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Connectionist theories, ART and cognitive potentials
C O N N E C T I O N I S T THEORIES, ART AND C O G N I T I V E P O T E N T I A L S L E N A - C N R S La Salpetri~re, 47 Bd Hopital, 75631 Paris.
J.P. Banouet
The connectionist models of brain processes are likely to be increasingly central to the development of theoretical neuroscience for both experimental and "philosophical" reasons. This class of models has the unique potential of integrating results from a wide range of experimental fields related to brain research, from behavioral and cognitive psychology to cellular and molecular neurobiology. From a different point of view, the often implicit philosophical options underlying the different theoretical approaches cover the whole range of philosophical positions. Thus the nativist theory of brain development states that experience does not induce any significant increase of order in an already highly structured brain system. The neural models corresponding to this theory are highly structured systems designed to fit specific data. Yet, learning can also be tested on this type of model to explore the adaptation of the apriori structures for the computation of a specific class of functions. Support for this position is found on the macroscopic level of brain organization, which is for a large part genetically determined. Yet, on the microscopic level of brain structure, neurobiologists have observed during the early stages of maturation a transient redundancy in the patterns of connectivity (Changeux and Danchin, 1976) or various patterns of activity, named prerepresentations, in the adult. From this observation,the selectionist approach to brain development has postulated that interaction with the environment and specific experiences select or stabilize preexisting patterns of connection or firing. Such models of learning by selection have been applied on the level of neuronal networks and formally related to the statistical mechanics of disordered systems, e.g. the theory of spin glass. The originality of such models lies in that the initial state of the system can be viewed as a complex (and not flat) energy landscape. In such models, memory is described as a hierarchical ultrametric structure with possibilities of categorisation (Toulouse et al., 1986). Conversely, for the empiricists, the initial state of the system is a "tabula rasa". The internal organization of the system is entirely structured by experience and environment. In the corresponding neural models, the potential connectivity of the neural networks is maximal, and the synaptic weights are nul prior to the confrontation with experience. The properties and the structure of the system are entirely derived from the specific connectivity induced by experience. However, a certain "opacity" exist concerning the role played by each element in the system. Obviously, this approach does not correspond to the reality of the brain development, either on the macroscopic or on the microscopic level. Yet, this approach to learning in unstructured networks is perfectly justified as a study of a simplified problem. Even though these philosophical positions seem difficult to reconcile, when placed in the framework of the connectionist theory, they appear to be more complementary than contradictory. In fact, both nativist and empiricist features can be traced back to some of the most elaborated connectionist theories. For example, the Adaptive Resonnance Theory (ART) of Grossberg, postulate macrostages able to compute functional properties. Different features characteristic of the organization of the brain macrocircuits can be recognized at this level: the two-way connections between many important brain structures, the dichotomy of the pathways to perceptual systems into specific and nonspecific channel, and the hierarchical organization of the processing levels. These structures are innate and genetically determined . But at the microtheory level of analysis, specific network structures could also be entirely determined by a repetition of the same events. Two key properties of ART, namely the two-way traffic between two levels of analysis, F1 and F2, and the relations between long-term and short-term memory (LTM, STM) have been submitted to the constraint of experimental results combining behavioral and electrophysiological dependent variables, relevant to the level of the macrotheory. The electrophysiological variables are Event Related Potentials (ERP), often called "cognitive potentials" or "endogenous" potentials. They are distinct from "exogenous" evoked potentials, both on phenomenological and biological grounds. Indeed, they do not react to the physical characteristics of the stimuli, but rather reflect cognitive processes, mainly memorization and pattern recognition. Like all potentials recorded from the scalp, they do not significantly reflect action potentials but rather the summation of particularly slow excitatory or inhibitory post-synaptic potentials (slPSP, sEPSP). These are characterized by a synaptic delay of at least 250 msec and a maximal duration of several seconds. The resulting ERPs are therefore perfectly suited for the study of short-term memory and cognitive processes. The experimental results derived from the simultaneous recording of reaction time and ERPs in a learning paradigm of a cognitive task fit the predictions of the theory (Banquet and Grossberg,1987). They further suggest that the macrostages of the theory are selectively and differentially activated according to the stage of learning of the task and the experimental demands on the subject, which could correspond, on the cognitive level, to different processing strategies. Moreover, besides the parametric variations corresponding to cognitive processes and to short term memory traces, the ERPs give indirect evidence of the formation of a long term memory trace, comparable to the effect of long term potentiation (LTP) recorded in the animal during high frequency stimulation or classical conditionning.
159
J.P. Banouet
The connectionist models of brain processes are likely to be increasingly central to the development of theoretical neuroscience for both experimental and "philosophical" reasons. This class of models has the unique potential of integrating results from a wide range of experimental fields related to brain research, from behavioral and cognitive psychology to cellular and molecular neurobiology. From a different point of view, the often implicit philosophical options underlying the different theoretical approaches cover the whole range of philosophical positions. Thus the nativist theory of brain development states that experience does not induce any significant increase of order in an already highly structured brain system. The neural models corresponding to this theory are highly structured systems designed to fit specific data. Yet, learning can also be tested on this type of model to explore the adaptation of the apriori structures for the computation of a specific class of functions. Support for this position is found on the macroscopic level of brain organization, which is for a large part genetically determined. Yet, on the microscopic level of brain structure, neurobiologists have observed during the early stages of maturation a transient redundancy in the patterns of connectivity (Changeux and Danchin, 1976) or various patterns of activity, named prerepresentations, in the adult. From this observation,the selectionist approach to brain development has postulated that interaction with the environment and specific experiences select or stabilize preexisting patterns of connection or firing. Such models of learning by selection have been applied on the level of neuronal networks and formally related to the statistical mechanics of disordered systems, e.g. the theory of spin glass. The originality of such models lies in that the initial state of the system can be viewed as a complex (and not flat) energy landscape. In such models, memory is described as a hierarchical ultrametric structure with possibilities of categorisation (Toulouse et al., 1986). Conversely, for the empiricists, the initial state of the system is a "tabula rasa". The internal organization of the system is entirely structured by experience and environment. In the corresponding neural models, the potential connectivity of the neural networks is maximal, and the synaptic weights are nul prior to the confrontation with experience. The properties and the structure of the system are entirely derived from the specific connectivity induced by experience. However, a certain "opacity" exist concerning the role played by each element in the system. Obviously, this approach does not correspond to the reality of the brain development, either on the macroscopic or on the microscopic level. Yet, this approach to learning in unstructured networks is perfectly justified as a study of a simplified problem. Even though these philosophical positions seem difficult to reconcile, when placed in the framework of the connectionist theory, they appear to be more complementary than contradictory. In fact, both nativist and empiricist features can be traced back to some of the most elaborated connectionist theories. For example, the Adaptive Resonnance Theory (ART) of Grossberg, postulate macrostages able to compute functional properties. Different features characteristic of the organization of the brain macrocircuits can be recognized at this level: the two-way connections between many important brain structures, the dichotomy of the pathways to perceptual systems into specific and nonspecific channel, and the hierarchical organization of the processing levels. These structures are innate and genetically determined . But at the microtheory level of analysis, specific network structures could also be entirely determined by a repetition of the same events. Two key properties of ART, namely the two-way traffic between two levels of analysis, F1 and F2, and the relations between long-term and short-term memory (LTM, STM) have been submitted to the constraint of experimental results combining behavioral and electrophysiological dependent variables, relevant to the level of the macrotheory. The electrophysiological variables are Event Related Potentials (ERP), often called "cognitive potentials" or "endogenous" potentials. They are distinct from "exogenous" evoked potentials, both on phenomenological and biological grounds. Indeed, they do not react to the physical characteristics of the stimuli, but rather reflect cognitive processes, mainly memorization and pattern recognition. Like all potentials recorded from the scalp, they do not significantly reflect action potentials but rather the summation of particularly slow excitatory or inhibitory post-synaptic potentials (slPSP, sEPSP). These are characterized by a synaptic delay of at least 250 msec and a maximal duration of several seconds. The resulting ERPs are therefore perfectly suited for the study of short-term memory and cognitive processes. The experimental results derived from the simultaneous recording of reaction time and ERPs in a learning paradigm of a cognitive task fit the predictions of the theory (Banquet and Grossberg,1987). They further suggest that the macrostages of the theory are selectively and differentially activated according to the stage of learning of the task and the experimental demands on the subject, which could correspond, on the cognitive level, to different processing strategies. Moreover, besides the parametric variations corresponding to cognitive processes and to short term memory traces, the ERPs give indirect evidence of the formation of a long term memory trace, comparable to the effect of long term potentiation (LTP) recorded in the animal during high frequency stimulation or classical conditionning.
159