Plasticity: Cells, Circuits, Brains, and Behavior

This chapter examines the physical mechanisms in nervous systems in order to elucidate the structural bases and functional principles of synaptic plasticity. Neuroscientific research on plasticity can be divided into four main streams: the neural mechanism for relatively simple kinds of plasticity, such as classical conditioning or habituation; anatomical and physiological studies of temporal lobe structures, including the hippocampus and the amygdala; study of the development of the visual system; and the relation between the animal's genes and the development of its nervous system. The chapter first considers the role of the mammalian hippocampus in learning and memory before discussing Donald Hebb's views on synaptic plasticity. It then explores the mechanisms underlying neuronal plasticity and those that decrease synaptic strength, the relevance of time with respect to plasticity, and the occurrence of plasticity during the development of the nervous system. It also describes modules, modularity, and networks in the brain.

Concluding and Beyond

This concluding chapter explores future avenues for research and what remains to be done if the computer-modeling projects aimed at understanding the mysteries of the brain are to progress. In particular, it considers the problem of constructing synthetic brains and the reasons why the long-range project of understanding how the brain works should engender such constructive ambitions. It also discusses three ways of addressing the constructive problem: Carver Mead's strategy of building artificial neural structures, such as retinas and cochleas, using silicon-based CMOS VLSI technology; Dana Ballard's method of integrating perception with motor control; and Rodney Brook's method which involves making mobots capable of getting around in the world using limited reflex repertoires. The chapter concludes with an assessment of theoretical and ethical questions about what to do with the knowledge gained from computational neuroscience.

Sensorimotor Integration

This chapter examines the mechanisms underlying sensorimotor integration by discussing three cases where anatomical and physiological studies of circuits are co-evolving with computer models of the circuit: the first focuses on the dorsal bending reflex in the leech, the second deals with the vestibulo-ocular reflex (VOR) in mammals, and the third is concerned with rhythmic behaviors generated by the spinal cord. The chapter explains each case in detail, beginning with the computational model for the local bending reflex in the leech and proceeding with a discussion of a model network incorporating known pathways, connections, and physiology of the VOR. It also considers how time is represented in nervous systems and describes the segmental swimming oscillator before concluding with an overview of modeling of neurons.

Introduction

This book introduces a conceptual framework for brain function based on large populations of neurons. It advances the hypothesis that emergent properties are high-level effects that depend on lower-level phenomena in some systematic way, drawing on the idea that brains are computational in nature. Areas and topics related to computational neuroscience covered in this book include computational mechanisms in neurons, analysis of signal processing in neural circuits, representation of sensory information, systems models of sensorimotor integration, and computational approaches to plasticity. The book emphasizes the importance of single neuron models as the foundation into which network models must eventually fit. It also provides a background discussion on neuroscience and the science of computation.

Representing the World

This chapter explores the neurobiology of representations, and more specifically how nervous systems represent in the occurrent sense. It first provides an overview of the basic anatomy and physiology of the mammalian visual system before discussing how neurons encode information by drawing on the comparison between “grandmother” coding and distributed coding. In particular, it considers vector coding vs. local coding and the conceptual fecundity of “state space,” along with the question of whether nervous systems honor at all the distinction between vectors of activation and matrices for processing. The chapter proceeds by analyzing the shape-from-shading model, computational models of stereo vision, hyperacuity, and vector averaging.

Computational Overview

This chapter provides an overview of computational principles that may be useful when addressing the question of computation in nervous systems as well as questions of biological systems. It begins by introducing several key mathematical concepts, including “function,” and the distinction between computable and noncomputable functions, and between linear and nonlinear functions. It then considers a number of computational principles, such as the look-up table and linear associators, before discussing a new type of principle that can accomplish the satisfaction of constraints by a process of “relaxation.” In particular, it describes Hopfield networks and Boltzmann machines. It also examines learning in neural nets, competitive learning, curve fitting, feedforward nets, and recurrent nets. Finally, it assesses the importance of optimization procedures to neuroscience, along with the use of realistic and abstract network models in neuroscience.

Neuroscience Overview

This chapter provides an overview of the “neuroscience” component of the “computational neuroscience” synergy. It begins with a discussion of three ideas about levels in nervous systems: levels of analysis, levels of organization, and levels of processing. Levels of organization are essentially anatomical, and refer to a hierarchy of components and to structures that comprise these components. Levels of processing are physiological, and refer to the location of a process relative to the transducers and muscles. Levels of analysis are conceptual, and refer to different kinds of questions asked about how the brain performs a task. The chapter proceeds by considering seven categories of structural organization in nervous systems: systems, topographic maps, layers and columns, local networks, neurons, synapses, and molecules. It concludes by presenting a short list of brain facts.