Abstract
Inhibitory neural networks are found to encode high volumes of information through delayed inhibition. We show that inhibition delay increases storage capacity through a Stirling transform of the minimum capacity which stabilizes locally coherent oscillations. We obtain both the exact and asymptotic formulas for the total number of dynamic attractors. Our results predict a (ln2)-N-fold increase in capacity for an N-neuron network and demonstrate high-density associative memories which host a maximum number of oscillations in analog neural devices.
| Language | English |
|---|---|
| Article number | 030301(R) |
| Pages | 1-4 |
| Number of pages | 4 |
| Journal | Physical Review E |
| Volume | 97 |
| Issue number | 3 |
| Early online date | 9 Mar 2018 |
| DOIs | |
| Status | Published - 31 Mar 2018 |
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ASJC Scopus subject areas
- Statistical and Nonlinear Physics
- Statistics and Probability
- Condensed Matter Physics
Cite this
Inhibition Delay Increases Neural Network Capacity through Stirling Transform. / Nogaret, Alain; King, Alastair.
In: Physical Review E, Vol. 97, No. 3, 030301(R), 31.03.2018, p. 1-4.Research output: Contribution to journal › Article
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TY - JOUR
T1 - Inhibition Delay Increases Neural Network Capacity through Stirling Transform
AU - Nogaret, Alain
AU - King, Alastair
PY - 2018/3/31
Y1 - 2018/3/31
N2 - Inhibitory neural networks are found to encode high volumes of information through delayed inhibition. We show that inhibition delay increases storage capacity through a Stirling transform of the minimum capacity which stabilizes locally coherent oscillations. We obtain both the exact and asymptotic formulas for the total number of dynamic attractors. Our results predict a (ln2)-N-fold increase in capacity for an N-neuron network and demonstrate high-density associative memories which host a maximum number of oscillations in analog neural devices.
AB - Inhibitory neural networks are found to encode high volumes of information through delayed inhibition. We show that inhibition delay increases storage capacity through a Stirling transform of the minimum capacity which stabilizes locally coherent oscillations. We obtain both the exact and asymptotic formulas for the total number of dynamic attractors. Our results predict a (ln2)-N-fold increase in capacity for an N-neuron network and demonstrate high-density associative memories which host a maximum number of oscillations in analog neural devices.
UR - http://www.scopus.com/inward/record.url?scp=85044216171&partnerID=8YFLogxK
U2 - 10.1103/PhysRevE.97.030301
DO - 10.1103/PhysRevE.97.030301
M3 - Article
VL - 97
SP - 1
EP - 4
JO - Physical Review E
T2 - Physical Review E
JF - Physical Review E
SN - 1539-3755
IS - 3
M1 - 030301(R)
ER -