Feature, Conﬁguration, History

A Bio-inspired Framework for Information Representation in Neural Networks

eric Alexandre, Maxime Carrere and Randa Kassab

Inria Bordeaux Sud-Ouest, 200 Avenue de la Vieille Tour, 33405 Talence, France

LaBRI, Universit

e de Bordeaux, Institut Polytechnique de Bordeaux, CNRS, UMR 5800, Talence, France

Institut des Maladies Neurod

eratives, Universit

e de Bordeaux, CNRS, UMR 5293, Bordeaux, France

Keywords:

Information Representation, Computational Neuroscience, Pavlovian Conditioning.

Abstract:

Artiﬁcial Neural Networks are very efﬁcient adaptive models but one of their recognized weaknesses is about

information representation, often carried out in an input vector without a structure. Beyond the classical

elaboration of a hierarchical representation in a series of layers, we report here inspiration from neuroscience

and argue for the design of heterogenous neural networks, processing information at feature, conﬁguration and

history levels of granularity, and interacting very efﬁciently for high-level and complex decision making. This

framework is built from known characteristics of the sensory cortex, the hippocampus and the prefrontal cortex

and is exempliﬁed here in the case of pavlovian conditioning, but we propose that it can be advantageously

applied in a wider extent, to design ﬂexible and versatile information processing with neuronal computation.

1 INTRODUCTION

Artiﬁcial Neural Networks (ANNs) have proven to

be an excellent formalism for adaptive information

processing and have been successfully applied to an

impressive range of application domains for such

tasks as classiﬁcation, control and prediction. One

of the reasons for the undebatable success of ANNs

is certainly its unique position between the fertile

ﬁelds of Machine Learning and Computational Neu-

roscience. On the one hand, Computational Neuro-

science irrigates ANNs with architectural and com-

putational principles inspired from observation of the

living brain. On the other hand, Machine Learning an-

chors in solid mathematical grounds the most promi-

nent features of ANNs including learning algorithms.

Cross-fertilization from both domains explains

most of the advances of ANNs in the past decades,

even if some weaknesses remain. Among them, one

of the most critical ﬂaws of ANNs is certainly re-

lated to information representation. Basically, ANNs

are mainly dedicated to unstructured information pro-

cessing (their input is a ’ﬂat’ vector of data with-

out structure), whereas most real-world applications

require the elaboration and exploitation of complex

structures for an adequate knowledge representation.

Several measures have already been proposed to

overcome this problem. Firstly, input vectors can be

built according to a structured representation of infor-

mation proposed a priori, but this cannot be adapted

by learning and cannot be applied to the inner rep-

resentation of ANNs. Secondly, in opposition to

the connectionist approach, the symbolic approach is

generally more efﬁcient at deﬁning and processing

elaborated knowledge representations and it has been

proposed accordingly to associate both paradigms in

the so-called Connectionist-Symbolic Integration ap-

proach (Sun and Alexandre, 1997) but here also, the

two kinds of modules are generally too independent.

Thirdly, some interesting attempts have been made to

propose fully connectionist solutions to this problem.

Connectionist solutions to structured information

processing are mainly centred on innovative archi-

tectures and propose to adapt existing learning algo-

rithms to exploit them. One of the early models in

this direction, the LRAAM network (Sperduti et al.,

1997), explains how a recurrent elaboration of a net-

work allows to encode graphs. More recently, deep

neural networks (Bengio et al., 2013) renew studies in

which complex representations can be extracted from

many-layered neural networks, as suggested by the ar-

chitecture of the visual system in the brain (Rousselet

et al., 2004).

These solutions to build and manipulate complex

representations in ANNs are diverse (recursive or iter-

ative), but both are hierarchical : Complex represen-

316

Alexandre F., Carrere M. and Kassab R..

Feature, Conﬁguration, History - A Bio-inspired Framework for Information Representation in Neural Networks.

DOI: 10.5220/0005156003160321

In Proceedings of the International Conference on Neural Computation Theory and Applications (NCTA-2014), pages 316-321

ISBN: 978-989-758-054-3

 2014 SCITEPRESS (Science and Technology Publications, Lda.)

tations are seen as more and more abstract represen-

tations. In this position paper, we argue that these ar-

chitectural solutions are not fully satisfactory because

they miss an important dimension, the granularity of

information representation. Taking inspiration from

the brain, it can be remarked that hierarchical integra-

tion is not the only way to elaborate structured infor-

mation representation : In the brain, specialized mod-

ules extract information in the data ﬂow at various lev-

els of granularity and mix them in heterogenous (and

not only hierachical) systems for elaborated decision-

making processes. As far as sensory information pro-

cessing is concerned, we propose that these levels of

granularity are of three kinds:

1. Feature. At a conceptual level, features can be

extracted in the information ﬂow and arranged in

a hierarchical way;

2. Conﬁguration. At the individual level, the ag-

glomeration in a ’ﬂat’ vector of all the perceived

sensory information allows for the representation

of speciﬁc cases;

3. History. At the statistical level, overall trends can

be extracted from the recent history of perception.

In the remaining of the paper, we ﬁrst give some

elements from neuroscience for the existence of such

representations in the brain of mammals, then we pro-

pose to illustrate them and put them in situation in the

case of pavlovian conditioning, before discussing the

usefulness of such representations and of their associ-

ation in heterogenous systems.

2 INFORMATION

REPRESENTATION: A VIEW

FROM NEUROSCIENCE

Information representation in a hierarchical way lead-

ing to abstraction of concepts has been extensively

reported in the brain, for example in the visual cor-

tex. This hierarchical view of knowledge representa-

tion has already inspired ANNs like deep networks,

as evoked in section 1. From Hubel and Wiesel ini-

tial idea that neurons selective to orientation in the

primary visual cortex are built from the activity of

contrast-sensitive cells in a receptive ﬁeld in the tha-

lamus (Hubel and Wiesel, 1962), further experiments

along the ventral visual pathway have identiﬁed neu-

ronal areas responding to a hierarchy of concepts

(Rousselet et al., 2004) captured in increasingly large

receptive ﬁelds, from simple shapes to complex pat-

terns like faces. In that respect, the ventral pathway of

the cortex is proposed as the locus of semantic mem-

ory, with a cascade of neurons extracting a hierarchy

of concepts. Here we will speak about feature repre-

sentation.

Nevertheless, hierarchy is probably not the main

characteristic to be evoked when characterizing infor-

mation processing in the brain, but rather modular-

ity and heterogeneity. Indeed, the brain is organized

along complementary learning systems, as it has al-

ready been captured in early modeling approaches

(Doya, 1999; Alexandre, 2000).

Particularly, learning mode and information rep-

resentation in the hippocampus have been contrasted

with that of the cortex described above (McClelland

et al., 1995). Whereas the cortex learns slowly to

elaborate a semantic memory as a hierarchy of con-

cepts that will be exploited in generalization, the hip-

pocampus receives as sensory input a summary sketch

of current cortical activity and learns in one shot an

episodic memory of a speciﬁc event in its spatial and

temporal context. Contrarily to the cortical case, this

memory process is not prone to generalization; in-

stead, when faced to a similar episode, the recall pro-

cess will rebuild the original episode: the represen-

tation in the hippocampus is consequently unitary,

storing conﬁgural representations of speciﬁc events

(O’Reilly and Rudy, 2001). Here we will speak about

representation of conﬁguration.

Though complementary, these memories are also

in cooperation : The hippocampus is fed with sensory

inputs encoded with cortical features and reciprocally

participates to the slow cortical learning through the

phenomenon of consolidation : Speciﬁc cases stored

in the hippocampus during some task are sent back to

the cortex for the extraction of new features, pertinent

to the task (O’Reilly and Rudy, 2001). Whereas the

cortex can work in generalization, the task is more

difﬁcult for the hippocampus : Faced to an episode

similar to a stored one, the hippocampus can only

choose between pattern completion (supposing that

the episodes are equivalent, the stored one will be

recalled) and pattern separation (supposing that the

episodes must be distinguished one from the other,

the new one will be stored as a different pattern)

(O’Reilly and Rudy, 2001).

As far as task execution is concerned, the pre-

frontal cortex is certainly one of the most involved

cerebral structures (Fuster, 2001), since it supports

the temporal organization of behavior, i.e. selecting

at each moment, the best action (or decision) to trig-

ger, depending on the external (perceptive) and in-

ternal (emotional and motivational) sensory informa-

tion. Most of the time this decision is not purely re-

active (not depending directly on current stimuli) but

deliberative (depending on a more complex evalua-

tion, also based on past experience). To that end, the

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317

prefrontal cortex builds task sets and maintains them

active in a working memory mechanism, for compar-

ison and selection (by a loop with subcortical struc-

tures, the basal ganglia) of the most adapted solution.

In short, task sets are collections of recent cases of

associations between actions (or decision) that have

been triggered in certain sensory and temporal con-

texts, together with their performance in reaching the

goal that was expected.

The prefrontal cortex is organized, from its pre-

motor to its anterior part, in a hierarchy of levels of

control (Koechlin et al., 2003) depending on the na-

ture of the sensory and temporal context (and on the

structure that sends this information). This cascade

of control is made according to the perceptual con-

text, the episodic context and the motivational and

emotional context (Kouneiher et al., 2009). At each

level of description, the decision to engage an action

is taken, based on its recent history of success in this

context. This statistical analysis makes us describe

the prefrontal cortex as a locus for the representation

of history.

To better understand the way these representations

are elaborated in a concrete case, their association and

their impact on cerebral processes, we propose an il-

lustration in the case of pavlovian conditioning.

3 CASE STUDY: PAVLOVIAN

CONDITIONING

Pavlovian or respondent conditioning is the learning

process by which an animal is able to associate an Un-

conditional Stimulus (US = biologically signiﬁcant

stimulus announcing pain or pleasure, e.g. an electric

shock) to a conditional stimulus (CS, a neutral event

like a tone, that predicts the US). This learning has

been extensively studied in so-called fear condition-

ing experiments and is reported to be acquired quickly

if the electric shock is given subsequently to the tone

(Herry et al., 2008). Later on, the animal exhibits fear

behavior (ex: freezing) when solely exposed to the au-

ditory CS. This can be explained as an anticipation or

a preparation to the forthcoming pain. This response

will disappear if the electric shock is no longer given

(extinction process) and get re-activated by a new as-

sociation (renewal process). As such, this simple de-

scription could assimilate pavlovian conditioning to

an elementary associative learning where, for exam-

ple, an hebbian learning rule could increase and de-

crease a weight between two neurons standing for the

CS and the US. But reality is much more complex,

as shown by many behavioral studies that required to

reﬁne our understanding on pavlovian conditioning.

One of the most famous paradigms is the blocking

paradigm : In an early step, an association CS1-US is

acquired. Then CS1 is paired with CS2 to announce

the US. Here it could be said that CS2 becomes also

a faithful predictor of US and, from a purely asso-

ciative learning point of view, it should acquire also

a predictive power. This is not conﬁrmed by exper-

iments: CS2 alone triggers no conditioned response.

This paradigm has be interpreted as a parsimonious

learning (CS1 is sufﬁcient to predict US; no need to

perform a new learning about CS2) and led to pro-

pose a non purely associative learning rule in the early

70’s, the competitive Rescorla-Wagner learning rule

(Rescorla and Wagner, 1972). This rule states that

the modiﬁcation of the strength of association (the

weight, considering a neuronal implementation with

an input layer of CS connected to an output layer of

US) between CSi and US is proportional to the er-

ror of prediction, i.e. the difference between what ac-

tually happens (the US) and what was expected (the

sum of the predictive values of all present CSi). Two

other multiplicative terms in the rule are deﬁned as

the associability (saliency) of the CS and the effec-

tiveness (behavioral importance) of the US and are

often deﬁned as constant (but cf. below).

Now comes the question of the nature of the CS

and here also behavioral experiments are confusing:

CS often corresponds to a salient stimulus (a tone, a

ﬂashing light), but sometimes as in the case of extinc-

tion and renewal, the pertinent element is the context

in which conditioning occurs. This makes often mod-

elers add a speciﬁc neuron in the CS layer standing

for ’the context’, even if this is not fully satisfactory,

concerning representation of information. Another

problem is about conﬁgural learning : In a prototypi-

cal case, CS1 or CS2 alone predicts US, whereas the

occurrence of both predicts no US. This is difﬁcult to

explain with representation of single CSs because in

this case CS1 and CS2 will acquire a strong predictive

strength and the conjunction of both should add these

strengths and predict the US. A practical solution to

this classical problem is to create a new representa-

tion for CS1+CS2, seen as a new conﬁgural stimulus

(Schmajuk and DiCarlo, 1992) but this raises again

the question of building by hand the representation of

sensory information during pavlovian conditioning.

All the models of pavlovian learning evoked so

far are said US-processing models because their main

goal is to predict the US from current CSs and con-

sequently the same rule of predictive strength mod-

iﬁcation is applied to each CS, whatever its history,

which is not realistic, as other behavioral experiments

indicate (Le Pelley, 2004), hence the need for CS-

processing models.

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On the one hand, the Pearce-Hall model (Pearce

and Hall, 1980) tries to capture the fact that a CS of-

ten associated with US learns less quickly than a not

well known CS and proposes that the term of associa-

bilility of the CS could change in time and depend on

the level of surprise (absolute value of error of predic-

tion) and also on its own current value.

On the other hand, the Mackintosh model (Mack-

intosh, 1975) maintains a high associability for reli-

able CS, to explain the fact that assigning a CS to a

new US is quicker after an overtaining of the CS with

the previous US. Both cases underline the need to take

into account the recent history of conditioning. They

also raise new problems since it is obvious that the

Pearce-Hall and Mackintosh models are somewhat

contradictory, respectively proposing to decrease and

increase the associability of a frequent CS.

Confronting abstract models to behavioral obser-

vations is also an incitation to integrate more realistic

constraints in the models. This is for example the case

with uncertainty : Our word is stochastic (real CSs

do not always reliably predict US) and non station-

ary (a CS-US association rule valid at one time can be

obsolete the time after). Classical models of pavlo-

vian conditioning generally address poorly this kind

of constraints, which is a strong incentive to develop

more realistic models, for example including bayesian

approaches (Yu and Dayan, 2005; Deco et al., 2008).

In summary, this brief overview of pavlovian con-

ditioning shows that, more than one hundred years af-

ter its identiﬁcation, pavlovian conditioning is still not

fully understood. We have mentionned several obser-

vations non consistent with the current understanding

of this learning mechanism and, for each of them, a

model yielding the corresponding behavior, but at the

moment, there is no complete model integrating all

these mechanisms. We think that going deeper in the

understanding of the underlying cerebral substrate is

an excellent way to design such a complete model.

4 CEREBRAL SUBSTRATE TO

PAVLOVIAN CONDITIONING

The amygdala is the core cerebral structure for pavlo-

vian conditioning (Holland and Gallagher, 1999) but

it cannot be considered in isolation for two reasons.

On the one hand, the amygdala receives from other

cerebral structures, sensory information about the CS

and the US, for their association, but also other kinds

of information needed at various steps of the condi-

tioning. On the other hand, pavlovian conditioning re-

sults in a series of effects, that are transmitted to other

regions in the brain. These effects are of course the

pavlovian response itself, but also other attentional

and representational effects.

Amygdala is in fact a complex and heterogenous

structure (LeDoux, 2007), composed of several sub-

divisions and it can be convenient to distinguish three

regions with speciﬁc functions and connectivities :

1. The lateral amygdala (LA) receives sensory infor-

mation about the US and the CS from the thala-

mus and the cortex. This region is responsible for

learning the CS-US association.

2. The central nucleus of the Amygdala (CeA) is in

charge of the three aspects of the pavlovian re-

sponse: the motor aspect (e.g. in the case of

fear, freezing), the autonomic aspect (e.g. phys-

iological changes like heart beat or temperature

increase) and the hormonal aspect (release of

stress hormones and neuromodulators like acetyl-

choline, norepinephrine, dopamine, etc.).

3. The basal amygdala (BA) has a major represen-

tational role. It receives from LA information

about CS-US association, modulates this informa-

tion with contextual inputs coming from the hip-

pocampus and prefrontal, to elaborate a represen-

tation about the sensory nature of the US (Cardi-

nal et al., 2002). This information is sent to the

CeA to produce the pavlovian response but also

to the sensory cortex (attentional affect) and to the

prefrontal cortex-basal ganglia system (emotional

evaluation of stimuli has also strong effect in de-

cision making and operant conditioning).

This very rough description of the amygdalar

circuitry allows to put into context all the mecha-

nisms and observations about pavlovian conditioning

evoked in section 3. We posit a general computational

principle with this circuitry : Sensory thalamic and

cortical inputs to LA (generic features) should be suf-

ﬁcient to reliably predict US but other sensory infor-

mation coming from the hippocampus to BA will pos-

sibly propose CS-US association rules corresponding

to more speciﬁc cases. Later on, these speciﬁc cases

could be ’compiled’ from the hippocampus, in the

creation of new features in the cortex.

The LA and BA regions project in CeA to trig-

ger the good pavlovian response. The choice between

the corresponding CS-US rules will be learned from

a monitoring of their performance in predicting US,

evaluated in the orbitofrontal cortex (OFC). This ante-

rior region of the prefrontal cortex has very dense re-

lations with all regions of the amygdala and builds an

history of errors of US prediction, depending on the

sensory context (in the cortex and in the hippocam-

pus) in which they occurred (Pauli et al., 2011). These

errors of prediction are of two kinds.

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319

A positive error of prediction corresponds to the

case where an US is received whereas it was not pre-

dicted by LA neither by BA : in this case BA will send

a signal to the hippocampus to make this structure

learn by heart the current episode (Paz et al., 2009).

On the next occurrence of this episode the hippocam-

pus will be able to recall the corresponding US and

send a conﬁgural representation to BA. If this is often

repeated, this will result in consolidation in the cortex

and in the emergence of a feature representation in the

cortex, leading to a reliable rule in LA.

When a negative error of prediction occurs (from

the current sensory context, an US has been predicted

and doesn’t occur), several causes can be evoked :

1. The rule is valid but the speciﬁc context corre-

sponds to an exception which must be stored in

the hippocampus as associated to no US (case of

an extinction). If this happens again in this con-

text, the rule in BA based on this hippocampal in-

put will be favored in this speciﬁc case with regard

to the general rule coming from LA.

2. The rule is stochastic and not fully reliable. In

this case the level of stochasticity of the rule has

to be updated in OFC. This case and the previ-

ous case also result in an increase of the level of

acetylcholine (Yu and Dayan, 2005), which favors

learning in BA, in the hippocampus and in the cor-

tex, with the aim of making more precise the rules

currently under consideration.

3. The rule is no longer valid because the world is

non stationary but it could be valid again in the

future. The rule is consequently conserved with-

out modiﬁcation but inhibited by OFC that trig-

gers a release of norepinephrine (Yu and Dayan,

2005). This neuromodulator acts on the thalamo-

cortical inputs of LA, to look for a new rule (John-

son et al., 2011).

These causes have to be distinguished from the his-

tory of performance in US prediction stored in OFC.

A corresponding algorithm has been proposed by (Yu

and Dayan, 2005), to evaluate the decision threshold

between stochasticity and non stationarity : When the

rule is highly stochastic, many examples are needed

before deciding for a new rule; conversely when the

rule is very reliable, this change will be made more

easily. This alternative is also useful to propose a way

to release the seamingly contradictory effects between

the Pearce-Hall and Mackintosh models : The Mack-

intosh model could apply only when the rule is judged

valid and stable (no error of prediction), to evaluate

its level of reliability, whereas the Pearce-Hall model

could apply only in the early stages of selection of the

rule, to explore and detect candidate CS.

5 DISCUSSION

In this paper, we have laid emphasis on the fact that

different levels of granularity of information are rep-

resented and processed in different brain regions and

can be manipulated in ANNs as well : Multi-layered

ANNs can extract a set of features, as a hierarchy

of concepts, with some analogy to the sensory cor-

tex. Associative memories like the Hopﬁeld model

have been shown to learn by heart conﬁguration of

features representing speciﬁc cases in a recurrent ar-

chitecture, as it is the case in the hippocampus. In ad-

dition to these cases, history of the statistical tenden-

cies of certain information ﬂows is believed to be cap-

tured in the prefrontal cortex. Though not yet mature

in ANNs, early models implementing this latter kind

of processing are proposed (Kouneiher et al., 2009).

In this paper, we have also shown, through the ex-

ample of pavlovian conditioning, why mixing these

levels of granularity is so powerful. On the one

hand, the combination of associative rules involv-

ing features and conﬁgurations is of central impor-

tance in this learning and is more generally reminis-

cent of the principle of mixing generic rules and spe-

ciﬁc cases. On the other hand, it seems possible to

reconcile different cases where pavlovian condition-

ing works differently by performing a statistical anal-

ysis that allows to distinguish different functioning

modes. Selecting strategies and switching between

procedures seem to be a major role of the prefrontal

cortex (Fuster, 2001; Koechlin et al., 2003), fed by

contextual and episodic information from the sensory

cortex and the hippocampus respectively, and oper-

ated through its control on many cerebral structures,

sometimes by the release of neuromodulators.

Pavlovian conditioning was chosen here to exem-

plify these principles but many characteristics of the

cerebral system support the fact that computing with

these different levels of representation is more gen-

eral than this only case. In addition, we are deeply

convinced that computing with these levels of rep-

resentation might be advantageously exported to the

domain of ANNs. It could provide this domain with

an efﬁcient way to combine generic rules and spe-

ciﬁc cases and to make decisions based on the evalu-

ation of performances of such heterogenous modules,

which seems to underlie an important part of our cere-

bral system and its emergent cognitive capabilities.

ACKNOWLEDGEMENTS

This work was partly supported by the Keops ANR

project.

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320

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