14 papers · ranked by Valyu relevance
Sheng Zhou, Chang Gao, Tobi Delbruck, Marian Verhelst + 1 more
Artificial intelligence (AI) has made significant strides towards efficient online processing of sensory signals at the edge through the use of deep neural networks with ever-expanding size. However, this trend has brought with it escalating computational costs and energy consumption, which have become major obstacles…
Catherine W. Tallman, Peter N. Steinmetz, John T. Wixted
Neurocomputational models hold that individual episodic memories are represented by a sparse, pattern-separated coding scheme in the hippocampus. Animal studies further suggest that the allocation of neurons to such codes is non-random and may be biased by their excitability at the time of encoding. Here, utilizing an…
Marko A. Ruslim, Martin J. Spencer, Hinze Hogendoorn, Hamish Meffin + 3 more
Many experimental and computational studies deal with sparseness, balance, and decorrelation in neural networks and explain the presence of these properties as fulfilling requirements related to optimum energy efficiency, network stability, and information representation. These studies leave the question of how these…
Yusuke Kuniyoshi, Tadashi Yamazaki
The hippocampus is thought to support spatial memory and navigation by constructing predictive representations of the environment. Predictive map theory formalizes this function as a successor representation (SR). However, existing models assume a fixed and uniform distribution of place fields, despite experimental…
Runguang Zhou, Douglas Zhou, Songting Li, Xiaoyu Chen + 1 more
Under the Marr-Ito-Albus framework, the cerebellum performs supervised learning in Purkinje cells upon the unsupervised sparse representations generated within granule cells, contributing fundamentally to associative learning in motor control. However, the specific mechanisms through which cerebellar circuitry and…
Liyu Qian, Zikai Zhu, Yuhan He, Jie Lu + 4 more
This paper presents a neuromorphic processing system integrating a compressed sensing spiking neural network (CSSNN) designed for sparse signal classification. The proposed CSSNN combines data coding, data compression, and SNN classification, enabling end-to-end optimization of network performance and model…
Naomi Auer, Lars Chen, Jakob Stubenrauch, Benjamin Lindner + 2 more
The brain can efficiently learn and form memories based on limited exposure to stimuli, often even in single trials. Two key factors are believed to support this ability: large synaptic plasticity to strongly encode new memories; and sparse coding, leading to low overlap between memory representations and to small…
Lei Guo, Nancheng Ma, Zhuoxuan Wang, Rumeng Liu + 1 more
Spiking neural networks (SNNs) offer inherent advantages in processing temporal information. However, their network topologies are predominantly algorithm-generated, lacking constraints from biological brain connectivity, which limits their bio-plausibility. In our previous work, we constructed a spiking neural network…
Sara Santos Silva, Daniela Bühn, Paxton Hall, William Clark + 2 more
Social visual processing in vertebrates employs sophisticated neural mechanisms ranging from categorical face cells to distributed sparse coding systems. In primates, recent evidence supports a “tuning landscape” model where neurons signal distances to prototypes in high-dimensional space rather than functioning as…
Hyeyoung Shin
Perception is a process of inference, whereby incoming sensory evidence is interpreted based on prior expectations about the sensory world. Thus, the neural code of perception should be evaluated based on how optimally it computes perceptual inference. However, the neural code of perception has conventionally been…
Joram Keijser, Sadra Sadeh
Neural computation spans a range of scales, from dendritic integration within individual neurons to collective dynamics across networks. Voltage imaging provides a powerful approach to study these processes, as it captures both spiking and subthreshold activity of genetically defined populations with high temporal…
Oak E. Milam, Gary Marsat, Stephen D. Ginsberg
Localizing the source of a signal requires sophisticated neural mechanisms, and we are still uncovering the coding principles that support accurate spatial processing. Weakly electric fish can detect and localize distant conspecifics, but the way this spatial information is encoded is unclear. Here, we investigate the…
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…
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…