Статті в журналах: "Dynamic encoding"

1

Megason, Sean G. "Dynamic Encoding in the Notch Pathway." Developmental Cell 44, no. 4 (February 2018): 411–12. http://dx.doi.org/10.1016/j.devcel.2018.02.006.

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2

Schraudolph, Nicol N., and Richard K. Belew. "Dynamic Parameter Encoding for genetic algorithms." Machine Learning 9, no. 1 (June 1992): 9–21. http://dx.doi.org/10.1007/bf00993252.

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3

FRANK, SCHMIEDLE, GU¨NTHER WOLFANG, and DRECHSLER R. "Dynamic Re-Encoding During MDD Minimization." Multiple-Valued Logic 8, no. 5-6 (January 1, 2002): 625–43. http://dx.doi.org/10.1080/10236620215303.

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4

Pyles, J. A., and M. J. Tarr. "Neural mechanisms of dynamic object encoding." Journal of Vision 13, no. 9 (July 25, 2013): 492. http://dx.doi.org/10.1167/13.9.492.

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5

Chen, Kevin S. "Optimal Population Coding for Dynamic Input by Nonequilibrium Networks." Entropy 24, no. 5 (April 25, 2022): 598. http://dx.doi.org/10.3390/e24050598.

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The efficient coding hypothesis states that neural response should maximize its information about the external input. Theoretical studies focus on optimal response in single neuron and population code in networks with weak pairwise interactions. However, more biological settings with asymmetric connectivity and the encoding for dynamical stimuli have not been well-characterized. Here, we study the collective response in a kinetic Ising model that encodes the dynamic input. We apply gradient-based method and mean-field approximation to reconstruct networks given the neural code that encodes dynamic input patterns. We measure network asymmetry, decoding performance, and entropy production from networks that generate optimal population code. We analyze how stimulus correlation, time scale, and reliability of the network affect optimal encoding networks. Specifically, we find network dynamics altered by statistics of the dynamic input, identify stimulus encoding strategies, and show optimal effective temperature in the asymmetric networks. We further discuss how this approach connects to the Bayesian framework and continuous recurrent neural networks. Together, these results bridge concepts of nonequilibrium physics with the analyses of dynamics and coding in networks.

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6

Chen, Kevin S. "Optimal Population Coding for Dynamic Input by Nonequilibrium Networks." Entropy 24, no. 5 (April 25, 2022): 598. http://dx.doi.org/10.3390/e24050598.

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The efficient coding hypothesis states that neural response should maximize its information about the external input. Theoretical studies focus on optimal response in single neuron and population code in networks with weak pairwise interactions. However, more biological settings with asymmetric connectivity and the encoding for dynamical stimuli have not been well-characterized. Here, we study the collective response in a kinetic Ising model that encodes the dynamic input. We apply gradient-based method and mean-field approximation to reconstruct networks given the neural code that encodes dynamic input patterns. We measure network asymmetry, decoding performance, and entropy production from networks that generate optimal population code. We analyze how stimulus correlation, time scale, and reliability of the network affect optimal encoding networks. Specifically, we find network dynamics altered by statistics of the dynamic input, identify stimulus encoding strategies, and show optimal effective temperature in the asymmetric networks. We further discuss how this approach connects to the Bayesian framework and continuous recurrent neural networks. Together, these results bridge concepts of nonequilibrium physics with the analyses of dynamics and coding in networks.

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7

Chen, Kevin S. "Optimal Population Coding for Dynamic Input by Nonequilibrium Networks." Entropy 24, no. 5 (April 25, 2022): 598. http://dx.doi.org/10.3390/e24050598.

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Анотація:

The efficient coding hypothesis states that neural response should maximize its information about the external input. Theoretical studies focus on optimal response in single neuron and population code in networks with weak pairwise interactions. However, more biological settings with asymmetric connectivity and the encoding for dynamical stimuli have not been well-characterized. Here, we study the collective response in a kinetic Ising model that encodes the dynamic input. We apply gradient-based method and mean-field approximation to reconstruct networks given the neural code that encodes dynamic input patterns. We measure network asymmetry, decoding performance, and entropy production from networks that generate optimal population code. We analyze how stimulus correlation, time scale, and reliability of the network affect optimal encoding networks. Specifically, we find network dynamics altered by statistics of the dynamic input, identify stimulus encoding strategies, and show optimal effective temperature in the asymmetric networks. We further discuss how this approach connects to the Bayesian framework and continuous recurrent neural networks. Together, these results bridge concepts of nonequilibrium physics with the analyses of dynamics and coding in networks.

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8

Day, Mitchell L., Brent Doiron, and John Rinzel. "Subthreshold K+ Channel Dynamics Interact With Stimulus Spectrum to Influence Temporal Coding in an Auditory Brain Stem Model." Journal of Neurophysiology 99, no. 2 (February 2008): 534–44. http://dx.doi.org/10.1152/jn.00326.2007.

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Neurons in the auditory brain stem encode signals with exceptional temporal precision. A low-threshold potassium current, IKLT, present in many auditory brain stem structures and thought to enhance temporal encoding, facilitates spike selection of rapid input current transients through an associated dynamic gate. Whether the dynamic nature of IKLT interacts with the timescales in spectrally rich input to influence spike encoding remains unclear. We examine the general influence of IKLT on spike encoding of stochastic stimuli using a pattern classification analysis between spike responses from a ventral cochlear nucleus (VCN) model containing IKLT, and the same model with the IKLT dynamics removed. The influence of IKLT on spike encoding depended on the spectral content of the current stimulus such that maximal IKLT influence occurred for stimuli with power concentrated at frequencies low enough (<500 Hz) to allow IKLT activation. Further, broadband stimuli significantly decreased the influence of IKLT on spike encoding, suggesting that broadband stimuli are not well suited for investigating the influence of some dynamic membrane nonlinearities. Finally, pattern classification on spike responses was performed for physiologically realistic conductance stimuli created from various sounds filtered through an auditory nerve (AN) model. Regardless of the sound, the synaptic input arriving at VCN had similar low-pass power spectra, which led to a large influence of IKLT on spike encoding, suggesting that the subthreshold dynamics of IKLT plays a significant role in shaping the response of real auditory brain stem neurons.

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9

PARK, Youngsu, Jong-Wook KIM, Johwan KIM, and Sang Woo KIM. "New Encoding Method of Parameter for Dynamic Encoding Algorithm for Searches (DEAS)." IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E94-A, no. 9 (2011): 1804–16. http://dx.doi.org/10.1587/transfun.e94.a.1804.

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10

Staten, Henry. "Dynamic Encoding in a Simple Autogenic System." Biosemiotics 14, no. 3 (December 2021): 583–87. http://dx.doi.org/10.1007/s12304-021-09465-5.

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11

Mantiuk, Rafal, Grzegorz Krawczyk, Karol Myszkowski, and Hans-Peter Seidel. "Perception-motivated high dynamic range video encoding." ACM Transactions on Graphics 23, no. 3 (August 2004): 733–41. http://dx.doi.org/10.1145/1015706.1015794.

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12

Storkey, A. J., and C. K. I. Williams. "Image modeling with position-encoding dynamic trees." IEEE Transactions on Pattern Analysis and Machine Intelligence 25, no. 7 (July 2003): 859–71. http://dx.doi.org/10.1109/tpami.2003.1206515.

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13

Ma, Wen, Lin Chen, Chao Du, and Wei D. Lu. "Temporal information encoding in dynamic memristive devices." Applied Physics Letters 107, no. 19 (November 9, 2015): 193101. http://dx.doi.org/10.1063/1.4935220.

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14

Kraus, Nina. "Dynamic encoding of pitch, timing, and timbre." Journal of the Acoustical Society of America 124, no. 4 (October 2008): 2470. http://dx.doi.org/10.1121/1.4782707.

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15

Sonnen, Katharina F., and Alexander Aulehla. "Dynamic signal encoding—From cells to organisms." Seminars in Cell & Developmental Biology 34 (October 2014): 91–98. http://dx.doi.org/10.1016/j.semcdb.2014.06.019.

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16

Chalasani, Rakesh, and Jose C. Principe. "Context Dependent Encoding Using Convolutional Dynamic Networks." IEEE Transactions on Neural Networks and Learning Systems 26, no. 9 (September 2015): 1992–2004. http://dx.doi.org/10.1109/tnnls.2014.2360060.

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17

Natarajan, Rama, Quentin J. M. Huys, Peter Dayan, and Richard S. Zemel. "Encoding and Decoding Spikes for Dynamic Stimuli." Neural Computation 20, no. 9 (September 2008): 2325–60. http://dx.doi.org/10.1162/neco.2008.01-07-436.

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Naturally occurring sensory stimuli are dynamic. In this letter, we consider how spiking neural populations might transmit information about continuous dynamic stimulus variables. The combination of simple encoders and temporal stimulus correlations leads to a code in which information is not readily available to downstream neurons. Here, we explore a complex encoder that is paired with a simple decoder that allows representation and manipulation of the dynamic information in neural systems. The encoder we present takes the form of a biologically plausible recurrent spiking neural network where the output population recodes its inputs to produce spikes that are independently decodeable. We show that this network can be learned in a supervised manner by a simple local learning rule.

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18

Zhou, Shanglin, Sotiris C. Masmanidis, and Dean V. Buonomano. "Encoding time in neural dynamic regimes with distinct computational tradeoffs." PLOS Computational Biology 18, no. 3 (March 3, 2022): e1009271. http://dx.doi.org/10.1371/journal.pcbi.1009271.

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Converging evidence suggests the brain encodes time in dynamic patterns of neural activity, including neural sequences, ramping activity, and complex dynamics. Most temporal tasks, however, require more than just encoding time, and can have distinct computational requirements including the need to exhibit temporal scaling, generalize to novel contexts, or robustness to noise. It is not known how neural circuits can encode time and satisfy distinct computational requirements, nor is it known whether similar patterns of neural activity at the population level can exhibit dramatically different computational or generalization properties. To begin to answer these questions, we trained RNNs on two timing tasks based on behavioral studies. The tasks had different input structures but required producing identically timed output patterns. Using a novel framework we quantified whether RNNs encoded two intervals using either of three different timing strategies: scaling, absolute, or stimulus-specific dynamics. We found that similar neural dynamic patterns at the level of single intervals, could exhibit fundamentally different properties, including, generalization, the connectivity structure of the trained networks, and the contribution of excitatory and inhibitory neurons. Critically, depending on the task structure RNNs were better suited for generalization or robustness to noise. Further analysis revealed different connection patterns underlying the different regimes. Our results predict that apparently similar neural dynamic patterns at the population level (e.g., neural sequences) can exhibit fundamentally different computational properties in regards to their ability to generalize to novel stimuli and their robustness to noise—and that these differences are associated with differences in network connectivity and distinct contributions of excitatory and inhibitory neurons. We also predict that the task structure used in different experimental studies accounts for some of the experimentally observed variability in how networks encode time.

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19

Kamali, Fatemeh, Amir Abolfazl Suratgar, Mohamad Bagher Menhaj, and Reza Abbasi Asl. "Receptive Field Encoding Model for Dynamic Natural Vision." Neuroscience Journal of Shefaye Khatam 7, no. 4 (October 1, 2019): 1–7. http://dx.doi.org/10.29252/shefa.7.4.1.

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20

Beltracchi, Leo. "Encoding a Model-Based Display with Dynamic Data." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 44, no. 22 (July 2000): 579–82. http://dx.doi.org/10.1177/154193120004402222.

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A model-based display of the heat engine cycle for a nuclear power plant is defined and illustrated in terms of the thermodynamic first principles used to design the plant. The model-based display is a modified Rankine Cycle, the basic heat engine cycle for power plants. The display is made from measured process variables and the properties of water and presented on a CRT in iconic form, thereby providing a direct perception of the process. This structure of display design is an example of Rasmussen's means-ends hierarchy; starting with the abstract and ending with the specific display. Encoding the display with dynamic data aids operators in monitoring and interpreting the plant during transients and disturbances. Analytical data on the TMI-2 accident is used to illustrate the dynamic coding of the model-based display. The concepts discussed and illustrated are applicable to fossil and nuclear power plants and to other process industries.

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21

Yoon, Hee Kyung, Hee Yeon Im, and Soojin Park. "Differential encoding of dynamic objects in navigational context." Journal of Vision 20, no. 11 (October 20, 2020): 1744. http://dx.doi.org/10.1167/jov.20.11.1744.

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22

Barbieri, Riccardo, Loren M. Frank, David P. Nguyen, Michael C. Quirk, Victor Solo, Matthew A. Wilson, and Emery N. Brown. "Dynamic Analyses of Information Encoding in Neural Ensembles." Neural Computation 16, no. 2 (February 1, 2004): 277–307. http://dx.doi.org/10.1162/089976604322742038.

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Neural spike train decoding algorithms and techniques to compute Shan-non mutual information are important methods for analyzing how neural systems represent biological signals. Decoding algorithms are also one of several strategies being used to design controls for brain-machine inter-faces. Developing optimal strategies to desig n decoding algorithms and compute mutual information are therefore important problems in com-putational neuroscience. We present a general recursive filter decoding algorithm based on a point process model of individual neuron spiking activity and a linear stochastic state-space model of the biological signal. We derive from the algorithm new instantaneous estimates of the en-tropy, entropy rate, and the mutual information between the signal and the ensemble spiking activity. We assess the accuracy of the algorithm by computing, along with the decoding error, the true coverage probabil-ity of the approximate 0.95 confidence regions for the individual signal estimates. We illustrate the new algorithm by reanalyzing the position and ensemble neural spiking activity of CA1 hippocampal neurons from two rats foraging in an open circular environment. We compare the per-formance of this algorithm with a linear filter constructed by the widely used reverse correlation method. The median decoding error for Animal 1 (2) during 10 minutes of open foraging was 5.9 (5.5) cm, the median entropy was 6.9 (7.0) bits, the median information was 9.4 (9.4) bits, and the true coverage probability for 0.95 confidence regions was 0.67 (0.75) using 34 (32) neurons. These findings improve significantly on our pre-vious results and suggest an integrated approach to dynamically reading neural codes, measuring their properties, and quantifying the accuracy with which encoded information is extracted.

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23

Che, Hao, Zhijun Wang, Kai Zheng, and Bin Liu. "DRES: Dynamic Range Encoding Scheme for TCAM Coprocessors." IEEE Transactions on Computers 57, no. 7 (2008): 902–15. http://dx.doi.org/10.1109/tc.2007.70838.

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24

McCarthy, Sean T. "Choosing Encoding Parameters for High-Dynamic Range Streaming." SMPTE Motion Imaging Journal 127, no. 3 (April 2018): 26–38. http://dx.doi.org/10.5594/jmi.2018.2799778.

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25

Potter, Garrett D., Tommy A. Byrd, Andrew Mugler, and Bo Sun. "Dynamic Sampling and Information Encoding in Biochemical Networks." Biophysical Journal 112, no. 4 (February 2017): 795–804. http://dx.doi.org/10.1016/j.bpj.2016.12.045.

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26

Gu, Dong Juan, and Li Yong Wan. "A XML Document Coding Schema Based on Binary." Applied Mechanics and Materials 496-500 (January 2014): 1877–80. http://dx.doi.org/10.4028/www.scientific.net/amm.496-500.1877.

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In order to resolve the inefficiency for XML data query and support dynamic updates, etc, this paper has proposed an improved method to encode XML document nodes. On the basic of region encoding and the prefix encoding, it introduces a XML document coding schema base on binary (CSBB). The CSBB code use binary encoding strategy and make the bit string inserted in order. The bit string inserted algorithm can generate ordered bit string to reserve space for the inserted new nodes, and not influence on the others. Experiments shows the CSBB code can effectively avoid re-encoding of nodes, and supports the nodes Dynamic Update.

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27

Luo, Huan, Yadong Wang, David Poeppel, and Jonathan Z. Simon. "Concurrent Encoding of Frequency and Amplitude Modulation in Human Auditory Cortex: MEG Evidence." Journal of Neurophysiology 96, no. 5 (November 2006): 2712–23. http://dx.doi.org/10.1152/jn.01256.2005.

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A natural sound can be described by dynamic changes in envelope (amplitude) and carrier (frequency), corresponding to amplitude modulation (AM) and frequency modulation (FM), respectively. Although the neural responses to both AM and FM sounds are extensively studied in both animals and humans, it is uncertain how they are corepresented when changed simultaneously but independently, as is typical for ecologically natural signals. This study elucidates the neural coding of such sounds in human auditory cortex using magnetoencephalography (MEG). Using stimuli with both sinusoidal modulated envelope (ƒAM, 37 Hz) and carrier frequency (ƒFM, 0.3–8 Hz), it is demonstrated that AM and FM stimulus dynamics are corepresented in the neural code of human auditory cortex. The stimulus AM dynamics are represented neurally with AM encoding, by the auditory steady-state response (aSSR) at ƒAM. For sounds with slowly changing carrier frequency (ƒFM <5 Hz), it is shown that the stimulus FM dynamics are tracked by the phase of the aSSR, demonstrating neural phase modulation (PM) encoding of the stimulus carrier frequency. For sounds with faster carrier frequency change (ƒFM ≥ 5 Hz), it is shown that modulation encoding of stimulus FM dynamics persists, but the neural encoding is no longer purely PM. This result is consistent with the recruitment of additional neural AM encoding over and above the original neural PM encoding, indicating that both the amplitude and phase of the aSSR at ƒAM track the stimulus FM dynamics. A neural model is suggested to account for these observations.

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28

Shao, Yitian, Vincent Hayward, and Yon Visell. "Compression of dynamic tactile information in the human hand." Science Advances 6, no. 16 (April 2020): eaaz1158. http://dx.doi.org/10.1126/sciadv.aaz1158.

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A key problem in the study of the senses is to describe how sense organs extract perceptual information from the physics of the environment. We previously observed that dynamic touch elicits mechanical waves that propagate throughout the hand. Here, we show that these waves produce an efficient encoding of tactile information. The computation of an optimal encoding of thousands of naturally occurring tactile stimuli yielded a compact lexicon of primitive wave patterns that sparsely represented the entire dataset, enabling touch interactions to be classified with an accuracy exceeding 95%. The primitive tactile patterns reflected the interplay of hand anatomy with wave physics. Notably, similar patterns emerged when we applied efficient encoding criteria to spiking data from populations of simulated tactile afferents. This finding suggests that the biomechanics of the hand enables efficient perceptual processing by effecting a preneuronal compression of tactile information.

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29

Krishna, C. V., Abhijit Jas, and Nur A. Touba. "Achieving high encoding efficiency with partial dynamic LFSR reseeding." ACM Transactions on Design Automation of Electronic Systems 9, no. 4 (October 2004): 500–516. http://dx.doi.org/10.1145/1027084.1027089.

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30

Su, Bing, Jiahuan Zhou, Xiaoqing Ding, and Ying Wu. "Unsupervised Hierarchical Dynamic Parsing and Encoding for Action Recognition." IEEE Transactions on Image Processing 26, no. 12 (December 2017): 5784–99. http://dx.doi.org/10.1109/tip.2017.2745212.

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31

Váša, L., and O. Petřík. "Optimising Perceived Distortion in Lossy Encoding of Dynamic Meshes." Computer Graphics Forum 30, no. 5 (August 2011): 1439–49. http://dx.doi.org/10.1111/j.1467-8659.2011.02018.x.

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32

Aoi, Mikio C., Valerio Mante, and Jonathan W. Pillow. "Prefrontal cortex exhibits multidimensional dynamic encoding during decision-making." Nature Neuroscience 23, no. 11 (October 5, 2020): 1410–20. http://dx.doi.org/10.1038/s41593-020-0696-5.

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33

Larson, Gregory Ward. "LogLuv Encoding for Full-Gamut, High-Dynamic Range Images." Journal of Graphics Tools 3, no. 1 (January 1998): 15–31. http://dx.doi.org/10.1080/10867651.1998.10487485.

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34

Harrison, Neil A., Marcus A. Gray, and Hugo D. Critchley. "Dynamic pupillary exchange engages brain regions encoding social salience." Social Neuroscience 4, no. 3 (June 2009): 233–43. http://dx.doi.org/10.1080/17470910802553508.

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35

Tian, Hui, Ke Zhou, and Dan Feng. "Dynamic matrix encoding strategy for voice-over-IP steganography." Journal of Central South University of Technology 17, no. 6 (December 2010): 1285–92. http://dx.doi.org/10.1007/s11771-010-0633-y.

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36

Pannala, Mittu, Naren Ramakrishnan, and Rolf Müller. "Dynamic encoding of sensory information in biomimetic sonar baffle." Journal of the Acoustical Society of America 134, no. 5 (November 2013): 4211. http://dx.doi.org/10.1121/1.4831458.

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37

Lesica, Nicholas A., Chong Weng, Jianzhong Jin, Chun-I. Yeh, Jose-Manuel Alonso, and Garrett B. Stanley. "Dynamic Encoding of Natural Luminance Sequences by LGN Bursts." PLoS Biology 4, no. 7 (June 13, 2006): e209. http://dx.doi.org/10.1371/journal.pbio.0040209.

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38

Kimchi, E. Y., and M. Laubach. "Dynamic Encoding of Action Selection by the Medial Striatum." Journal of Neuroscience 29, no. 10 (March 11, 2009): 3148–59. http://dx.doi.org/10.1523/jneurosci.5206-08.2009.

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39

Rickert, J., A. Riehle, A. Aertsen, S. Rotter, and M. P. Nawrot. "Dynamic Encoding of Movement Direction in Motor Cortical Neurons." Journal of Neuroscience 29, no. 44 (November 4, 2009): 13870–82. http://dx.doi.org/10.1523/jneurosci.5441-08.2009.

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40

Bilkey, David K., and Jonathon M. Clearwater. "The dynamic nature of spatial encoding in the hippocampus." Behavioral Neuroscience 119, no. 6 (2005): 1533–45. http://dx.doi.org/10.1037/0735-7044.119.6.1533.

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41

Ruifeng Xu, S. N. Pattanaik, and C. E. Hughes. "High-Dynamic-Range Still-Image Encoding in JPEG 2000." IEEE Computer Graphics and Applications 25, no. 6 (November 2005): 57–64. http://dx.doi.org/10.1109/mcg.2005.133.

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42

Yu, Shouyi, Ying Peng, and Xiaoming Zheng. "System identification using dynamic ga based on numeric encoding." Journal of Central South University of Technology 4, no. 2 (November 1997): 128–31. http://dx.doi.org/10.1007/s11771-997-0014-3.

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43

Curreli, Sebastiano, Jacopo Bonato, Sara Romanzi, Stefano Panzeri, and Tommaso Fellin. "Complementary encoding of spatial information in hippocampal astrocytes." PLOS Biology 20, no. 3 (March 3, 2022): e3001530. http://dx.doi.org/10.1371/journal.pbio.3001530.

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Calcium dynamics into astrocytes influence the activity of nearby neuronal structures. However, because previous reports show that astrocytic calcium signals largely mirror neighboring neuronal activity, current information coding models neglect astrocytes. Using simultaneous two-photon calcium imaging of astrocytes and neurons in the hippocampus of mice navigating a virtual environment, we demonstrate that astrocytic calcium signals encode (i.e., statistically reflect) spatial information that could not be explained by visual cue information. Calcium events carrying spatial information occurred in topographically organized astrocytic subregions. Importantly, astrocytes encoded spatial information that was complementary and synergistic to that carried by neurons, improving spatial position decoding when astrocytic signals were considered alongside neuronal ones. These results suggest that the complementary place dependence of localized astrocytic calcium signals may regulate clusters of nearby synapses, enabling dynamic, context-dependent variations in population coding within brain circuits.

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44

V, Shavali, Dr Sreerama Reddy G.M, and Dr Ramana Reddy.P. "Performance of rc low encoding techniques for reducing coupling transitions." International Journal of Engineering & Technology 7, no. 4.20 (November 28, 2018): 36. http://dx.doi.org/10.14419/ijet.v7i4.20.22118.

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RC Network has delay propagation by wire and dynamic power dissipation. Basically it can perform two encoding techniques. They are Firstly it will reduce more dynamic power dissipation and delay propagation of wire simultaneously. Its simulation results of coupling activity and switching activity is more when the input is in Toggle state on 8-bit and for 32-bit data buses It increases. To reduce dynamic power is bus and total propagation delay the encoding techniques is Introduced which reduces coupling Coupling transitions, Dynamic power. Secondly it will also reduce more total power consumption when Width of Bus and Length of Bits Increases Its coupling activity is Reduced Gradually when the Data moves for one state to another State and switching activity is Reduced

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45

Chen, Ye, Peter Beech, Ziwei Yin, Shanshan Jia, Jiayi Zhang, Zhaofei Yu, and Jian K. Liu. "Decoding dynamic visual scenes across the brain hierarchy." PLOS Computational Biology 20, no. 8 (August 2, 2024): e1012297. http://dx.doi.org/10.1371/journal.pcbi.1012297.

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Understanding the computational mechanisms that underlie the encoding and decoding of environmental stimuli is a crucial investigation in neuroscience. Central to this pursuit is the exploration of how the brain represents visual information across its hierarchical architecture. A prominent challenge resides in discerning the neural underpinnings of the processing of dynamic natural visual scenes. Although considerable research efforts have been made to characterize individual components of the visual pathway, a systematic understanding of the distinctive neural coding associated with visual stimuli, as they traverse this hierarchical landscape, remains elusive. In this study, we leverage the comprehensive Allen Visual Coding—Neuropixels dataset and utilize the capabilities of deep learning neural network models to study neural coding in response to dynamic natural visual scenes across an expansive array of brain regions. Our study reveals that our decoding model adeptly deciphers visual scenes from neural spiking patterns exhibited within each distinct brain area. A compelling observation arises from the comparative analysis of decoding performances, which manifests as a notable encoding proficiency within the visual cortex and subcortical nuclei, in contrast to a relatively reduced encoding activity within hippocampal neurons. Strikingly, our results unveil a robust correlation between our decoding metrics and well-established anatomical and functional hierarchy indexes. These findings corroborate existing knowledge in visual coding related to artificial visual stimuli and illuminate the functional role of these deeper brain regions using dynamic stimuli. Consequently, our results suggest a novel perspective on the utility of decoding neural network models as a metric for quantifying the encoding quality of dynamic natural visual scenes represented by neural responses, thereby advancing our comprehension of visual coding within the complex hierarchy of the brain.

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46

Xu, Kuang, and Yuan Zhong. "Information and Memory in Dynamic Resource Allocation." Operations Research 68, no. 6 (November 2020): 1698–715. http://dx.doi.org/10.1287/opre.2019.1940.

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We propose a general framework, dubbed stochastic processing under imperfect information (SPII), to study the impact of information constraints and memories on dynamic resource allocation. The framework involves a stochastic processing network (SPN) scheduling problem in which the scheduler may access the system state only through a noisy channel, and resource allocation decisions must be carried out through the interaction between an encoding policy (that observes the state) and an allocation policy (that chooses the allocation). Applications in the management of large-scale data centers and human-in-the-loop service systems are among our chief motivations. We quantify the degree to which information constraints reduce the size of the capacity region in general SPNs and how such reduction depends on the amount of memories available to the encoding and allocation policies. Using a novel metric, capacity factor, our main theorem characterizes the reduction in capacity region (under “optimal” policies) for all nondegenerate channels and across almost all combinations of memory sizes. Notably, the theorem demonstrates, in substantial generality, that (1) the presence of a noisy channel always reduces capacity, (2) more memory for the allocation policy always improves capacity, and (3) more memory for the encoding policy has little to no effect on capacity. Finally, all of our positive (achievability) results are established through constructive, implementable policies.

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47

Eden, Uri T., Loren M. Frank, Riccardo Barbieri, Victor Solo, and Emery N. Brown. "Dynamic Analysis of Neural Encoding by Point Process Adaptive Filtering." Neural Computation 16, no. 5 (May 1, 2004): 971–98. http://dx.doi.org/10.1162/089976604773135069.

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Neural receptive fields are dynamic in that with experience, neurons change their spiking responses to relevant stimuli. To understand how neural systems adapt the irrepresentations of biological information, analyses of receptive field plasticity from experimental measurements are crucial. Adaptive signal processing, the well-established engineering discipline for characterizing the temporal evolution of system parameters, suggests a framework for studying the plasticity of receptive fields. We use the Bayes' rule Chapman-Kolmogorov paradigm with a linear state equation and point process observation models to derive adaptive filters appropriate for estimation from neural spike trains. We derive point process filter analogues of the Kalman filter, recursive least squares, and steepest-descent algorithms and describe the properties of these new fil-ters. We illustrate our algorithms in two simulated data examples. The first is a study of slow and rapid evolution of spatial receptive fields in hippocampal neurons. The second is an adaptive decoding study in which a signal is decoded from ensemble neural spiking activity as the recep-tive fields of the neurons in the ensemble evolve. Our results provide a paradigm for adaptive estimation for point process observations and suggest a practical approach for constructing filtering algorithms to track neural receptive field dynamics on a millisecond timescale.

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48

Jiang, Yi, Hong Bo Zhang, and Fan Lin. "A Continued Fraction Encoding and Labeling Scheme for Dynamic XML Data." Advanced Materials Research 204-210 (February 2011): 960–63. http://dx.doi.org/10.4028/www.scientific.net/amr.204-210.960.

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We present a new efficient XML encoding and labeling scheme for dynamic XML document called CFE (Continued Fraction-based Encoding) which labels nodes with continued fractions in this paper. CFE has three important properties which form the foundations of this paper. The experimental results show that CFE provides fairly reasonable XML query processing performance while completely avoiding re-labeling for updates.

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49

Wang, Ping, Hang-Yu Wang, Xing-Jie Gao, Hua-Xia Zhu, Xiao-Peng Zhang, Feng Liu, and Wei Wang. "Encoding and Decoding of p53 Dynamics in Cellular Response to Stresses." Cells 12, no. 3 (February 2, 2023): 490. http://dx.doi.org/10.3390/cells12030490.

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In the cellular response to stresses, the tumor suppressor p53 is activated to maintain genomic integrity and fidelity. As a transcription factor, p53 exhibits rich dynamics to allow for discrimination of the type and intensity of stresses and to direct the selective activation of target genes involved in different processes including cell cycle arrest and apoptosis. In this review, we focused on how stresses are encoded into p53 dynamics and how the dynamics are decoded into cellular outcomes. Theoretical modeling may provide a global view of signaling in the p53 network by coupling the encoding and decoding processes. We discussed the significance of modeling in revealing the mechanisms of the transition between p53 dynamic modes. Moreover, we shed light on the crosstalk between the p53 network and other signaling networks. This review may advance the understanding of operating principles of the p53 signaling network comprehensively and provide insights into p53 dynamics-based cancer therapy.

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50

Peng, Hong, Jingwen Yan, Ying Yu, and Yaozhi Luo. "Structural Surrogate Model and Dynamic Response Prediction with Consideration of Temporal and Spatial Evolution: An Encoder–Decoder ConvLSTM Network." International Journal of Structural Stability and Dynamics 21, no. 10 (June 21, 2021): 2150140. http://dx.doi.org/10.1142/s0219455421501406.

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In this paper, a new deep learning framework named encoding convolution long short-term memory (encoding ConvLSTM) is proposed to build a surrogate structural model with spatiotemporal evolution of structure, estimate the structural spatiotemporal state and predict the dynamic response under similar future dynamic load conditions. The main work of this study includes: (a) The spatiotemporal response tensor database is developed using discrete-time history data of structural dynamic response. (b) As an extension of LSTM, convolution operation is combined with LSTM network to construct structural surrogate model from the spatiotemporal evolution structural performance. (c) To enhance the anti-interference ability of structural surrogate models, a new three-layer encoding layer is added for denoising autoencoders of the hybrid network. The influence of building types and input noise on the accuracy and antinoise performance of the surrogate models are analyzed through the dynamic response prediction of a frame-shear wall, a cylindrical, and a spherical reticulated shell structure. As a testbed for the proposed network, a case study is performed on a laboratory stadium structure. The results demonstrate that the developed surrogate model can predict the structural dynamic response precisely with more under 30% noise interference.

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