3
Shreesh P. Mysore
08/24/2003
Why Let Networks Grow?
Thomas R. Shultz
Department of Psychology and School of Computer Science
McGill University
1205 Penfield Ave.
Montreal, Quebec,
Canada H3A 1B1
E-mail:
Shreesh P. Mysore
Control and Dynamical Systems Program
California Institute of Technology
1200 E. California Blvd.
Pasadena, CA 91125
U.S.A.
E-mail:
Steven R. Quartz
Division of the Humanities and Social Sciences, and
Computation and Neural Systems Program
California Institute of Technology
1200 E. California Blvd.
Pasadena, CA 91125
U.S.A.
E-mail:
1. Introduction
Beyond the truism that to develop is to change, the process of developmental change itself has been relatively neglected in developmental science. Methodologically, behavioral studies have traditionally utilized cross-sectional studies, which have revealed a great deal about how certain behavioral and cognitive abilities differ at various developmental times, but tend to reveal less about the developmental processes that operate to transform those cognitive and behavioral abilities. A variety of reasons exist that account for the lack of explanations of developmental change, and range from the methodological challenges their study entails to principled, learning-theoretic arguments against the very existence of such processes (Macnamara, 1982; Pinker, 1984).
Among the most influential arguments against developmental change was Chomsky’s (1980) instantaneity argument, which argued that there is nothing from a learning standpoint that developmental change adds to an account of development – if one starts with the null hypothesis that children are qualitatively similar learners to adults and suppose that development is instantaneous (in the sense that there are no time dependencies in development), then departing from this by supposing children are initially more restricted learners only results in weakening their acquisition properties. The upshot is that less is less, and developmental change will only add to an account of development by reducing acquisition properties. Thus, as a sort of charity principle, one ought to at least start with the hypothesis that children do not differ substantially in their acquisition capacity from adults, since explaining development is hard enough without having to do so with greatly diminished learning.
These sorts of arguments lead to the widespread assumption that there is little intrinsic theoretical insight to be gained from studying processes of developmental change. Indeed, even with the advent of connectionist models and their adoption in developmental science, most of the models that were used were qualitatively similar to models of adult learning. That is, most models used a fixed feedforward architecture in which the main free parameter was connection strengths. Therefore, such models implicitly adopted Chomsky’s argument and began with the assumption that the immature and mature state are qualitatively identical.
Yet, how well-founded is this assumption, and is it the case that the main free parameter of learning is akin to the connection strengths of a fixed architecture? Viewed from the perspective of developmental cognitive neuroscience, it is now well established that the structural features of neural circuits undergo substantial alterations throughout development (e.g., Quartz and Sejnowski, 1997). Do such changes add anything of interest to the explanation of development and developmental change? If so, does a better understanding of the processes of developmental change undermine Chomsky’s argument, and thereby demonstrate that an understanding of developmental change is crucial for understanding the nature of cognitive development?
In this chapter, we ask the question, why let networks grow? We begin by reviewing the wealth of accumulating data from neuroscience that network growth appears to be a much more central feature of learning than is traditionally assumed. Indeed, it appears that the assumption that the main modifiable parameter underlying learning is changes in connection strength in an otherwise fixed network may be in need of revision. In section 2, we revisit this question with our eye toward the evidence for neurogenesis – the postnatal birth and functional integration of new neurons – as an important component of learning throughout the lifespan. In section 3, we turn to consider the computational implications of learning-directed growth. There, we will consider whether less really is really less, or whether by breaking down the traditional distinction between intrinsic maturation and cognitive processes of learning the learning mechanism underlying cognitive development thereby becomes substantially different, and more powerful, in its learning properties than a fixed architecture. In section 4, we present a case in which activity-dependent network growth underlies important acquisition properties in a model system, auditory localization in the barn owl. Our motivation for presenting this model system is that this non-human system is very well characterized in terms of the biological underpinnings, which provide important clues into the likely biological mechanisms underlying human cognitive development, and which give rise to general computational principles of activity-dependent network growth. In section 5, we present evidence from modeling cognitive development in children. There, we explore a number of computational simulations utilizing the Cascade Correlation (CC) algorithm. In contrast to back propagation algorithm, CC starts with a minimal network and adds new units as a function of learning. Thus, CC can be viewed at a high level as a rule for neurogenesis, and thus is a means of exploring the computational and learning properties of such developmental processes. In section 6, we present the main conclusions from this work and point to areas of future research and open research questions.
2. Experience-dependent neurogenesis in adult mammalian brains
Two broad categories of neural plasticity exist, based on the nature of expression and encoding of change in the nervous system. They are ``synaptic efficacy change" and ``structural plasticity". Whereas synaptic efficacy change has been the predominantly studied form of plasticity (Martin and Morris, 2002), neuroscience research also contains abundant evidence of structural plasticity - dendritic spine morphology change in response to stimuli, synaptogenesis, and/or reorganization of neural circuits (see (Lendvai et al., 2000; Zito and Svoboda, 2002) for experience-dependent spine plasticity and a review of synaptogenesis respectively). The one form of adult structural plasticity that has not been a part of mainstream neuroscience until recently, is neurogenesis, i.e., the birth or generation of new neurons. Adult neurogenesis, as the name suggests, is the production of new neurons postnatally. We briefly discuss the state of current day research in this area.
2.1 Background and status of current research in neurogenesis
As early as the turn of the twentieth century, researchers such as Koelliker and His had described the development of the central nervous system in mammals in great detail and had found that the complex architecture of the brain appears to remain fixed from soon after birth (see (Gross, 2000) for references). Hence, the idea that neurons may be added continually was not considered seriously. Cajal and others described the different phases of neuronal development and as neither mitotic figures nor the phases of development were seen in adult brains, they suggested that neurogenesis stops soon after birth. Although there were sporadic reports raising the possibility of mammalian adult neurogenesis, the predominantly held view was opposed to this idea. In the 1950s, the 3H-dT autoradiography[1] technique was introduced to neuroscience research and in 1961, Smart (Smart, 1961) used it, for the first time, to study neurogenesis in the post-natal brain (3-day old mice). Subsequently, Altman (Altman, 1962; Altman and Das, 1965) published a series of papers in the 1960s reporting autoradiographic evidence for new neurons in the neocortex, olfactory bulb (OB), and dentage gyrus (DG) of the young and adult rat. He also reported new neurons in the neocortex of the adult cat. Unambiguous evidence testifying to the neuronal nature (as opposed glial) of these proliferating cells was not available. Starting from 1977 through 1985, Kaplan (Kaplan, 1984; Kaplan, 1985) published a series of articles that used electron microscopy along with 3H-dT labeling to confirm the results of Altman et al from 15 years ago. He was able to verify the neuronal nature of the proliferating cells in the DG and OB of adult rats, cerebral cortex of adult rats. In parallel, from 1983 through 1985, Nottebohm et al (Goldman and Nottebohm, 1983; Nottebohm, 1985; Paton and Nottebohm, 1984) published results showing (using EM, 3H-dT autoradiography and electrophysiology) that neurogenesis occurs in adult songbirds in areas that correspond to the primate cerebral cortex and hippocampus of adult macaque monkeys. Simultaneously in 1985, Rakic (Rakic, 1985) published an authoritative 3H-dT study on adult rhesus monkeys which concluded contrarily that all neurons of the adult rhesus are generated during prenatal and early postnatal life. Further, based on subsequent studies with electron microscopy and an immunocytochemical marker for astroglia (and not neurons), Eckenhoff and Rakic (Eckenhoff and Rakic, 1988) suggested that a stable neuronal population in adult brains may be a biological necessity in an organism whose survival relies on learned behavior acquired over time, and the traditional view of adult neurogenesis continued to be held.
Starting from 1988, it was finally established without doubt, that neurogenesis occurs in the dentate gyrus of the adult rat. Stanfield and Trice (Stanfield and Trice, 1988) showed that new cells in the rat DG extend axons into the mossy fiber pathway (DG to CA3 projection). More studies (that followed the fate of a cell from cell division to a differentiation in its final site of residence (Kornack and Rakic, 1999) confirmed adult neurogenesis in the DG and OB of the macaque monkey. While DG neurogenesis is now widely accepted in both rodents and primates, neocortical neurogenesis is still debated. In primates, cortical neurogenesis has stirred a controversy in the past few years. Gould et al (Gould et al., 1999) recently reported that the macaque monkey neocortex (prefrontal, parietal and temporal lobes) acquires large numbers of new neurons throughout adulthood. Further, Shankle at al (Shankle et al., 1998) reported independently that large additions that alternate with comparable losses of neurons occur in the human neocortex in the first years of postnatal life. The potentially broad biomedical implications and impact on the development of replacement therapy for neurological disorders have added to the attention these reports have received. In their study, Gould et al (Gould et al., 2001) used the BrdU immunohistochemical labeling[2] technique to detect cell proliferation. Other researchers like Rakic (Nowakowski and Hayes, 2000; Rakic, 2002) are skeptical about the results because of the high doses of BrdU, the argued non-specificity of cell type markers used, potential misinterpretation of endothelial cells as migrating neurons, the ambiguity in the identity of migrating bipolar shaped cells (neurons, immature oligodendrocytes and immature astrocytes can all adopt bipolar shapes), the large numbers of neurons in the migratory stream (approximately 2500 new neurons per day (Gould et al., 2001), and false positives due to optical problems (potential superposition of small BrdU labeled satellite glial cells on unlabeled neuronal soma due to the use of an insufficient number of optical planes in confocal microscopy).
In summary, incontrovertible evidence now exists showing that adult neurogenesis occurs in the DG and OB of mammals (including primates) (Gage, 2002). The research community is at present not unanimous in its view of neocortical neurogenesis in mammals, especially primates. We now turn to the effects of psychological and environmental factors on DG neurogenesis.
2.2 Modulation of hippocampal neurogenesis
Several studies (see (Gross, 2000)) show that the lifespan of a newly generated hippocampal neuron is positively affected by enriched environment living (in the wild and in laboratory conditions; in birds (Barnea and Nottebohm, 1994; Nottebohm et al., 1994), in mice(Kempermann et al., 1997; Kempermann et al., 1998), in rats (Nilsson et al., 1999). Oestrogen is known to stimulate the production of new immature neurons in the DG. Interestingly, just running in a wheel enhances the number of BrdU labeled cells in the DG, although this could be due to several reasons (enhanced environmental stimulation, reduction in stress or due to an effect of exercise like increased blood flow).
Stressful experiences have been shown to decrease the numbers of new neurons in the DG, by downregulating cell proliferation. Studies suggest that this effect is caused by alterations in the hypothalamic pituitary adrenal axis (HPA). The influence of stress has been introduced through exposure to predator odor in adult rats and in 1 week old rat pups, social stress in tree shrews and marmosets, and developmental stress in rats (with effects that persist into adulthood). Adrenal steroids probably underlie this effect as stress increases adrenal steroid levels and glucocorticoids decrease the rate of neurogenesis. Further, it has been shown that the decreased rate of neurogenesis associated with ageing is due to an increase in glucocorticoid levels (see (Gould and Gross, 2002) for a summary of these effects and for references).
Neurogenesis has been correlated with hippocampally dependent learning experiences. For instance, trace eye blink conditioning and spatial learning (both hippocampally dependent tasks) in animals lead to an increase in the number of neurons – this happens through an extension in the survival of neurons rather than an increase in their production. There appears to be a critical period following cell production such that learning occurring in this period increases neuronal lifespan.
All the above factors that affect adult neurogenesis in the dentate are interpreted, along with the evidence of their continual addition in several areas of the vertebrate brain, as indicating that it is functionally significant and not a mere relic left over from evolution.
2.3 Function of neurogenesis
An important question in the context of adult neurogenesis is whether it subserves a valid function or is simply a vestige of development (Gross, 2000). We will first look at some evidence that suggests that it has a useful role to play, and then look at what exactly that role may be (Kempermann, 2002; Nottebohm, 2002) and how it is achieved.
New hippocampal neurons are suspected of having a role in learning and memory. Several pieces of evidence have been suggested (Gross, 2000) in support of this argument. New neurons are added to structures that are important for learning and memory (like the hippocampus, lateral prefrontal cortex, inferior temporal cortex, and posterior parietal cortex). Several conditions that are detrimental to the proliferation of granule cells in the dentate (like stress, increased levels of glucocorticoids, etc) are also implicated in lower performance in hippocampally dependent learning tasks and this suggests a causal link between the two. Several conditions that increase proliferation (like enriched environments, increased oestrogen levels, wheel running, etc) also enhance performance. Increases in social complexity have been found to enhance the survival of new neurons in birds (Lipkind et al., 2002). However, it is to be noted that in the cases of both positive and negative modulators of neurogenesis, other factors such as changes in the dendritic structure of synapses can contribute to changes in learning performance. Finally, adult generated neurons may share some properties with embryonic and early postnatal neurons in their ability to extend axons, form new connections more readily, and to make more synapses (!)}. Granule cells in the dentate show LTP of greater duration in younger rats compared with older ones, and adult generated granule cells appear to have a lower threshold for LTP. More speculatively, Gross (Gross, 2000) suggests that the transient nature of many adult neurons could mediate the conversion of short term memory (encoded in their activity patterns) to long term memory (involving a transfer of these activity patterns to older circuits followed by the death of these new cells). A curious fact is that the lifetimes of the new adult-generated cells in the rat and macaque dentate are three and nine weeks respectively, which correspond to the approximate time of hippocampal storage in these species. Thus, learning and memory may involve the construction of entirely new circuits with new or unused elements (structural plasticity through neurogenesis (discussed here), spine motility (Lendvai et al., 2000), and synaptogenesis (Zito and Svoboda, 2002)), as well as the modulation of existing circuits (synaptic efficacy change, (Martin and Morris, 2002)).