Gerald M. Edelman

Degeneracy and complexity in biological systems

Degeneracy, the ability of elements that are structurally different to perform the same function or yield the same output, is a well known characteristic of the genetic code and immune systems. Here, we point out that degeneracy is a ubiquitous biological property and argue that it is a feature of complexity at genetic, cellular, system, and population levels. Furthermore, it is both necessary for, and an inevitable outcome of, natural selection.

Large-scale model of mammalian thalamocortical systems

The understanding of the structural and dynamic complexity of mammalian brains is greatly facilitated by computer simulations. We present here a detailed large-scale thalamocortical model based on experimental measures in several mammalian species. The model spans three anatomical scales. (i) It is based on global (white-matter) thalamocortical anatomy obtained by means of diffusion tensor imaging (DTI) of a human brain. (ii) It includes multiple thalamic nuclei and six-layered cortical microcircuitry based on in vitro labeling and three-dimensional reconstruction of single neurons of cat visual cortex. (iii) It has 22 basic types of neurons with appropriate laminar distribution of their branching dendritic trees. The model simulates one million multicompartmental spiking neurons calibrated to reproduce known types of responses recorded in vitro in rats. It has almost half a billion synapses with appropriate receptor kinetics, short-term plasticity, and long-term dendritic spike-timing-dependent synaptic plasticity (dendritic STDP). The model exhibits behavioral regimes of normal brain activity that were not explicitly built-in but emerged spontaneously as the result of interactions among anatomical and dynamic processes. We describe spontaneous activity, sensitivity to changes in individual neurons, emergence of waves and rhythms, and functional connectivity on different scales.

Complexity and coherency: integrating information in the brain

The brains of higher mammals are extraordinary integrative devices. Signals from large numbers of functionally specialized groups of neurons distributed over many brain regions are integrated to generate a coherent, multimodal scene. Signals from the environment are integrated with ongoing, patterned neural activity that provides them with a meaningful context. We review recent advances in neurophysiology and neuroimaging that are beginning to reveal the neural mechanisms of integration. In addition, we discuss concepts and measures derived from information theory that lend a theoretical basis to the notion of complexity as integration of information and suggest new experimental tests of these concepts.

Connectivity and complexity: the relationship between neuroanatomy and brain dynamics

Nervous systems facing complex environments have to balance two seemingly opposing requirements. First, there is a need quickly and reliably to extract important features from sensory inputs. This is accomplished by functionally segregated (specialized) sets of neurons, e.g. those found in different cortical areas. Second, there is a need to generate coherent perceptual and cognitive states allowing an organism to respond to objects and events, which represent conjunctions of numerous individual features. This need is accomplished by functional integration of the activity of specialized neurons through their dynamic interactions. These interactions produce patterns of temporal correlations or functional connectivity involving distributed neuronal populations, both within and across cortical areas. Empirical and computational studies suggest that changes in functional connectivity may underlie specific perceptual and cognitive states and involve the integration of information across specialized areas of the brain. The interplay between functional segregation and integration can be quantitatively captured using concepts from statistical information theory, in particular by defining a measure of neural complexity. Complexity measures the extent to which a pattern of functional connectivity produced by units or areas within a neural system combines the dual requirements of functional segregation and integration. We find that specific neuroanatomical motifs are uniquely associated with high levels of complexity and that such motifs are embedded in the pattern of long-range cortico-cortical pathways linking segregated areas of the mammalian cerebral cortex. Our theoretical findings offer new insight into the intricate relationship between connectivity and complexity in the nervous system.

Neural Darwinism: Selection and reentrant signaling in higher brain function

Variation and selection within neural populations play key roles in the development and function of the brain. In this article, I review a population theory of the nervous system aimed at understanding the significance of these processes. Since its original formulation in 1978, considerable evidence has accumulated to support this theory of neuronal group selection. Extensive neural modeling based on the theory has provided useful insights into several outstanding neurobiological problems including those concerned with integration of cortical function, sensorimotor control, and perceptually based behavior.

Naturalizing consciousness: A theoretical framework

Consciousness has a number of apparently disparate properties, some of which seem to be highly complex and even inaccessible to outside observation. To place these properties within a biological framework requires a theory based on a set of evolutionary and developmental principles. This paper describes such a theory, which aims to provide a unifying account of conscious phenomena.

Isolation and characterization of a hemagglutinin from Limulus polyphemus.

A natural hemagglutinin from the hemolymph of the horseshoe crab Limulus polyphemus has been isolated and partially characterized. The hemagglutinin is a protein having a molecular weight of approximately 400,000 and an S20,w0 = 13.5 s. It consists of subunits having a molecular weight of 22,500 which are linked through non-covalent interactions. The agglutinating activity is potentiated by calcium ions. Starch gel electrophoresis, amino acid analysis, two-dimensional high-voltage electrophoresis of tryptic peptides, and immunological analysis were performed on the purified hemagglutinin. The data indicate that the hemagglutinin structure differs in many respects from that of vertebrate immunoglobulins.

VALUE-DEPENDENT SELECTION IN THE BRAIN: SIMULATION IN A SYNTHETIC NEURAL MODEL

Many forms of learning depend on the ability of an organism to sense and react to the adaptive value of its behavior. Such value, if reflected in the activity of specific neural structures (neural value systems), can selectively increase the probability of adaptive behaviors by modulating synaptic changes in the circuits relevant to those behaviors. Neuromodulatory systems in the brain are well suited to carry out this process since they respond to evolutionarily important cues (innate value), broadcast their responses to widely distributed areas of the brain through diffuse projections, and release substances that can modulate changes in synaptic strength. The main aim of this paper is to show that, if value-dependent modulation is extended to the inputs of neural value systems themselves, initially neutral cues can acquire value. This process has important implications for the acquisition of behavioral sequences. We have used a synthetic neural model to illustrate value-dependent acquisition of a simple foveation response to a visual stimulus. We then examine the improvement that ensues when the connections to the value system are themselves plastic and thus become able to mediate acquired value. Using a second-order conditioning paradigm, we demonstrate that auditory discrimination can occur in the model in the absence of direct positive reinforcement and even in the presence of slight negative reinforcement. The discriminative responses are accompanied by value-dependent plasticity of receptive fields, as reflected in the selective augmentation of unit responses to valuable sensory cues. We then consider the time-course during learning of the responses of the value system and the transfer of these responses from one sensory modality to another. Finally, we discuss the relation of value-dependent learning to models of reinforcement learning. The results obtained from these simulations can be directly related to various reported experimental findings and provide additional support for the application of selectional principles to the analysis of brain and behavior.

Role of nitric oxide in NMDA-evoked release of [3H]-dopamine from striatal slices

Evidence that excitatory amino acids act via N-methyl-D-aspartate (NMDA) receptors to evoke the release of catecholamines from axonal terminals and synaptosomes has been used to argue for the presence of pre-synaptic NMDA receptors. NMDA receptor agonists also generate nitric oxide (NO) which rapidly diffuses through neural tissue. We find that exogenously applied NO evokes [3H]-dopamine release from cultured neurons. This release is not blocked by the NMDA antagonist MK-801 nor by tetrodotoxin. Both NG-nitroarginine which inhibits NO synthesis, and hemoglobin which binds extracellular NO, block NMDA-evoked [3H]-dopamine release from striatal slices. A major role of endogenously-synthesized NO may be to evoke neurotransmitter release in local volumes of neural tissue.

The brain-derived neurotrophic factor enhances synthesis of Arc in synaptoneurosomes

Protein synthesis in neurons is essential for the consolidation of memory and for the stabilization of activity-dependent forms of synaptic plasticity such as long-term potentiation (LTP). Activity-dependent translation of dendritically localized mRNAs has been proposed to be a critical source of new proteins necessary for synaptic change. mRNA for the activity-regulated cytoskeletal protein, Arc, is transcribed during LTP and learning, and disruption of its translation gives rise to deficits in both. We have found that selective translation of Arc in a synaptoneurosomal preparation is induced by the brain-derived neurotrophic factor, a neurotrophin that is released during high-frequency stimulation patterns used to elicit LTP. This effect involves signaling through the TrkB receptor and is blocked by the N-methyl-d-aspartate-type glutamate receptor antagonist, MK801. The results suggest there is a synergy between neurotrophic and ionotropic mechanisms that may influence the specificity and duration of changes in synaptic efficacy at glutamatergic synapses.