A Large-Scale Model of the Functioning Brain

Ask you doctor to relate your damage to this model and where the differences/damage are located.
Then on to what the protocols are to recover from those damages.
Diagrams at the link.

http://ompldr.org/vZ2kzbw/Science-2012-Eliasmith-1202.pdf
Large-scale neural simulations are becoming
increasingly common [see (1) for a
review]. These include the ambitious Blue
Brain Project (2), which has simulated about
1 million neurons in cortical columns and includes
considerable biological detail, accurately reflecting
spatial structure, connectivity statistics, and
other neural properties. More recent work has simulated
many more neurons, such as the 1 billion
neurons simulated in the Cognitive Computation
Project (3), which has been hailed as a catscale
simulation. A human-scale simulation of
100 billion neurons has also been reported (4).
Although impressive scaling has been achieved,
no previous large-scale spiking neuron models have
demonstrated how such simulations connect to a
variety of specific observable behaviors. The focus
of this past work has been on scaling to larger numbers
of neurons and more detailed neuron models.
Unfortunately, simulating a complex brain alone
does not address one of the central challenges for
neuroscience: explaining how complex brain activity
generates complex behavior. In contrast,we present
here a spiking neuron model of 2.5 million neurons
that is centrally directed to bridging the brainbehavior
gap. Ourmodel embodies neuroanatomical
and neurophysiological constraints, making it directly
comparable to neural data at many levels of
analysis. Critically, the model can perform a wide
variety of behaviorally relevant functions.We show
results on eight different tasks that are performed
by the same model, without modification.
All inputs to the model are 28 by 28 images of
handwritten or typed characters. All outputs are
the movements of a physically modeled arm
that has mass, length, inertia, etc. For convenience,
we refer to the model as “Spaun” (Semantic Pointer
Architecture Unified Network) (see Fig. 1 and
supplementarymaterials andmethods section S1.1).
Many of the tasks we have chosen are the subject of
extensive modeling in their own right [e.g., image
recognition (5, 6), serial working memory (WM)
(7, 8), and reinforcement learning (RL) (9, 10)],
and others demonstrate abilities that are rare for
neural network research and have not yet been demonstrated
in spiking networks (e.g., counting, question
answering, rapid variable creation, and fluid reasoning).
The eight tasks (termed “A0” to “A7”) that
Spaun performs are: (A0) Copy drawing. Given a
randomly chosen handwritten digit, Spaun should
produce the same digit written in the same style
as the handwriting (movie S1; all supplemental
movies can be viewed at http://nengo.ca/build-abrain/
spaunvideos). (A1) Image recognition. Given
a randomly chosen handwritten digit, Spaun should
produce the same digit written in its default writing
(movie S2). (A2) RL. Spaun should perform
a three-armed bandit task, in which it must determine
which of three possible choices generates the
greatest stochastically generated reward. Reward
contingencies can change from trial to trial (movie
S3). (A3) Serial WM. Given a list of any length,
Spaun should reproduce it (movie S4). (A4) Counting.
Given a starting value and a count value, Spaun
should write the final value (that is, the sum of the
two values) (movie S5). (A5) Question answering.
Given a list of numbers, Spaun should answer
either one of two possible questions: (i) what is in
a given position in the list? (a “P” question) or (ii)
given a kind of number, at what position is this
number in the list? (a “K” question) (movie S6).
(A6) Rapid variable creation. Given example syntactic
input/output patterns (e.g., 0 0 7 4→7 4;
0024→2 4; etc.), Spaun should complete a novel
pattern given only the input (e.g., 0 0 1 4 → ?)
(movie S7). (A7) Fluid reasoning. Spaun should
perform a syntactic or semantic reasoning task
that is isomorphic to the induction problems from
the Raven’s Progressive Matrices (RPM) test for
fluid intelligence (11). This task requires completing
patterns of the form: 1 2 3; 5 6 7; 3 4 ? (movie S8).
Each input image is shown for 150 ms and separated
by a 150-ms blank (see table S2 for example inputs
for each task). The model is told what the task will
be by showing it an “A” and the number of the task
(0 to 7). The model is then shown input defining
the task (see Figs. 2 and 3 for examples). Spaun is
robust to invalid input (fig. S10) and performs
tasks in any order without modeler intervention.
Figure 1A shows the anatomical architecture
of the model. Connectivity and functional ascriptions
to brain areas in Spaun are consistent with
current empirical evidence (table S1). In general,
we modeled neuron and synaptic response properties
on the electrophysiology literature for the
relevant anatomical areas. For instance, the basal
ganglia have largely GABAergic neurons, with
dopamine modulating learning in the striatum,
and the cortex has largely N-methyl-D-aspartate and
AMPA synaptic connections (supplementary section
S1.3). As a result, the dynamics in the model
are tightly constrained by underlying neural properties
(see supplementary section S2.4).
The functional architecture of the model is described
in Fig. 1B. The network implementing the
Spaunmodel consists of three compression hierarchies,
an action-selection mechanism, and five subsystems.
Components of the model communicate
using spiking neurons that implement neural representations
that we call “semantic pointers,” using
various firing patterns. Semantic pointers can be
understood as being elements of a compressed
neural vector space (supplementary sections S1.1
and S1.2). Compression is a natural way to understandmuch
of neural processing. For instance, the
number of cells in the visual hierarchy gradually
decreases from the primary visual cortex (V1) to the
inferior temporal cortex (IT) (12), meaning that the
information has been compressed from a higherdimensional
(image-based) space into a lowerdimensional
(feature) space (supplementary section
S1.3). This same kind of operationmaps well to the
motor hierarchy (13),where lower-dimensional firing
patterns are successively decompressed (for example,
when a lower-dimensional motor representation
in Euclidean space moves down the motor
hierarchy to higher-dimensional muscle space).
Compression is functionally important because
low-dimensional representations can be more efficiently
manipulated for a variety of neural computations.
Consequently, learning or defining different
compression/decompression operations provides a
means of generating neural representations that
are well suited to a variety of neural computations.
The specific compression hierarchies in Spaun are
(see Fig. 1B): (i) a visual hierarchy, which compresses
image input into lower-dimensional firing
patterns; (ii) a motor hierarchy that decompresses
firing patterns in a low-dimensional space to drive
a simulated arm; and (iii) aWM, which constructs
compressed firing patterns to store serial position
information. TheWMsubsystem includes several
subcomponents that provide stable representations
of intermediate task states, task subgoals, and context.
Spaun’s action-selection mechanism is based
on a spiking basal ganglia model that we have
developed in other work (14) but is here extended
to process higher-dimensional neural representations.
The basal ganglia determine which state
the network should be in, switching as appropriate
for the current task goals. Consequently,
the model’s functional states are not hardwired,
as the basal ganglia are able to control the order
of operations by changing information flow between
subsystems of the architecture.