Meaning of the input Input can represent the magnitude of directly experiment sensory information or directly apply control information. The input changes slowly,and can be assumed constant value
Meaning of the input Input can represent the magnitude of directly experiment sensory information or directly apply control information. The input changes slowly,and can be assumed constant value
3.2 ADDITIVE NEURONAL FEEDBACK Neurons do not compute alone.Neuron modify their state activations with external input and with the feedback from one another. This feedback takes the form of path-weighted signals from synaptically connected neurons
Neurons do not compute alone. Neuron modify their state activations with external input and with the feedback from one another. 3.2 ADDITIVE NEURONAL FEEDBACK This feedback takes the form of path-weighted signals from synaptically connected neurons
Synaptic Connection Matrices n neurons in field Fx p neurons in field Fy The ith neuron axon in Fx-a synapse mi →jth neurons in Fy mii is constant,can be positive,negative or zero
Synaptic Connection Matrices n neurons in field p neurons in field FX FY The ith neuron axon in a synapse jth neurons in mij mij is constant,can be positive,negative or zero. FX FY
Meaning of connection matrix The synaptic matrix or connection matrix M is an n-by-p matrix of real number whose entries are the synaptic efficacies.mii the ijth synapse is excitatory if mi>0 inhibitory if mii< The matrix M describes the forward projections from neuron field Fx to neuron field Fy The matrix N describes the backward projections from neuron field Fy to neuron field Fx
Meaning of connection matrix The synaptic matrix or connection matrix M is an n-by-p matrix of real number whose entries are the synaptic efficacies. the ijth synapse is excitatory if inhibitory if m 0 ij m 0 ij mij The matrix M describes the forward projections from neuron field to neuron field FX FY The matrix N describes the backward projections from neuron field to neuron field FY FX
Bidirectional and Unidirectional connection Topologies Bidirectional networks M and N have the same or approximately the same structure.N=MT M=NT Unidirectional network A neuron field synaptically intraconnects to itself.M nxn. BAM M is symmetric, M=MI the unidirectional network is BAM 2002.10.8
2002.10.8 Bidirectional and Unidirectional connection Topologies Bidirectional networks M and N have the same or approximately the same structure. Unidirectional network T M = N M T N = A neuron field synaptically intraconnects to itself.M nxn. BAM M is symmetric, the unidirectional network is BAM M M T =