Bernal S D_2010.pdf - University of Plymouth

3.4. EXISmm MODELS
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Figure S.lI: Neuronal local inference circuit (LINC) implemenling the operations of a node
in a Forney fucUff graph wilh fi input nodes (K;| and 6 hidden variables {X,}.
Each LINC is built from populiilions of leaky integrate-and-fire neurons (small
red, black and dashed rectangles), which implement the two basic operations:
weighted m;iximi/,aiioii circuits {max frames) and summation circuits (I frames).
The main black frame shows ihe neuronal LINC for the variable Xj. which re*
ceives input from neighbour nodes and Xs (although due to size limitations only
projcciions frimi nodes Xi ""d X^ are shown). For each neighbour ntxle, and for
each of the three stales of Xj, a maximization node finds the maximum of the
weighted message values. Note a linear summation circuit adds the corresponding
weight (log domain) to each input message prior to the maximization step.
The set of weighted maximum results (or each slate are then combined in the
three corresponding summation circuits. The three sums are then noniialized by
a normaii/aiion circuit (green doited frame), which contains a recurrendy connected
inhibitory population (black rectangle in the centre). Tlie vector of single
valued outputs of each summation circuit represents the output message of Xj,
which will be propagated to all of its neighbours (Litvak and Ullman 2009).
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