<s>
A	O
layer	O
in	O
a	O
deep	B-Algorithm
learning	I-Algorithm
model	O
is	O
a	O
structure	O
or	O
network	B-Architecture
topology	I-Architecture
in	O
the	O
model	O
's	O
architecture	O
,	O
which	O
takes	O
information	O
from	O
the	O
previous	O
layers	O
and	O
then	O
passes	O
it	O
to	O
the	O
next	O
layer	O
.	O
</s>
<s>
There	O
are	O
several	O
famous	O
layers	O
in	O
deep	B-Algorithm
learning	I-Algorithm
,	O
namely	O
convolutional	O
layer	O
and	O
maximum	O
pooling	O
layer	O
in	O
the	O
convolutional	B-Architecture
neural	I-Architecture
network	I-Architecture
,	O
fully	O
connected	O
layer	O
and	O
ReLU	B-Algorithm
layer	O
in	O
vanilla	O
neural	O
network	O
,	O
RNN	B-Algorithm
layer	O
in	O
the	O
RNN	B-Algorithm
model	O
and	O
deconvolutional	O
layer	O
in	O
autoencoder	B-Algorithm
etc	O
.	O
</s>
<s>
There	O
is	O
an	O
intrinsic	O
difference	O
between	O
deep	B-Algorithm
learning	I-Algorithm
layering	O
and	O
neocortical	O
layering	O
:	O
deep	B-Algorithm
learning	I-Algorithm
layering	O
depends	O
on	O
network	B-Architecture
topology	I-Architecture
,	O
while	O
neocortical	O
layering	O
depends	O
on	O
intra-layers	O
homogeneity	B-General_Concept
.	O
</s>
