21 papers · ranked by Valyu relevance
Peter Cariani, Janet M. Baker
Here we present evidence for the ubiquity of fine spike timing and temporal coding broadly observed across sensory systems and widely conserved across diverse phyla, spanning invertebrates and vertebrates. A taxonomy of basic neural coding types includes channel activation patterns, temporal patterns of spikes, and…
Janet M. Baker, Peter Cariani
Time is essential for understanding the brain. A temporal theory for realizing major brain functions (e.g., sensation, cognition, motivation, attention, memory, learning, and motor action) is proposed that uses temporal codes, time-domain neural networks, correlation-based binding processes and signal dynamics. It…
Nigel Crook, Alexander D. Rast, Eleni Elia, Mario Antoine Aoun
Introduction In this work, we introduce a novel approach to one of the historically fundamental questions in neural networks: how to encode information? More particularly, we look at temporal coding in spiking networks, where the timing of a spike as opposed to the frequency, determines the information content. In…
Duho Sihn, Sung-Phil Kim
Hierarchical structures constitute a wide array of brain areas, including the visual system. One of the important questions regarding visual hierarchical structures is to identify computational principles for assigning functions that represent the external world to hierarchical structures of the visual system. Given…
Sam Post, William Mol, Noorhan Rahmatullah, Anubhuti Goel
Whether in music, language, baking, or memory, our experience of the world is fundamentally linked to time. However, it is unclear how temporal information is encoded, particularly in the range of milliseconds to seconds. Temporal processing at this scale is critical to prediction and survival, such as in a prey…
Iulia-Maria Comşa, Luca Versari, Thomas Fischbacher, Jyrki Alakuijala
'Jyrki Alakuijala'] Spiking neural networks with temporal coding schemes process information based on the relative timing of neuronal spikes. In supervised learning tasks, temporal coding allows learning through backpropagation with exact derivatives, and achieves accuracies on par with conventional artificial neural…
Zihan Pan, Jibin Wu, Malu Zhang, Haizhou Li + 1 more
—Neural encoding plays an important role in faithfully describing the temporally rich patterns, whose instances include human speech and environmental sounds. For tasks that involve classifying such spatio-temporal patterns with the Spiking Neural Networks (SNNs), how these patterns are encoded directly influence the…
Shanglin Zhou, Sotiris C. Masmanidis, Dean V. Buonomano
Converging evidence suggests the brain encodes time in time-varying patterns of neural activity, including neural sequences, ramping activity, and complex dynamics. Temporal tasks that require producing the same time-dependent output patterns may have distinct computational requirements in regard to the need to exhibit…
Leila Reddy, Benedikt Zoefel, Jessy K. Possel, Judith C. Peters + 6 more
An indispensable feature of episodic memory is our ability to temporally piece together different elements of an experience into a coherent memory. Hippocampal “time cells” – neurons that represent temporal information – may play a critical role in this process. While these cells have been repeatedly found in rodents…
Anna Cattani, Gaute T. Einevoll, Stefano Panzeri
The phase-of-firing code is a neural coding scheme whereby neurons encode information using the time at which they fire spikes within a cycle of the ongoing oscillatory pattern of network activity. This coding scheme may allow neurons to use their temporal pattern of spikes to encode information that is not encoded in…
Anthony Stigliani, Brianna Jeska, Kalanit Grill-Spector
How does high-level visual cortex process temporal aspects of our visual experience? By modeling neural responses with millisecond precision in separate sustained and transient channels, we predict fMRI responses for stimuli ranging from 33 ms to 20 s. Using this approach, we discovered that lateral temporal regions…
Yi Jiang, Sen Lu, Abhronil Sengupta
—Spiking Neural Networks (SNNs), recognized as the third generation of neural networks, are known for their bioplausibility and energy efficiency, especially when implemented on neuromorphic hardware. However, the majority of existing studies on SNNs have concentrated on deterministic neurons with rate coding, a method…
Sophie Bagur, Jacques Bourg, Alexandre Kempf, Thibault Tarpin + 7 more
The brain constantly associates time-varying sensory inputs with behavioral decisions. However, how temporal information in sensory neuronal circuits is linked to perception and behavioral output remains unclear. Here, by training mice to categorize patterned optogenetic stimulations in the auditory cortex, we show…
Mohammad Dehghani-Habibabadi, Klaus Richard Pawelzik
Spiking model neurons can be set up to respond selectively to specific spatio-temporal spike patterns by optimization of their input weights. It is unknown, however, if existing synaptic plasticity mechanisms can achieve this temporal mode of neuronal coding and computation. Here it is shown that changes of synaptic…
Lucas Rudelt, D. Marx, F. Paul Spitzner, Benjamin Cramer + 2 more
'Johannes Zierenberg' 'Viola Priesemann'] A core challenge for the brain is to process information across various timescales. This could be achieved by a hierarchical organization of temporal processing through intrinsic mechanisms (e.g., recurrent coupling or adaptation), but recent evidence from spike recordings of…
Lopez-Randulfe, Javier, Reeb, Nico + 2 more
Processing sensor data with spiking neural networks on digital neuromorphic chips requires converting continuous analog signals into spike pulses. Two strategies are promising for achieving low energy consumption and fast processing speeds in end-to-end neuromorphic applications. First, to directly encode analog…
Jared M. Salisbury, Stephanie E. Palmer
Almost all neural computations involve making predictions. Whether an organism is trying to catch prey, avoid predators, or simply move through a complex environment, the data it collects through its senses can guide its actions only to the extent that it can extract from these data information about the future state…
Fuzhan Rahmanian, Robert M. Lee, Dominik Linzner, Kathrin Michel + 4 more
Predicting and monitoring battery life early and across chemistries is a significant challenge due to the plethora of degradation paths, form factors, and electrochemical testing protocols. Existing models typically translate poorly across different electrode, electrolyte, and additive materials, mostly require a fixed…
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Real-world datasets in chemical engineering and bioengineering processes--such as those from catalytic reactors, multiphase flows, polymerization reactors, bioreactors, and clinical trials--can often be unlabelled or disorganized, rendering the training of existing supervised learning models ineffective at learning the…
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This research presents a novel approach to obstacle detection during navigation using a combination of Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks. The primary objective is to generate accurate image captions that describe the content of images, which is crucial for applications such…
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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…