Doctoral thesis (Dissertations and theses)
INFERNET : A neurocomputational model of binding and inference
Sougné, Jacques
1999
 

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Keywords :
INFERENCE; Spiking neurons
Abstract :
[en] We know that color and form are processed in distinct areas of the human brain. This information must somehow be brought together. How the brain might achieve these colorform associations, as well as all other associations of this type, is one of the central themes of this dissertation. When looking at a field of poppies on a sunny day, how can we correctly associate the color red with the poppies, green with the grass, and blue with the sky, while avoiding associating the color red with the grass and the color blue with poppies? How can we associate the perception of red poppies with the name “red poppy,” and with its superordinate category “flower?” A red poppy is composed of several features, like its shape, color, texture, etc. How might a cognitive system bind these features to build a coherent whole? If we see Louise picking a red poppy, how can we correctly associate Louise with the picker and the red poppy with the picked object, without making the opposite and incorrect association? These associations may seem easy to us, but how does the brain achieve them? How a cognitive system binds a set of features together, associates a filler with a role, a value with a variable, an attribute with a concept, ... is what we mean by “the binding problem.” This thesis focuses on the neurobiological processes that enable connectionist cognitive systems to display binding abilities, on the constraints that affect the binding process, and on the cognitive consequences of these constraints. To study these processes, we developed a computer model of them. This method forces a detailed and unequivocal description of processes used by the simulation. This method is also a powerful means of generating new hypotheses. In this study we attempt to link psychological processes with the neuronal constraints that act on brain functioning. The brain is composed of approximately 10 billion highly interconnected neurons. To achieve binding it is necessary for neurons to communicate with each other because it has been shown that different aspects of a perceived object are not processed in the same cortical areas. Therefore, there must be a means for binding neurons responding to each of these different aspects. The neurons responding to the color red, to the object’s shape, and to its name must be linked to produce a coherent whole representing the red poppy. Neurons are connected by synapses. The functioning of these connections is constrained by the architecture of the brain and by the process of signal transmission. A particular neuron is connected to a relatively small set of other neurons. Therefore, communication between any two neurons generally requires a chain of transmission through intermediate neurons. A pre-synaptic neuron has an effect on another neuron (called the post-synaptic neuron) only if the pre-synaptic neuron emits an action potential (i.e., if it fires). As a consequence, this brief polarization, which last a few milliseconds, results in a modification of other neurons' firing potential. Transmission efficiency depends on the strength of the connecting synapses and the state of the post-synaptic neuron. When a neuron emits an action potential, it is completely insensitive to incoming signals for a short period, then its sensitivity slowly increases. A single pre-synaptic cortical neuron cannot alone provoke the post-synaptic neuron firing. This post-synaptic neuron must receive convergent and more or less synchronized signals from many synapses in order to fire. These neurobiological properties of neurons and neuronal firing constrain the way in which the brain can achieve binding. Among the various hypotheses of how this could be done, we chose synchronization of action potentials for our model. In the red poppy example, neurons responding to the color red will fire in synchrony with those responding to the shape of the flower and to the name “red poppy.” This particular synchronized cluster corresponding to “red poppy” must be temporally distinguished from the cluster responding to “green grass.” Numerous neurobiological studies seem to confirm this action-potential synchrony hypothesis. They show that synchronization involves a particular timing precision and occurs at a particular oscillation frequency. This oscillation requires participating neurons to fire repeatedly and rhythmically for a particular period of time. These properties of firing timing and duration have been implemented in a computer simulation called INFERNET. This artificial neural network uses integrate-and-fire nodes (artificial neuronlike elements). These nodes fire at a precise moment and transmit their activation, with a particular strength and delay, to nodes connected to them. When the potential of the node reaches a particular threshold, it emits a spike. Thereafter, the potential is reset to a resting value. As with real neurons, this node will then be completely insensitive to incoming signals for a short period, after which its sensitivity will slowly increase. INFERNET solves the binding problem by means of oscillation synchrony. Symbols are represented by clusters of nodes firing in synchrony. Fillers are also bound to their roles by synchrony. This synchronous activity defines a window of synchrony i.e., an interval during which the required nodes fire. This time interval takes neurally plausible values. Object discrimination is achieved by a succession of windows of synchrony. Bindings are maintained in memory by the use of particular oscillations. The rhythmic activity and the synchrony precision constrain the number of distinct entities that the system is able to maintain in memory. This represents the short term memory span of INFERNET. We show that this span is comparable with human short term memory span. The limited number of windows of synchrony also constrains predicate representations. This prediction is tested on human participants. If there are too many windows of synchrony, these will interfere with each other. In addition, binding strength decreases with time. These two properties explain why the short-term memory of INFERNET displays primacy and recency effects similar to those observed in humans. Bindings in INFERNET are also constrained by the number of intermediate steps required for particular role nodes to enter into synchrony with the filler nodes. This constraint is shown to provided a plausible explanation of various differences human reasoning. The last INFERNET constraint concerns multiple instantiation. This problem arises in connectionist networks as soon as a symbol has to be simultaneously used twice in different ways. Since INFERNET’s short term memory is the transient activation of parts of long term memory, it cannot make multiple copies of a symbol, in the same way, for example, that a symbolic system does. The INFERNET solution to the multiple instantiation problem involves superposition of different node oscillations. This process is constrained by the refractory period of the nodes. A number of simulations with INFERNET and experiments on humans show that this solution is psychologically plausible. Multiple instantiation is also shown to be a plausible explanation of certain similarity effects in short term memory. INFERNET is also shown to be capable of symbolic processing with using neurologically and psychologically plausible mechanisms that have the advantages of generalization and noise tolerance found in connectionist networks. Finally, under certain circumstances, noise is shown to enhance INFERNET’s processing capabilities.
Disciplines :
Neurosciences & behavior
Author, co-author :
Sougné, Jacques ;  Université de Liège - ULiège > UDI FAPSE
Language :
English
Title :
INFERNET : A neurocomputational model of binding and inference
Defense date :
August 1999
Number of pages :
224
Institution :
ULiège - Université de Liège
Degree :
Doctorat
Promotor :
De Keyser, Véronique
Jury member :
French, Robert M.
Brédart, Serge
Leclecq, Dieudonné
Cleermans, Axel
Mareschal, Denis
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since 04 June 2009

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