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Connection

Hava Siegelmann to Neurons

This is a "connection" page, showing publications Hava Siegelmann has written about Neurons.
Connection Strength

1.501
  1. Amgalan A, Taylor P, Mujica-Parodi LR, Siegelmann HT. Unique scales preserve self-similar integrate-and-fire functionality of neuronal clusters. Sci Rep. 2021 03 05; 11(1):5331.
    View in: PubMed
    Score: 0.468
  2. Tal A, Peled N, Siegelmann HT. Biologically inspired load balancing mechanism in neocortical competitive learning. Front Neural Circuits. 2014; 8:18.
    View in: PubMed
    Score: 0.289
  3. Cabessa J, Siegelmann HT. The computational power of interactive recurrent neural networks. Neural Comput. 2012 Apr; 24(4):996-1019.
    View in: PubMed
    Score: 0.249
  4. Siegelmann HT, Holzman LE. Neuronal integration of dynamic sources: Bayesian learning and Bayesian inference. Chaos. 2010 Sep; 20(3):037112.
    View in: PubMed
    Score: 0.226
  5. Lipson H, Siegelmann HT. Clustering irregular shapes using high-order neurons. Neural Comput. 2000 Oct; 12(10):2331-53.
    View in: PubMed
    Score: 0.114
  6. Saunders DJ, Patel D, Hazan H, Siegelmann HT, Kozma R. Locally connected spiking neural networks for unsupervised feature learning. Neural Netw. 2019 Nov; 119:332-340.
    View in: PubMed
    Score: 0.105
  7. Pietrzykowski AZ, Friesen RM, Martin GE, Puig SI, Nowak CL, Wynne PM, Siegelmann HT, Treistman SN. Posttranscriptional regulation of BK channel splice variant stability by miR-9 underlies neuroadaptation to alcohol. Neuron. 2008 Jul 31; 59(2):274-87.
    View in: PubMed
    Score: 0.049
Connection Strength

The connection strength for concepts is the sum of the scores for each matching publication.

Publication scores are based on many factors, including how long ago they were written and whether the person is a first or senior author.