Gerald M. Edelman

Reentry in nervous systems is the ongoing bidirectional exchange of signals along reciprocal axonal fibers linking two or more brain areas. The hypothesis that reentrant signaling serves as a general mechanism to couple the functioning of multiple areas of the cerebral cortex and thalamus was first proposed in 1977 and 1978 (Edelman, 1978). A review of the amount and diversity of supporting experimental evidence accumulated since then suggests that reentry is among the most important integrative mechanisms in vertebrate brains (Edelman, 1993). Moreover, these data prompt testable hypotheses regarding mechanisms that favor the development and evolution of reentrant neural architectures.

Animal behavior often involves a temporally ordered sequence of actions learned from experience. Here we describe simulations of interconnected networks of spiking neurons that learn to generate patterns of activity in correct temporal order. The simulation consists of large-scale networks of thousands of excitatory and inhibitory neurons that exhibit short-term synaptic plasticity and spike-timing dependent synaptic plasticity. The neural architecture within each area is arranged to evoke winner-take-all (WTA) patterns of neural activity that persist for tens of milliseconds. In order to generate and switch between consecutive firing patterns in correct temporal order, a reentrant exchange of signals between these areas was necessary. To demonstrate the capacity of this arrangement, we used the simulation to train a brain-based device responding to visual input by autonomously generating temporal sequences of motor actions.

Introduction Within the past few decades, the nature of consciousness has become a central issue in neuroscience, and it is increasingly the focus of both theoretical and empirical work. Studying consciousness is vital to developing an understanding of human perception and behavior, of our relationships with one another, and of our relationships with other potentially conscious animals. Although the study of consciousness through the construction of artificial models is a recent innovation, the advantages of such an approach are clear. First, models allow us to investigate consciousness in ways that are currently not feasible using human subjects or other animals. Second, an artifact that exhibits the necessary and sufficient properties of consciousness may conceivably be the forerunner of a new and very useful class of neuromorphic robots. A model of consciousness must take into account current theories of its biological bases. Although the field of artificial consciousness is a new one, it is striking how little attention has been given to modeling mechanisms. Instead, great – and perhaps undue – emphasis has been placed on purely phenomenological models. Many of these models are strongly reductionist in aim and fail to specify neural mechanisms.

Neuromorphic and brain-based robotics have enormous potential for furthering our understanding of the brain. By embodying models of the brain on robotic platforms, researchers can investigate the roots of biological intelligence and work towards the development of truly intelligent machines. This book provides a broad introduction to this groundbreaking area for researchers from a wide range of fields, from engineering to neuroscience. Case studies explore how robots are being used in current research, including a whisker system that allows a robot to sense its environment and neurally inspired navigation systems that show impressive mapping results. Looking to the future, several chapters consider the development of cognitive, or even conscious robots that display the adaptability and intelligence of biological organisms. Finally, the ethical implications of intelligent robots are explored, from morality and Asimov's three laws to the question of whether robots have rights.

The Dynamic Core and Global Workspace hypotheses were independently put forward to provide mechanistic and biologically plausible accounts of how brains generate conscious mental content. The Dynamic Core proposes that reentrant neural activity in the thalamocortical system gives rise to conscious experience. Global Workspace reconciles the limited capacity of momentary conscious content with the vast repertoire of long-term memory. In this paper we show the close relationship between the two hypotheses. This relationship allows for a strictly biological account of phenomenal experience and subjectivity that is consistent with mounting experimental evidence. We examine the constraints on causal analyses of consciousness and suggest that there is now sufficient evidence to consider the design and construction of a conscious artifact.

This paper presents an embodied approach to linking nervous system structure and function to behavior.

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 onin vitrolabeling 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 recordedin vitroin 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.

Biologically based mobile devices have been constructed that differ from robots based on artificial intelligence. These brain-based devices (BBDs) contain simulated brains that autonomously categorize signals from the environment without a priori instruction. Two such BBDs, Darwin VII and Darwin X, are described here. Darwin VII recognizes objects and links categories to behavior through instrumental conditioning. Darwin X puts together the “what,”“when,” and “where” from cues in the environment into an episodic memory that allows it to find a desired target. Although these BBDs are designed to provide insights into how the brain works, their principles may find uses in building hybrid machines. These machines would combine the learning ability of BBDs with explicitly programmed control systems.

The ribosome filter hypothesis postulates that ribosomes are not simply translation machines but also function as regulatory elements that differentially affect or filter the translation of particu...

Recent recordings of place field activity in rodent hippocampus have revealed correlates of current, recent past, and imminent future events in spatial memory tasks. To analyze these properties, we used a brain-based device, Darwin XI, that incorporated a detailed model of medial temporal structures shaped by experience-dependent synaptic activity. Darwin XI was tested on a plus maze in which it approached a goal arm from different start arms. In the task, a journey corresponded to the route from a particular starting point to a particular goal. During maze navigation, the device developed place-dependent responses in its simulated hippocampus. Journey-dependent place fields, whose activity differed in different journeys through the same maze arm, were found in the recordings of simulated CA1 neuronal units. We also found an approximately equal number of journey-independent place fields. The journey-dependent responses were either retrospective, where activity was present in the goal arm, or prospective, where activity was present in the start arm. Detailed analysis of network dynamics of the neural simulation during behavior revealed that many different neural pathways could stimulate any single CA1 unit. That analysis also revealed that place activity was driven more by hippocampal and entorhinal cortical influences than by sensory cortical input. Moreover, journey-dependent activity was driven more strongly by hippocampal influence than journey-independent activity.