Computational Intelligence and Neuroscience / 2011 / Article / Alg 1

Research Article

Lateral Information Processing by Spiking Neurons: A Theoretical Model of the Neural Correlate of Consciousness

Algorithm 1

Algorithm which updates the state variables of neuron 𝑖 from one time step to the next.
(1) π‘œ 𝑖 ∢ = ( 1 βˆ’ 𝛼 π‘œ ) π‘œ 𝑖 // output decay
(2) π‘Ž 𝑖 ∢ = ( 1 βˆ’ 𝛼 π‘Ž ) π‘Ž 𝑖 // activation decay
(3) π‘Ž 𝑖 ∢ = π‘Ž 𝑖 + 𝛼 π‘Ž βˆ‘ 𝑗 𝑀 𝑖 𝑗 π‘œ 𝑗 // sum over all inputs
(4) // average activation across open gap
(5) // junctions (reconfigurable resistive grid)
(6) π‘Ž 𝑖 ∢ = a v g ( { π‘Ž 𝑖 } βˆͺ { π‘Ž 𝑗 ∣ N e u r o n 𝑗 is connected to
(7) non-refracting neuron 𝑖 via open gap junction;
(8) π‘Ž 𝑗 is then set to average π‘Ž 𝑖 } )
(9) π‘Ž 𝑖 ∢ = m a x [ βˆ’ 1 , π‘Ž 𝑖 ] // limit activation
(10) // reduce threshold based on size of sub-network
(11) 𝑑 𝑖 ∢ = m a x [ 0 , 1 βˆ’ 𝛾 β‹… 𝑁 𝑠 ]
(12) i f ( π‘Ž 𝑖 > 𝑑 𝑖 ) { // neuron fires if above firing threshold
(13) π‘Ž 𝑖 = 0 // activation is reduced to 0
(14) π‘œ 𝑖 = 1 βˆ’ 𝑁 𝑛 πœ– // output rises to 1
(15) // some activation is distributed to conn. neurons
(16) if ( 𝑗 is connected to 𝑖 via open gap junction)
(17) π‘Ž 𝑗 ∢ = π‘Ž 𝑗 + πœ–
(18) }
(19) // temporal averaging of own output
(20) Μƒ π‘Ž 𝑖 = ( 1 βˆ’ 𝛼 𝑑 ) Μƒ π‘Ž 𝑖 + 𝛼 𝑑 π‘œ 𝑖
(21) // spatial averaging of temporal average
(22) π‘Ž ξ…ž 𝑖 = 1 / ( 1 + 𝑁 𝑛 ) ( π‘Ž 𝑖 + βˆ‘ 𝑗 π‘Ž 𝑗 )
(23) π‘Ž 𝑖 = ( 1 βˆ’ 𝛼 𝑠 ) π‘Ž ξ…ž 𝑖 + 𝛼 𝑠 Μƒ π‘Ž 𝑖
(24) // check if temporal average is above sync-threshold
(25) i f ( Μƒ π‘Ž 𝑖 > π‘Ž 𝑖 )
(26) open gap junctions
(27) else
(28) close gap junctions

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