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…
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…
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…
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…
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…
Toktam Samiei, Hafiz Fareed Ahmed, Edward Zagha, Erfan Nozari
Despite over a century of research into the neural code, the fundamental principles by which the brain encodes sensory information remain debated. In this study we provide converging evidence for the presence of a dynamic, fast-switching integration of rate and temporal coding in the thalamus, primary visual cortex…
Shuzhen Zuo, Chenyu Wang, Lei Wang, Zhiyong Jin + 6 more
Episodic memory involves encoding and remembering the order of events experienced over time. Previous work examining the mechanisms of temporal-order memories has focused on the hippocampus and prefrontal cortices but has largely ignored the memory ensembles in the medial posterior parietal cortex (mPPC). Combining in…
Bryan C. Souza, Jan L. Klee, Luca Mazzucato, Francesco P. Battaglia
Temporal associations between sensory stimuli separated in time rely on the interaction between the hippocampus and medial prefrontal cortex (mPFC). However, it is not known how changes in their neural activity support the emergence of temporal association learning. Here, we use simultaneous electrophysiological…
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…
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…
Madhuvanthi Srivatsav R, Shantanu Chakrabartty, Chetan Singh Thakur
Address-Event-Representation (AER) is a spike-routing protocol that allows the scaling of neuromorphic and spiking neural network (SNN) architectures to a size that is comparable to that of digital neural network architectures. However, in conventional neuromorphic architectures, the AER protocol and, in general, any…
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…
Ido Aizenbud, Nicholas Audette, Ryszard Auksztulewicz, Krzysztof Basiński + 46 more
'Krzysztof Basiński' 'André M. Bastos' 'Michael Berry' 'Andres Canales-Johnson' 'Hannah Choi' 'Claudia Clopath' 'Uri Cohen' 'Rui Ponte Costa' 'Roberto De Filippo' 'Roman Doronin' 'Steven P. Errington' 'Jeffrey P. Gavornik' 'Colleen J. Gillon' 'Arno Granier' 'Jordan P. Hamm' 'Loreen Hertäg' 'Henry Kennedy' 'Sandeep…
Ziqiao Yu, Pengfei Sun, Danyal Akarca, Dan F. M. Goodman
We investigate the extent to which Spiking Neural Networks (SNNs) trained with Surrogate Gradient Descent (Surrogate GD), with and without delay learning, can learn from precise spike timing beyond firing rates. We first design synthetic tasks isolating intra-neuron inter-spike intervals and cross-neuron synchrony…
Zachary Friedenberger, Emerson F. Harkin, Katalin Tóth, Richard Naud
When a neuron breaks silence, it can emit action potentials in a number of patterns. Some responses are so sudden and intense that electrophysiologists felt the need to single them out, labeling action potentials emitted at a particularly high frequency with a metonym – bursts. Is there more to bursts than a figure of…
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…
Rongpei Gou, Jingyi Yang, Menghan Guo, Yingjun Chen + 1 more
Central nervous system (CNS) drugs have had a significant impact on human health, e.g., treating a wide range of neurodegenerative and psychiatric disorders. In recent years, deep learning-based generative models, particularly those for designing drugs from scratch, have shown great potential for accelerating drug…
Authors not listed
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…