16 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…
Geoffrey Kasenbacher, Daniel Ruepp, Gerrit A. Ecke
Sparse coding provides a principled framework for signal representation by expressing an input as a linear combination of only a small number of basis functions. The Locally Competitive Algorithm (LCA) is particularly attractive in the context of neuromorphic computing because its dynamics, leaky integration…
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…
Mingchen Liu, Fei Song, Bailu Si, Liqin Zhou + 1 more
Spatial attention is often partitioned into endogenous, exogenous, and social forms, yet it remains unclear whether a single neural circuit can support all three and how their population codes are organized. Here we trained recurrent artificial neural networks (ANNs) with convolutional sensory front-ends on three…
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…
Tadanobu Chuyo Kamijo, Naoki Nakajima, Takeshi Aihara
The dentate gyrus (DG) decorrelates entorhinal inputs (pattern separation); area CA3 completes partial cues via recurrent autoassociation. The density of CA3 recurrent connectivity is contested, with estimates from ∼0.9% (10) to ∼9–11% (30). We ask how completion depends on recurrent connectivity (C_RC_) and whether…
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…
Lihong Cao
The human brain encodes a virtually infinite repertoire of semantic concepts using a finite number of neurons, a feat that defies the capacity limits of classical attractor networks. While “Concept Cells” in the medial temporal lobe (MTL) exhibit extreme sparsity, the information-theoretic principles governing their…
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…
Thomas Fel, Matthew Kowal, Mozes Jacobs, Dron Hazra + 21 more
What is the geometry of a visual percept? The most widely used protocols for decomposing neural network representations into interpretable parts treat concepts as isolated directions, yet recent work shows that concepts are often realized as geometric structures in low dimensional regions of activation space. We turn…
Shuhong Huang, Ruben Portugues, James E. Fitzgerald
The ultimate goal of sensory coding is to extract and represent the cues required for adaptive motor output. This suggests that sensory codes and behavioral outcomes may align, and a variety of studies have argued that both biological and engineered sensory systems represent stimuli similarly when they play similar…
Kyle Bojanek, Olivier Marre, Stephanie E. Palmer
Populations of sensory neurons are thought to be shaped by selective pressures for optimal information transmission, yet real neural circuits display substantial variability across stimulus repeats, across time, and between individuals. Reconciling this variability with normative theories requires understanding not…
Lingyun Ke, M. Hu
Encoding static images into spike trains is a crucial step for enabling Spiking Neural Networks (SNNs) to process visual information efficiently. However, existing schemes such as rate coding, Poisson encoding, and time-to-first-spike (TTFS) often ignore spatial relationships and yield temporally inconsistent spike…
Ghahari, Azar, Eden, Uri T.
In the last decade, there have been major advances in clusterless decoding algorithms for neural data analysis. These algorithms use the theory of marked point processes to describe the joint activity of many neurons simultaneously, without the need for spike sorting. In this study, we examine information-theoretic…
Authors not listed
The discovery of chemically novel or structurally anomalous metal-organic frameworks (MOFs) is essential for expanding reticular design space and enhancing dataset reliability. We present CHEM-AD (Chemically Unusual Metal–organic Frameworks via Autoencoder-based Detection), a label-free, CPU-efficient pipeline that…