FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4310263760> ?p ?o ?g. }

• W4310263760 abstract "Spiking neural networks were introduced to understand spatiotemporal information processing in neurons and have found their application in pattern encoding, data discrimination, and classification. Bioinspired network architectures are considered for event-driven tasks, and scientists have looked at different theories based on the architecture and functioning. Motor tasks, for example, have networks inspired by cerebellar architecture where the granular layer recodes sparse representations of the mossy fiber (MF) inputs and has more roles in motor learning. Using abstractions from cerebellar connections and learning rules of deep learning network (DLN), patterns were discriminated within datasets, and the same algorithm was used for trajectory optimization. In the current work, a cerebellum-inspired spiking neural network with dynamics of cerebellar neurons and learning mechanisms attributed to the granular layer, Purkinje cell (PC) layer, and cerebellar nuclei interconnected by excitatory and inhibitory synapses was implemented. The model's pattern discrimination capability was tested for two tasks on standard machine learning (ML) datasets and on following a trajectory of a low-cost sensor-free robotic articulator. Tuned for supervised learning, the pattern classification capability of the cerebellum-inspired network algorithm has produced more generalized models than data-specific precision models on smaller training datasets. The model showed an accuracy of 72%, which was comparable to standard ML algorithms, such as MLP (78%), Dl4jMlpClassifier (64%), RBFNetwork (71.4%), and libSVM-linear (85.7%). The cerebellar model increased the network's capability and decreased storage, augmenting faster computations. Additionally, the network model could also implicitly reconstruct the trajectory of a 6-degree of freedom (DOF) robotic arm with a low error rate by reconstructing the kinematic parameters. The variability between the actual and predicted trajectory points was noted to be ± 3 cm (while moving to a position in a cuboid space of 25 × 30 × 40 cm). Although a few known learning rules were implemented among known types of plasticity in the cerebellum, the network model showed a generalized processing capability for a range of signals, modulating the data through the interconnected neural populations. In addition to potential use on sensor-free or feed-forward based controllers for robotic arms and as a generalized pattern classification algorithm, this model adds implications to motor learning theory." @default.
• W4310263760 created "2022-11-30" @default.
• W4310263760 creator A5063781474 @default.
• W4310263760 creator A5066357721 @default.
• W4310263760 date "2022-11-28" @default.
• W4310263760 modified "2023-09-26" @default.
• W4310263760 title "A cerebellum inspired spiking neural network as a multi-model for pattern classification and robotic trajectory prediction" @default.
• W4310263760 cites W1523232908 @default.
• W4310263760 cites W1550824444 @default.
• W4310263760 cites W1582813140 @default.
• W4310263760 cites W1585139495 @default.
• W4310263760 cites W1611348645 @default.
• W4310263760 cites W1645800954 @default.
• W4310263760 cites W1760258693 @default.
• W4310263760 cites W1857448332 @default.
• W4310263760 cites W1883557202 @default.
• W4310263760 cites W188692684 @default.
• W4310263760 cites W1938385190 @default.
• W4310263760 cites W1964606125 @default.
• W4310263760 cites W1964892106 @default.
• W4310263760 cites W1965335952 @default.
• W4310263760 cites W1966104725 @default.
• W4310263760 cites W1967751939 @default.
• W4310263760 cites W1969588561 @default.
• W4310263760 cites W1972466164 @default.
• W4310263760 cites W1974765629 @default.
• W4310263760 cites W1976619609 @default.
• W4310263760 cites W1978847978 @default.
• W4310263760 cites W1979096535 @default.
• W4310263760 cites W1983329616 @default.
• W4310263760 cites W1983762683 @default.
• W4310263760 cites W1987501357 @default.
• W4310263760 cites W1993533483 @default.
• W4310263760 cites W1993740947 @default.
• W4310263760 cites W1994781353 @default.
• W4310263760 cites W2010098657 @default.
• W4310263760 cites W2012592267 @default.
• W4310263760 cites W2014815532 @default.
• W4310263760 cites W2015517344 @default.
• W4310263760 cites W2022737586 @default.
• W4310263760 cites W2023459285 @default.
• W4310263760 cites W2025668498 @default.
• W4310263760 cites W2026193431 @default.
• W4310263760 cites W2027166702 @default.
• W4310263760 cites W2032943627 @default.
• W4310263760 cites W2036244090 @default.
• W4310263760 cites W2038897596 @default.
• W4310263760 cites W2040715937 @default.
• W4310263760 cites W2041507602 @default.
• W4310263760 cites W2042013578 @default.
• W4310263760 cites W2043699248 @default.
• W4310263760 cites W2047433628 @default.
• W4310263760 cites W2049799368 @default.
• W4310263760 cites W2052280289 @default.
• W4310263760 cites W2054140453 @default.
• W4310263760 cites W2055850746 @default.
• W4310263760 cites W2060052647 @default.
• W4310263760 cites W2060054159 @default.
• W4310263760 cites W2064573518 @default.
• W4310263760 cites W2065528657 @default.
• W4310263760 cites W2065575763 @default.
• W4310263760 cites W2074847031 @default.
• W4310263760 cites W2076273063 @default.
• W4310263760 cites W2076845962 @default.
• W4310263760 cites W2079668152 @default.
• W4310263760 cites W2088505072 @default.
• W4310263760 cites W2091727926 @default.
• W4310263760 cites W2097861969 @default.
• W4310263760 cites W2100393076 @default.
• W4310263760 cites W2101691260 @default.
• W4310263760 cites W2103019454 @default.
• W4310263760 cites W2103617625 @default.
• W4310263760 cites W2106279406 @default.
• W4310263760 cites W2112666468 @default.
• W4310263760 cites W2114414717 @default.
• W4310263760 cites W2117702366 @default.
• W4310263760 cites W2121458485 @default.
• W4310263760 cites W2123612003 @default.
• W4310263760 cites W2123742740 @default.
• W4310263760 cites W2128589492 @default.
• W4310263760 cites W2130753066 @default.
• W4310263760 cites W2132494021 @default.
• W4310263760 cites W2134172496 @default.
• W4310263760 cites W2138131004 @default.
• W4310263760 cites W2148450540 @default.
• W4310263760 cites W2155051950 @default.
• W4310263760 cites W2156430261 @default.
• W4310263760 cites W2157580187 @default.
• W4310263760 cites W2159639631 @default.
• W4310263760 cites W2165095254 @default.
• W4310263760 cites W2167049598 @default.
• W4310263760 cites W2168096604 @default.
• W4310263760 cites W2168529864 @default.
• W4310263760 cites W2168596899 @default.
• W4310263760 cites W2168681596 @default.
• W4310263760 cites W2168828075 @default.
• W4310263760 cites W2168851095 @default.
• W4310263760 cites W2171236529 @default.
• W4310263760 cites W2220734730 @default.
• W4310263760 cites W2274405424 @default.

• next