jordi Garcia-Ojalvo + 2 more
The ability to learn is typically attributed to animals with brains. However, the apparently simplest form of learning, habituation, in which a steadily decreasing response is exhibited to a repeated stimulus, is found not only in animals but also in single-cell organisms and individual mammalian cells. Habituation has…
Colin Bredenberg, Cristina Savin
Normative models of synaptic plasticity use a combination of mathematics and computational simulations to arrive at predictions of behavioral and network-level adaptive phenomena. In recent years, there has been an explosion of theoretical work on these models, but experimental confirmation is relatively limited. In…
Colin Bredenberg, Cristina Savin
Normative models of synaptic plasticity use a combination of mathematics and computational simulations to arrive at predictions of behavioral and network-level adaptive phenomena. In recent years, there has been an explosion of theoretical work on these models, but experimental confirmation is relatively limited. In…
Authors not listed
The use of hybrid models, combing mechanistic and machine learning (ML), has emerged as a promising approach, contributing to the development of Industry 4.0. This work presents a hybrid model that forecasts minibioreactor (MBR) production runs of mammalian cell culture recombinant for monoclonal antibodies (mAbs)…
Authors not listed
Predicting drug-induced toxicity remains a central challenge in computational toxicology, particularly for organ-specific adverse effects that arise from diverse structural, biochemical, and mechanistic origins. Existing deep learning models excel at pattern recognition but often lack mechanistic interpretability…
Authors not listed
In recent years, generative deep learning has emerged as a transformative approach in drug design, promising to explore the vast chemical space and generate novel molecules with desired biological properties. This perspective examines the challenges and opportunities of applying generative models to drug discovery…
Authors not listed
Machine olfaction—the artificial replication of the sense of smell—faces significant challenges due to the absence of large, standardized training datasets. Unlike vision, language, and audio models, which benefit from extensive corpora such as ImageNet, GLUE, and AudioSet, olfaction lacks scaled equivalents and…