“Origins of communicative disorders ” to Elizabeth Bates, and by a grant from the John D. and Catherine T. MacArthur Foundation. We are grateful to Larry Juarez and Meiti Opie The effects of focal brain injury are investigated in the first stages of language development, during the passage from first words to grammar. Parent report and/or free speech data are reported for 53 infants and preschool children between 10- 44 months of age. All children had suffered a single, unilateral brain injury to the left or right hemisphere, incurred before six months of age (usually in the pre- or perinatal period). This is the period in which we should expect to see maximal plasticity, but it is also the period in which the initial specializations of particular cortical regions ought to be most evident. In direct contradiction of hypotheses based on the adult aphasia literature, results from 10- 17 months suggest that children with righthemisphere injuries are at greater risk for delays in word comprehension, and in the gestures that normally precede and accompany language onset. Although there were no differences between left- vs. right-hemisphere injury per se on expressive language, children whose lesions include the left temporal lobe did show significantly greater delays in expressive vocabulary and

Large-scale neural networks are thought to be an essential substrate for the implementation of cognitive function by the brain. If so, then a thorough understanding of cognition is not possible without knowledge of how the large-scale neural networks of cognition (neurocognitive networks) operate. Of necessity, such understanding requires insight into structural, functional, and dynamical aspects of network operation, the intimate interweaving of which may be responsible for the intricacies of cognition. Knowledge of anatomical structure is basic to understanding how neurocognitive networks operate. Phylogenetically and ontogenetically determined patterns of synaptic connectivity form a structural network of brain areas, allowing communication between widely distributed collections of areas. The function of neurocognitive networks depends on selective activation of anatomically linked cortical and subcortical areas in a wide variety of configurations. Large-scale functional networks provide the cooperative processing which gives expression to cognitive function. The dynamics of neurocognitive network function relates to the evolving patterns of interacting brain areas that express cognitive function in real time. This article considers the proposition that a basic similarity of the structural, functional, and dynamical features of all neurocognitive networks in the brain causes them to function according to common operational principles. The formation of neural context through the coordinated mutual constraint of multiple interacting cortical areas, is considered as a guiding principle underlying all cognitive functions. Increasing knowledge of the operational principles of neurocognitive networks is likely to promote the advancement of cognitive theories, and to seed strategies for the enhancement of cognitive abilities.

This paper reports on a simple neurogenesis model that is combined with evolutionary computation. Since the integration of an evolutionary process with neural networks is such an exciting field of study, with the promise of discovering new computational models and, possibly, providing novel biological insights, much research has been conducted in this area. However, only a few studies have incorporated a development stage, and none have modeled metabolism and other chemical reactions in a consistent manner. In this paper, we present a simple model of neurogenesis and cell differentiation which combines evolutionary computing, metabolism, development, and neural networks. The model represents an evolutionary large-scale chaos as a mathematical foundation. An evolutionary large-scale chaos is a large-scale chaos whose map functions change through evolutionary computing. Experiments indicate that the model is capable of evolving and growing large neural networks, and exhibits phenomena an...

A simple model of self-organised learning with no classical (Hebbian) reinforcement is presented. Synaptic connections involved in mistakes are depressed. The model operates at a highly adaptive, probably critical, state reached by extremal dynamics similar to that of recent evolution models. Thus, one might think of the mechanism as synaptic Darwinism. It is widely believed that learning in the brain resides in alterations of synaptic efficacy. Without exception, contemporary formulations of such learning follows Hebb’s ideas [1] of reinforcement: synaptic connections among neurons excited during a a given firing pattern are strengthened by a process of long term potentiation (LTP). However, long term synaptic depression (LTD) in the mammalian brain is almost as prevalent as potentiation, but there appears to be little or no understanding of its functional role. Working hypotheses covers a wide range, where depression is given always an auxiliary function to potentiation [2]. A recent review [3], reflecting the current variety of ideas regarding the functional role of LTD, speculates: “Although it is conceivable that LTP is

The author reviews reemerging coconstructive conceptions of development and recent empirical findings of developmental plasticity at different levels spanning several fields of developmental and life sciences. A cross-level dynamic biocultural coconstructive framework is endorsed to understand cognitive and behavioral development across the life span. This framework integrates main conceptions of earlier views into a unifying frame, viewing the dynamics of life span development as occurring simultaneously within different time scales (i.e., moment-to-moment microgenesis, life span ontogeny, and human phylogeny) and encompassing multiple levels (i.e., neurobiological, cognitive, behavioral, and sociocultural). Viewed through this metatheoretical framework, new insights of potential interfaces for reciprocal cultural and experiential influences to be integrated with behavioral genetics and cognitive neuroscience research can be more easily prescribed. Metaphorically speaking, two related pendulums, one swinging back and forth from nature to nurture and the other from brain to mind, have been running the clocks of developmental and cognitive inquiries for centuries. Instead of polarizing toward either end, this review of recent advances in life and developmental sciences

We show that stochastic learning of attractors can take place in a situation in which either only potentiation or only depression of synaptic efficacies are caused in a structured Hebbian way. In each case the transition in the opposite sense take place at random, but occurs only upon presentation of a stimulus. The outcome is an associative memory with the palimpsest property. It is shown that structured potentiation produces more effective learning than structured depression, i.e. it creates a network with a much higher number of retrievable memories. Introduction Unsupervised learning of uncorrelated stimuli in attractor networks was recently described [1] as a stochastic process on the distribution of synaptic values characterizing the network. In this approach synapses have a finite number of stable states (efficacies). Learning is schematized as a random walk among them. Probability of each step is determined by the activities of the two neurons connected by the synapse. Neuron...

... This paper analyses abstract properties of selective systems to understand the evolutionary dynamics that occur within organisms. The Price Equation and Fisher's fundamental theorem of natural selection, two of the most powerful concepts in evolutionary genetics, are applied in a general way to internal selective systems in learning and development. This analysis emphasizes generative mechanisms and selective filters as genetically controlled phenotypes of individual organisms. Generative mechanisms create the variation on which selection acts. Selective filters determine the extent to which selection within the organism optimizes organismal performance. The methods of Price and Fisher provide a general way in which to partition evolutionary change into improvements caused by selection and the tendency of high performance variants to deteriorate because of competition or environmental change. This balance between selective improvement, at a rate equal to the variance in fitness, and a matching deterioration in performance, provides general insight into the common properties of adaptive systems in genetics, learning and development. These ideas are applied to a model of honey bee foraging. This example clarifies the relation between genes and phenotypes controlled by internal selective systems

Computers may be "smart" in terms of brute processing power, but their abilities to learn are limited to what can easily be programmed. A computer can indeed learn to solve new problems, but only ones that are quite similar to those it has already been programmed to solve. Computers cannot yet form new abstract representations, manipulate these representations, and integrate disparate knowledge (e.g., linguistic, contextual, emotional) to solve novel problems in ways managed by every normal young child. Even the Grey parrots (Psittacus erithacus) I study, with their evolutionary distance from humans, succeed on such tasks. How can parrots, with walnut-sized brains, succeed where a computer cannot? The birds' success likely arises for two reasons. First, a parrot, like a young child, does not rely exclusively on conditioned responses or simple associative learning, but has a repertoire of desires and purposes that cause it to form and test ideas about the world and how it can deal with and function in the world; these ideas, unlike simple associations or conditioned responses, can amount to representations of cognitive processing. Second, I hypothesize that their learning processes resemble those of young children because I have found that a social interaction paradigm is necessary to train the birds to communicate with us using the sounds of English speech. Because learning occurs more slowly in birds than humans, and is thus easier to study, I suggest that by deepening our understanding of the social processes whereby nonhumans advance from conditioned responses to representation-based learning we will uncover rules that can be adapted improve the ability of nonliving computational systems to perform advanced learning.

This paper series focusses on the intersection of neural networks and evolutionary computation. It is addressed to researchers from artificial intelligence as well as the neurosciences.

ABSTRACT The PC12 line of nerve growth factor (NGE)-responsive rat pheochromocytoma cells was used as a model system to determine whether properties of microtubules change during neurite growth and maturation. In the absence of NGF, PC12 cells lack processes. After several days with NGF, PC12 cells begin extending neurites and, by 2-3 wk with NGF, PC12 cells have long (~1 mm), highly branched neurites. We examined the effect of colchicine on microtubules of PC12 cells grown without NGF or with NGF for 1 or 21 d. PC12 cells grown under the various conditions were exposed to 50/~M colchicine for 1 or 6 h, and were then assayed for their content of polymerized tubulin using a biochemical assay. Microtubule levels in drug-treated cultures were compared to those in non-drug-treated control sister cultures. PC12 cells grown without NGF or with NGF for 1 d were depleted of MT by 1 h with colchicine. In contrast, microtubule levels in long-term NGF-treated cells exposed to colchicine for 6 h were reduced to only-57 % of those in control cells. Control experiments indicated that the observed differential susceptibility to colchicine was not due to differences in colchicine uptake or to the effects of colchicine on cell viability. These observations suggest that microtubules of PC12 cells grown without NGF or with NGF for 21 d differ in their properties. Such differences may be related to one or more of the changes in structure and/or motility that result from treatment with NGF. The determinants of neuronal morphology are present within the neuron as well as within its environment (35, 39). Several lines of evidence indicate that the cytoskeleton is the structural