Neural Network Modeling: Statistical Mechanics and Cybernetic PerspectivesCRC Press, 12.7.1994 - 256 sivua Neural Network Modeling offers a cohesive approach to the statistical mechanics and principles of cybernetics as a basis for neural network modeling. It brings together neurobiologists and the engineers who design intelligent automata to understand the physics of collective behavior pertinent to neural elements and the self-control aspects of neurocybernetics. The theoretical perspectives and explanatory projections portray the most current information in the field, some of which counters certain conventional concepts in the visualization of neuronal interactions. |
Sisältö
Neural and Brain Complex | 15 |
Concepts of Mathematical Neurobiology | 31 |
4 | 42 |
4 | 54 |
4 | 78 |
6 | 87 |
9 | 94 |
Neural Field Theory Quasiparticle Dynamics | 130 |
Frustration | 203 |
Partition Function | 204 |
Total Energy | 206 |
Sigmoidal Function | 207 |
Free Energy | 208 |
Matrix Methods in Littles Model | 210 |
Persistent States and Occurrence of Degeneracy in the Maximum Eigenvalue of the Transition Matrix | 215 |
Diagonalizability of the Characteristic Matrix | 217 |
Informatic Aspects of Neurocybernetics | 160 |
Magnetism and the Ising SpinGlass Model | 196 |
Bonds and Sites | 201 |
The Hard Spin | 202 |
Overlap of Replicas and Replica Symmetry Ansatz | 220 |
| 222 | |
| 233 | |
Muita painoksia - Näytä kaikki
Neural Network Modeling: Statistical Mechanics and Cybernetic Perspectives P. S. Neelakanta,Dolores DeGroff Rajoitettu esikatselu - 2018 |
Neural Network Modeling: Statistical Mechanics and Cybernetic Perspectives P. S. Neelakanta,Dolores DeGroff Rajoitettu esikatselu - 2018 |
Yleiset termit ja lausekkeet
action potential algorithm analogy annealing artificial neural networks aspects associative memory average axon behavior biological Boltzmann machine cellular collective computational concepts configuration considerations considered correlation corresponding cybernetics defined depicting dichotomous dipole disorganization distribution dynamics eigenvalues eigenvectors energy function entropy Equation equilibrium excitatory extensive quantity external ferromagnetic Figure finite firing given Hamiltonian Hence Hopfield information processing inhibitory input interaction intracellular Ising model Ising spin large number lattice learning Little's model long-range order magnetic spin mathematical matrix McCulloch-Pitts membrane neural activity neural assembly neural complex neural field theory neural network neural system neurocybernetic neuronal transmission noise nonlinear objective function output parameter partition function patterns Peretto pertinent physical postsynaptic probability random real neurons refers relation relevant represents S₁ self-organizing sigmoidal sigmoidal function simulated annealing spatial specified spin system state-transition statistical mechanics stochastical subsets symmetric synaptic temperature theory thermodynamic threshold transition units variable vector wave function weights
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