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Fig. 1. A model of limbic system proposed in this paper. Neurons in cortex, hippocampus and
amygdala are expressed with dark, gray and white dots respectively. Signals are expressed with
arrow lines.
second layer of cortex, dentate gyrus, the third layer of hippocampus and the first
layer of hippocampus respectively. The signal flow of the hippocampus-neocortex
model is showed with arrows, i.e.: input stimuli (Input layer) → sensory memory
(CX1) → short-term memory (CX2) → intermediate memory (DG) → Hebbian learn-
ing and chaotic processing of storage and recollection (CA3) → decoding (CA1) →
long-term memory (CX2). The output of CA3, which is a result of chaotic memory
processing, projects to Amygdala and the output of Amygdala is input to CA3 to
realize chaotic state control of MCNN in CA3.
The dynamics of association cortex is given as following.
1
L
excitatory
I
=
.
(1)
i
0
L
inhibitory
cx
i
1
x
(
t
)
=
I
(
t
)
.
(2)
i
N
j
cx
i
2
cx
ij
2
cx
2
cx
j
2
x
(
t
)
=
f
(
w
x
(
t
1
)
+
=
0
(3)
cx
2
cx
1
cx
i
1
cx
2
ca
1
ca
i
1
cx
w
x
(
t
)
+
w
x
(
t
)
θ
)
.
is the output value of the i th neuron in the Input layer of association
Where
I i
( t
)
x cx
i
1
(
t
)
x cx
i
2
(
t
)
are the output value of the i th neuron in CX1 and CX2
cortex,
and
cx
ij
2 cx
2
denotes the weight of connection (variable) between the j th
w
respectively,
c w denote the
weights of connections (fixed) between layers of CX1 and CX2, CX2 and CA1 re-
spectively,
cx
2 cx
1
2 ca
1
neuron (output) and the i th neuron in the CX2 layer,
w
x ca
1
is the output of the i th neuron in CA1,
cx
(
t
)
θ
is a threshold value
i
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