N. A. Cayco-Gajic

Cellular and Molecular Neuroscience · 2017–2019

A researcher in the Science corpus: 2 papers; trusts 135 researchers and is trusted by 5 researchers here, as inferred from the citation graph.

Subfields

• Cellular and Molecular Neuroscience — 169 citations into it
• Cognitive Neuroscience — 152 citations into it; #9,509 of 10,852 by trust here, #8,899 by citations
• Neurology — 86 citations into it; #1,182 of 1,455 by trust here, #1,234 by citations
• Sensory Systems — 79 citations into it
• Developmental Neuroscience — 24 citations into it
• Electrical and Electronic Engineering — 24 citations into it; #9,175 of 16,539 by trust here, #15,217 by citations
• Genetics — 21 citations into it
• Neuroscience — 17 citations into it; #314 of 487 by trust here, #466 by citations

Whom N. A. Cayco-Gajic trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Gilles Laurent
2. Glenn C. Turner
3. Rainer W. Friedrich
4. Henrik Jörntell
5. D. Marr
6. Peter Földiák
7. Masao Ito
8. Thomas M. Cover
9. S. Farris
10. Paul Chadderton

Who trusts N. A. Cayco-Gajic overall

1. Liyuan Wang
2. R. Silver
3. Xingxing Zhang
4. Mehrdad Jazayeri
5. M. Modi
6. Hang Su

Papers from N. A. Cayco-Gajic's perspective

Ordered by how strongly the researchers N. A. Cayco-Gajic trusts endorse each paper. Same corpus, a different order for every researcher.

1. A theory of cerebellar cortex
2. Pattern Separation in the Dentate Gyrus and CA3 of the Hippocampus
3. Integration of quanta in cerebellar granule cells during sensory processing
4. Oscillations and Sparsening of Odor Representations in the Mushroom Body
5. A Theory of Cerebellar Function
6. Sparse coding of sensory inputs.
7. Olfactory representations by Drosophila mushroom body neurons.
8. The Cerebellum as a Neuronal Machine
9. Olfactory network dynamics and the coding of multidimensional signals
10. Development of a tonic form of synaptic inhibition in rat cerebellar granule cells resulting from persistent activation of GABAA receptors.

Their papers

• Re-evaluating circuit mechanisms underlying pattern separation (2019) — 224 citations
• Sparse synaptic connectivity is required for decorrelation and pattern separation in feedforward networks (2017) — 153 citations