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Domain-Specific Multi-Level IR Rewriting for GPU: The Open Earth Compiler for GPU-accelerated Climate Simulation ...
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On Turing Machines Deciding According to the Shortest Computations
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In: Axioms ; Volume 10 ; Issue 4 (2021)
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Receptive field transformations of the optimal HSNN predicts transformations observed along the ascending auditory pathway.
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Abstract:
( a ) Example spectro-temporal receptive field (STRF) measured for the optimal network change systematically between consecutive network layers. All STRFs are normalized to the same color scale (red = increase in activity or excitation; blue = decrease in activity or inhibition/suppression; green tones = lack of activity). In the early network layers STRFs are relatively fast with short duration and latencies, and relatively narrowly tuned. STRFs become progressively slower, slightly broader, and have longer patterns of inhibition across the network layers, mirroring changes in spectral and temporal selectivity observed in the ascending auditory pathway. The measured ( b ) integration times, ( c ) latencies, and ( d ) bandwidths increase across the six network layers. ( e ) Examples STRFs from the auditory nerve (AN) [ 26 ], inferior colliculus (IC) [ 7 ], thalamus (MGB) and primary auditory cortex (A1) [ 8 ] become progressively longer and have progressively more complex spectro-temporal sensitivity along the ascending auditory pathway. Average integration times ( f ), latencies ( g ) and bandwidths ( h ) between AN and A1 follow similar trends as the optimal HSNN ( b - d ). Asterisks (*) designate significant comparisons (t-test with Bonferroni correction, p<0.01) relative to layer 1 for the optimal network ( b - d ) or relative to the auditory nerve for the neural data ( f - h ) while error bars designate SD.
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Keyword:
auditory pathway; auditory pathway hierarchy; auditory pathway organization; auditory system hierarchy; Biological Sciences not elsewhere classified; HSNN; HSSN; Infectious Diseases; Information Systems not elsewhere classified; Neuroscience; performance; Science Policy; sequential nonlinear feature extraction computations; Spiking network optimized; word recognition accuracy; word recognition task
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URL: https://doi.org/10.1371/journal.pcbi.1007558.g003
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Linguistic Knowledge and Unconscious Computations
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In: Rivista internazionale di Filosofia e Psicologia; V. 7, N. 3 (2016); 338-349 ; 2239-2629 ; 2039-4667 (2016)
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| 5 |
Integrated Cognitive-neuroscience Architectures for Understanding Sensemaking (ICArUS): A Computational Basis for ICArUS Challenge Problem Design
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In: DTIC (2014)
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| 6 |
What artificial grammar learning reveals about the neurobiology of syntax
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| 7 |
Quantified boolean formulae : formal processings and parallel computations. ; Formules booléennes quantifiées : transformations formelles et calculs parallèles
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In: https://tel.archives-ouvertes.fr/tel-00578083 ; Informatique [cs]. Université d'Angers, 2010. Français (2010)
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| 8 |
Entity Profiling for Intelligence Using the Graphical Overview of Social and Semantic Interactions of People (GOSSIP) Software Tool
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In: DTIC (2010)
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| 9 |
Teaching Intelligence Analysis with TIACRITIS
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In: DTIC (2010)
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| 10 |
The Temporal Morphology of Infrasound Propagation
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In: DTIC (2010)
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| 11 |
The Use of Systemic-Functional Linguistics in Automated Text Mining
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In: DTIC (2009)
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| 12 |
Integrating Language and Cognition in Grounded Adaptive Agents
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In: DTIC (2008)
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| 13 |
Support for the Annual Meeting (30th) of the Cognitive Science Society
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In: DTIC (2008)
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| 14 |
Plenty of Blame to Go Around: A Qualitative Approach to Attribution of Moral Responsibility
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In: DTIC (2007)
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| 16 |
Evaluating a Computational Model of Social Causality and Responsibility
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In: DTIC (2006)
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| 17 |
Verbale Selektion von räumlichen Objekten - Ein benutzerorientierter Ansatz für mobile Systeme auf Basis von Ontologien ...
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| 19 |
A Programming Language for Probabilistic Computation
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In: DTIC (2005)
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| 20 |
Joint Parsing and Semantic Role Labeling
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In: DTIC AND NTIS (2005)
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