Chapter 6 Architecture and Equilibria 6.2 Global Equi libra:convergence and stability Equilibrium is steady state Convergence is synaptic equilibrium.M=0 6.1 Stability is neuronal equilibrium. X=0 6.2 More generally neural signals reach steady state even though the activations still change.We denote steady state in the neuronal field F =0 6.3 Neuron fluctuate faster than synapses fluctuate Stability-Convergence dilemma The synapsed slowly encode these neural patterns being learned;but when the synapsed change ,this tends 2004d ihdo the stable neuronal patterns2004.11.10 7 Chapter 6 Architecture and Equilibria 6.2 Global Equilibra:convergence and stability Equilibrium is steady state . Convergence is synaptic equilibrium. Stability is neuronal equilibrium. More generally neural signals reach steady state even though the activations still change.We denote steady state in the neuronal field Neuron fluctuate faster than synapses fluctuate. Stability - Convergence dilemma : The synapsed slowly encode these neural patterns being learned; but when the synapsed change ,this tends to undo the stable neuronal patterns. M = 0 6.1 • X = 0 6.2 • Fx Fx = 0 6.3 •