=Paper=
{{Paper
|id=Vol-1583/CoCoNIPS_2015_paper_13
|storemode=property
|title=Combinatorial Structures and Processing in Neural Blackboard Architectures
|pdfUrl=https://ceur-ws.org/Vol-1583/CoCoNIPS_2015_paper_13.pdf
|volume=Vol-1583
|authors=Frank van der Velde,Marc de Kamps
|dblpUrl=https://dblp.org/rec/conf/nips/VeldeK15
}}
==Combinatorial Structures and Processing in Neural Blackboard Architectures==
https://ceur-ws.org/Vol-1583/CoCoNIPS_2015_paper_13.pdf
Combinatorial structures and processing in Neural
Blackboard Architectures
Frank van der Velde Marc de Kamps
CTIT, CPE-BMS Institute for Artificial Intelligence
University of Twente, Enschede and Biological Systems
IOP, Leiden University of Leeds
The Netherlands Leeds, United Kingdom
f.vandervelde@utwente.nl M.deKamps@leeds.ac.uk
Abstract
We discuss and illustrate Neural Blackboard Architectures (NBAs) as the basis for
variable binding and combinatorial processing the brain. We focus on the NBA for
sentence structure. NBAs are based on the notion that conceptual representations
are in situ, hence cannot be copied or transported. Novel combinatorial struc-
tures can be formed with these representations by embedding them in NBAs. We
discuss and illustrate the main characteristics of this form of combinatorial pro-
cessing. We also illustrate the NBA for sentence structures by simulating neural
activity as found in recently reported intracranial brain observations. Furthermore,
we will show how the NBA can account for ambiguity resolution and garden path
effects in sentence processing.
1 Introduction
We outline and illustrate the implementation and processing of combinatorial structures in Neural
Blackboard Architectures (or NBAs). First, we will discuss the ideas on which NBAs are based.
Then, we will illustrate sentence representation and processing in the NBA for sentence structure
[1]. Next, we will simulate neural activation patterns that arise in the NBA during sentence process-
ing and relate it to recently reported intracranial brain observations. Finally, we will illustrate and
discuss how the NBA handles linguistic phenomena such as unproblematic ambiguities and garden
path sentences [2].
2 In situ concept representations
The development of Neural Blackboard Architectures is based on the notion that representations
in the brain, such as representations of concepts, are ‘in situ’, in line with the neural assemblies
proposed by Hebb [3]. Figure 1 (based on [4]) illustrates this with the concept cat. As Hebb argued,
a neural assembly develops over time as neurons that process information about a concept become
interconnected. These neurons could be located in different parts of the brain, depending on the
nature of the underlying concept. So, in case of a concept like cat, they will consist of neurons in
the visual and auditory cortex, involved in the perception of cats. They could also consist of neurons
involved in actions related to cats (e.g., pronouncing the word cat).
Concept representations with neural assemblies could be distributed, but parts of the assembly could
also consist of more local representations. They could consist of neurons involved in processing
information about the concepts, but also of neurons involved in actions related to concepts. This
entails that both incoming and outgoing connections determine the nature of a concept representation
[5]. Furthermore, unlike the original assemblies as described by Hebb, concepts as intended here
1
Copyright © 2015 for this paper by its authors. Copying permitted for private and academic purposes.
paw
pet
has Sentence blackboard
is
action
Phonology blackboard
cat
emotion
Perception blackboards
Other blackboards
Figure 1: In situ concept representation (based on [4]).
are not only associative neural structures. They also incorporate relations, as illustrated with the
relations is pet and has paw in Figure 1.
The assembly or web-like structure of a concept representation entails that concepts representations
are ‘in situ’ [4]. That is, wherever a concept is activated it always consists of the activation of
the assembly of that concept or a part of it. In this view, it is not possible to make a copy of a
concept representation and store it elsewhere in the brain to, for example, create a combinatorial
structure with it or use it in a reasoning process. In this sense, in situ concept representations are
fundamentally different from symbols in symbol architectures [6]. Due to the in situ nature of
concepts representations, they are also grounded in perception or action (depending on the nature of
the concept).
The in situ nature of neuronal concept representations imposes constraints on the way they can
be combined in higher-level cognitive combinatorial structures, such as sentences. In situ concept
representation cannot be copied or transported to construct combinatorial structures. Instead, as il-
lustrated in Figure 1, they are embedded in ‘neural blackboards’ to form combinatorial structures.
We assume that there will be specific neural blackboards for specific forms of combinatorial struc-
tures, and concept representations will be embedded in a number of them depending on the nature of
that concept and the role it plays in specific combinatorial structures. For example, the concept cat
will be embedded in a phonology blackboard, forming the word cat based on familiar phonemes. It
will also be embedded in a sentence blackboard, needed to form sentence structures. It could also be
embedded in a perception blackboard underlying the combinatorial nature of visual cognition [7].
Other blackboards, e.g., needed for sequential processing or reasoning, could be included as well.
The neural blackboards allow the construction of (potentially novel) combinatorial structures based
on (familiar) in situ concept representations, using forms of variable binding (depending on the
specific blackboard). They aim to satisfy the following characteristics:
• The concept representations remain in situ (and thus grounded) even when they are a part
of (novel) combinatorial structures.
• The combinatorial structures or parts thereof are content addressable, even with novel com-
binatorial structures (of familiar constituents).
• The variable binding of concepts in blackboards creates the connection paths needed to
produce behavior (linking sensory and motor activation), even with (novel) combinatorial
structures.
We will discuss and illustrate these characteristics using the sentence blackboard.
3 Sentence neural blackboard
Figure 2 illustrates the structure of the sentence Sonnet sees (the) dog in the Neural Blackboard
Architecture for sentence structure [1]. Here, we assume that the words sees and dog are familiar
2
Sentence blackboard
Sy
Conditional
Structure assemblies
connections:
Nx Vz
W1
Nu
1 2 3
sees dog
/so/ /nn/ /et/ Word
assemblies
Phonology blackboard
Figure 2: Sentence representation in a sentence neural blackboard.
constituents but Sonnet is a new name, not heard before. The representations of sees and dog consist
of neural assembly structures (‘word assemblies’), in line with the concept cat in Figure 1. For
simplicity, we represent them with ovals, but the full connection structure is always implied.
The sentence blackboard allows the creation of a (temporal) connection structure that binds the word
assemblies in line with the relations expressed by the sentence. To this end, the NBA consists of
‘structure assemblies’ such as Nx and Nu in Figure 2 (representing nouns) and Sy (sentence) and Vz
(verb). The word assemblies can bind to structure assemblies of their kind, and structure assemblies
can bind to each other in line with the sentence structure. The binding process results from specific
‘conditional connections’ (outlined below).
As noted in [8], humans have the ability to include new words and names in (even novel) sentences
structures, exemplified here with the name Sonnet. However, the ability to include novel words in
sentences is restricted to new words based on familiar phonemes. In the NBA this ability results
from a phonology blackboard in which new names can be formed based on familiar phonemes [9].
The phonology blackboard interacts with the sentence blackboard, which allows the incorporation
of the new name in a sentence structure [10]. Here, Sonnet activates a set of phonemes (illustrated
with the pseudo phonemes so, nn, and et), which bind to a structure assembly in the phonology
blackboard (W1). In turn, this structure assembly binds to the sentence structure in the sentence
blackboard.
Conditional connections S1
Structure assemblies
n n v
N1 X
n v
i N1 V1
sees t
cat
di t
N2
Word assemblies
Control WM dog
circuit circuit
Structure assemblies: S1 = Main assemblies v = Subassemblies
Figure 3: Binding in the sentence neural blackboard.
The binding process in the NBA is illustrated in more detail in Figure 3 with the sentence cat sees
dog. The conditional connections, here the red and black thick lines, consist of gating circuits. In the
NBA, each word assembly (e.g., of a noun) is connected to a set of structure assemblies of the same
kind (all Ni assemblies in the case of a noun) with gating circuits. (The number of these connections
can be reduced by representing words in the phonology blackboard first, see [9].) In turn, each
structure assembly consists of a ‘main assembly’, such as N1, and (a set of) subassemblies, such as
n or t. The connection between a main assembly and a subassembly consists of a gating circuit as
well.
3
Structure assemblies of different kinds, such as V1 and N2, are connected by their subassemblies of
the same kind. Here, their t (theme) subassemblies, which represents the fact that a verb can have a
theme (object). This connection also consists of a gating circuit.
The gating circuits operate by disinhibition (di), illustrated in Figure 3. So, when N1 is active,
it activates a neuron (or neuron population) X and an inhibitory neuron (or population) i. The
latter inhibits X, which blocks the flow of activation. But when i itself is inhibited (by neuron or
population di) activation can flow from N1 (via X) to n.
Gating circuits can be disinhibited (or ‘activated’) in one of two different ways. In case of gating
circuits between main assemblies and subassemblies the activation results from an external control
circuit that activates the di population. This is how syntactical operations affect binding in the
blackboard. A control circuit could have recognized that sees dog represent a verb and a theme. It
then activates all di populations in the gating circuits between all Vi and Nj assemblies and their t
assemblies. As a result, the active Vi and Nj will activate their t subassembly.
Gating circuits between subassemblies and between word and main assemblies are activated by
(specific) ‘working memory’ (WM) populations. A WM population remains active for a while, after
initial activation, by reverberating activation in the population [11]. An active WM population binds
the assemblies to which it is connected in the manner outlined below.
3.1 Variable binding in a connection path
Figure 4 illustrates how binding is achieved in the NBA. The parts (a), (b) and (c) illustrate the same
binding process with increasing detail. In (a), the binding between the t subassemblies of V1 (or
V1-t) and N2 (N2-t) in Figure 3 is repeated. Figure 4b illustrates that this binding is based on a
‘connection matrix’, which consists of columns and rows of ‘connection nodes’ illustrated in Figure
4c.
Yj
V1-t
Xi Xin Yin
V1 i di
t
N2-t
t
WM di
N2
i
Connection matrix
( = inhibition)
Connection node
(a) (b) (c)
Figure 4: Binding by activity in a Connection Matrix (based on [1]).
Each specific Vi -t and Nj -t pair of subassemblies is interconnected in a specific connection node, lo-
cated in a connection matrix dedicated to binding Vi -t and Nj -t subassemblies. In general, when two
assemblies Xi and Yj (e.g., V1-t and N2-t) are concurrently active in the processing of a sentence,
they activate a WM population in their connection node by means of a gating circuit, as illustrated
in Figure 4c. In turn, the active WM population disinhibits a gating circuit by which activation can
flow from Xi to Yj , and another such circuit, not show in (c), by which activation can flow from Yj
to Xi . As long as their WM population is active, Xi and Yj are ‘bound’ because activation will flow
from one to the other whenever one of them is (initially) activated.
The NBA allows any noun to bind to any verb in any thematic role using dedicated connection
matrices. Also, the NBA has structure assemblies that can bind to other structure assemblies, such
as S1 in Figure 3, or clause structure assemblies ([1]). In this way, hierarchical sentence structures
can be represented, such as relative or complement clauses.
4
A given word assembly can bind to different structure assemblies at the same time, allowing the
creation of sentence structures in which words are repeated (e.g., cat sees cat). Yet, word assemblies
remain is situ in this way, so words in sentence structures are always accessible and grounded.
The role of the NBA is thus to establish a ‘connection path’ for any sentence structure that can
be formed in a language. Although it has been questioned whether this would be possible [8],
an available connection path is in fact a necessary condition for any model of (neuronal) variable
binding. That is, whenever two neuronal representations are bound in a functional way, there is
some kind of (direct or indirect) connection path between those representations. This path is needed
to produce (meaningful) behavior based on the functional binding at hand. For example, to answer
questions about the relation expressed by the binding [10]. The ability of the brain to establish such
connection paths derives from its ‘small-word’ like connection structure [12]. The NBA indeed has
such a structure [1].
In the NBA the connection matrices are given. But in a pilot simulation [13] we investigated the
possible development of such a connection structure. The simulations indicated that a structure
like a connection matrix could arise during development, based on initially random activation and
connection patterns. But, of course, each node in the connection matrix would also have to have the
circuits as illustrated in Figure 4c for the binding process to occur.
3.2 Content addressable sentence structures
The role of the NBA to (temporarily) create a connection path needed for the production of behavior
is illustrated in Figure 5. This figure illustrates how the NBA can answer (binding) questions (for a
simulation, see [1]).
S2 Bill knows? S1
v n n v
v n n v
V2 N3 N1 V1
t sees knows t
t Bill
t
N4 N2
Mary
John
Figure 5: Content addressable sentence structures and competition in the NBA in answering (bind-
ing) questions. Grey nodes are active.
The structure of the sentence Bill knows John, stored in the NBA, can be used to answer a (binding)
question like “What does Bill know?” (or Bill knows?). The question activates the assemblies
for Bill and knows and provides information that Bill is the subject of knows. This results in the
activation of the sentence structure Bill knows. The question also asks for the theme of the relation
Bill knows, which can be used to activate the gating circuits for the theme (t) subassemblies. This
will initiate a flow of activation from V1 to N2 and thus to John in Figure 5, producing the answer
to the question.
The NBA can correctly produce the answer even when, for example, Bill is a constituent of other
sentences as well, as in Bill sees Mary. The representation of Bill is the same in situ word assembly
in both sentences (which directly derives from the notion of in situ representation). As noted above,
they are distinguished in the NBA by the binding with different noun main assemblies (here, N1 vs
N3). The question Bill knows? activates Bill, which in turn activates both N1 and N3 to which it is
bound. Because the question entails that Bill is the subject, this results in the activation of both S1
and S2.
However, the question also identifies knows as the verb and not sees. This results in the activation
of V1 which inhibits the activation of V2, because main assemblies of the same kind inhibit each
other in the NBA [1]. In this way, asking for the theme produces the correct answer John (bound to
knows) instead of the incorrect answer Mary (bound to sees).
5
Figure 5 illustrates the role of a connection path in producing behavior. The question provides
sensory information resulting in the activation of Bill knows. The answer (John) is produced by a
motor program that would be activated by the in situ word assembly of John. If there were not a
connection path that would link the sensory information (Bill knows) to the motor activation (John) it
is difficult to see how this behavior could be produced. Yet, this kind of behavior is possible for any
arbitrary binding of words in (potentially novel) sentence structures or even new words in sentence
structures, as in Figure 2. In each of these cases, the behavior of answering a question (probing for
relation information or binding) depends on connecting sensory information to motor activation, in
a manner that satisfies the binding relations expressed by the relation at hand. The NBA produces
this connection path due to its small-world like connection structure and its ability to bind arbitrary
relations by the binding process outlined above.
Figure 5 also illustrates the role of content addressability in the NBA. Due to the in situ nature
of word (concept) representation, the question Bill knows? directly activates the word assemblies
Bill and knows, Because they are bound to the sentence structure in the NBA (i.e., because word
representations remain in situ in any sentence structure) the activation of Bill and knows directly
activates the partial sentence structure Bill knows.
It seems that the structure of a question itself indicates that sentence structures are content address-
able. After all, the question Bill knows? presents the words Bill and knows, indicates that Bill is
the subject of knows and indicates that the response should be the theme. All of this information
is precisely the information needed to directly activate the partial sentence structure of Bill knows
(instead of other partial structures such as Bill sees) and to initiate the flow of activation to produce
the answer John. It is difficult to see how this process could be more direct and thus faster than
in this way. Given the evolutionary pressure on fast and yet meaningful behavior, it seems that the
NBA fits these constraints in an optimal way.
4 Sentence processing in the NBA
Figure 6 illustrates how sentence processing can proceed in the NBA [14]. Here, sentence processing
results from the interaction between a ‘control network’ and the activation in the neural blackboard.
Figure 6 illustrates the processing of cat sees dog presented in Figure 3.
S1 S1 S1
n v n v n v
n n v n v
N1 N1 V1 N1 V1
sees t sees t
cat cat cat
t
N2
dog
N-n-S S-v V-v V-t N-t
N V N
V T
cat sees dog
(a) (b) (c)
Figure 6: Sentence processing in the NBA (based on [14]).
Sentence processing is incremental in the NBA. The input to the control network consists of word
information (e.g. Noun, Verb) and predictions based on the activation in the blackboard that arise
during processing. Based on its input, the control network has learned to activate the gating circuits
in the NBA needed to create the sentence structure. In (a) cat is recognized as a noun and the
network activates the binding between S1 and N1 with their n subassemblies. It also activates the
v subassembly of S1, which in turn activates a ‘prediction node’ V, predicting the occurrence of
a verb of the main clause. In (b) the control network, based on the combination of the prediction
node V and the occurrence of the verb sees, activates the v subassembly of V1, which results in the
binding of S and V. It also activates the t subassembly of V1 and a prediction node of a theme (T),
6
anticipating an object of the verb. In (c), the combination of the T node and the noun dog results in
the binding of V1 and N2 with their t subassemblies.
In [14] we trained a control network with a set of training sentences with different syntactic forms
(e.g., subject, theme, relative clause, complement clause). The network could then control a sub-
stantially larger set of sentences in which these syntactic forms were combined in different ways.
For example, the control network could produce the correct binding operations in sentences with
arbitrary deep (center) embeddings. However, the binding process in the NBA starts to break down
with these sentences after one or two embeddings, due to competitions with the connection matrices
involved [1, 14]. Hence, the NBA could account for both competence and performance aspects of
language processing.
4.1 Simulating neuronal activation during sentence processing
Brain activation observed with fMRI indicates that neuronal activation increases for longer and more
complex sentences [15]. Observations with intracranial electrodes provided more detailed informa-
tion [16]. In particular, when (sub) phrases in a sentence are formed (or ‘merged’ in linguistic terms)
neuronal activity first increases but then briefly decreases.
Figure 7 shows the activity in the NBA when the sentence Ten sad student of Bill Gates will move
from [16] is processed (taking Bill Gates as a single noun). Figure 7 (left) shows the basic structure
of the sentence in the NBA. Figure 7 (right) shows the sum of all activity (Total) in the NBA, and the
sum activities of the main assemblies (MA), the subassemblies (SA), and the WM populations in the
connection matrices. All assemblies and gating circuits are modeled with Wilson Cowan population
dynamics [17]. MA and SA assemblies have reverberating activity as well (until they are inhibited).
Activation values are normalized to fit in one plot.
S1
n v Total
students N1 AX1 will
nm np va WM
NM1 na PP1 of V1 MA
ten move
Adj1
pn SA
sad
N2
B-Gates Ten sad students of B-G will move
(ms)
Figure 7: NBA activity in processing Ten sad student of Bill-Gates will move.
The sum of all activity resembles the patterns observed in [16]. For example, after Ten sad students
(forming a phrase) activation increases but then drops, as with other phrases in the sentence. This is
in particular due to the subassemblies, which are inhibited by feedback from their connection matrix
when a binding has succeeded. But overall activation increases with sentence length and complexity
[15].
Figure 8 illustrates the neural activity in the NBA when it resolves an unproblematic ambiguity
(UPA) presented in [2]. Here, it is UPA13a (Bird found in room died) vs UPA13b (Bird found in
room debris). The NBA resolves ambiguities by a competition between conflicting WM populations
[18]. The NBA structure of both sentences and the conflicts (red dashed lines) are illustrated in the
figure (left).
The assumption is that UPA13a is the basis interpretation. So, found is the verb of a relative clause
bound to bird. Simulation shows that this incremental sentence processing proceeds without prob-
lems. So, the processing of UPA13b starts in the same way. The figure shows the individual WM
activations obtained with UPA13b, which reveal the bindings achieved (the sum of this activity
would result in a plot as in Figure 7). When bird (N1) is presented it binds to S1, indicated by the
black line S1-N1 representing the WM binding population. After the initial activation by the word,
this population remains active at a sustained level, so the binding remains intact during (and after)
7
S1 v died
bird n V2
N1 v
C1-N1 V1-N3
V1 found S1-N1
c cv t
C1 vp N3 C1-V1 S1-V1
debris PP1-N2
in PP1 V1-PP1
pn
N2
(ms)
room Bird found in room debris
Figure 8: NBA activity in processing Bird found in room debris.
the sentence processing. The combination bird found is interpreted as found (V1) being the verb
of a relative clause bound to N1 (as in UPA13a). This binding succeeds, as indicated by the WM
populations C1-N1 (green line) and C1-V1 (red line). The bindings of found in the room proceed
without problems, indicated by the WM populations V1-PP1 (red dash) and PP1-N2 (blue dash).
However, the last word debris provides a different interpretation of found. Instead of the verb of a
relative clause it is the main verb of the sentence. This interpretation initiates the activation of the
WM population V1-N3 (green dash), which represents the binding of debris as theme of found. It
also initiates the activation of the WM population S1-V1 (blue line) which represents the binding of
found as the main verb. This binding is in conflict with the binding C1-V1 (red line). The competi-
tion between the WM populations results in the decline of the activation of C1-V1, which resolves
the binding conflict. Hence, ambiguity resolution in the NBA results from dynamic competitions in
the architecture [18].
Alternatively, UPA13b could be the basis interpretation. Then found is the main verb directly. In
that case died causes a conflict in UPA13a. However, simulations show that this conflict cannot be
resolved because V1 blocks the binding of V2 (died) to S1. Also, V1 cannot bind anymore as verb
to a relative clause of bird (N1), because the activations of N1 and V1 have been or are inhibited by
other main assemblies (N2 and V2 respectively). Hence, this ambiguity cannot be resolved by the
NBA. However, this ambiguity is similar to the classic garden path sentence The horse raced past de
barn fell [19], which indicates that the NBA can account for both ambiguity resolution and garden
path effects [18].
5 Conclusions
Neural Blackboard Architectures (NBAs) can account for variable binding and (novel) combinato-
rial processing in neural terms. They also satisfy a number of characteristics of brain representation
and processing. Conceptual representations are always in situ, also when they are a part of a combi-
natorial structure. Hence, combinatorial structures are content addressable. This allows their direct
activation based on input information. NBAs also provide the connection paths that are needed to
account for the production of behavior. The sentence NBA can account for observed patterns of
neuronal activation during sentence processing. It can also handle linguistics phenomena such as
ambiguity resolution and can account for garden path sentences by the dynamic competitions in the
architecture.
Acknowledgments
The work of Frank van der Velde was funded by the project ConCreTe. The project ConCreTe
acknowledges the financial support of the Future and Emerging Technologies (FET) programme
within the Seventh Framework Programme for Research of the European Commission, under FET
grant number 611733. The research of Marc de Kamps has received funding from the European
Union Seventh Framework Programme (FP7/2007-2013) under grant agreement no. 604102 (HBP)
(Ref: Article II.30. of the Grant Agreement).
8
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