Corticonics: Neural Circuits of the Cerebral CortexUnderstanding how the brain works is probably the greatest scientific and intellectual challenge of our generation. The cerebral cortex is the instrument by which we carry the most complex mental functions. Fortunately, there exists an immense body of knowledge concerning both cortical structure and the properties of single neurons in the cortex. With the advent of the supercomputer, there has been increased interest in neural network modeling. What is needed is a new approach to an understanding of the mammalian cerebral cortex that will provide a link between the physiological description and the computer model. This book meets that need by combining anatomy, physiology, and modeling to achieve a quantitative description of cortical function. The material is presented didactically, starting with descriptive anatomy and comprehensively examining all aspects of modeling. The book gradually leads the reader from the macroscopic cortical anatomy and standard electrophysiological properties of single neurons to neural network models and synfire chains. The most modern trends in neural network modeling are explored. |
Contents
Anatomy of the cerebral cortex | 1 |
12 Types of neurons | 6 |
13 Microscopic organization | 18 |
14 Embryogenesis of the cerebral cortex | 40 |
15 Quantitative considerations | 49 |
16 References | 59 |
The probability for synaptic contact between neurons in the cortex | 65 |
21 Uniform distribution | 66 |
46 References | 147 |
Models of neural networks | 150 |
52 The point neuron | 152 |
53 Small random networks | 153 |
54 Large random networks | 160 |
55 Recurrent cooperative networks | 173 |
56 Perceptrons | 193 |
203 | |
22 Nonuniform distribution | 79 |
23 Examples | 82 |
24 Generalization | 87 |
25 References | 90 |
Processing of spikes by neural networks | 92 |
32 Transmission of a single spike through a synapse | 96 |
33 Using the ASG | 101 |
34 Transmission of a spike train | 110 |
35 Examples | 113 |
117 | |
Relations between membrane potential and the synaptic response curve | 118 |
42 Randomly firing neurons | 120 |
43 The autocorrelation function for spike trains | 133 |
44 Synaptic effects on periodically firing neurons | 136 |
45 Synaptic effects on randomly firing neurons | 139 |
Transmission through chains of neurons | 208 |
62 Existence of divergingconverging connections | 212 |
63 Transmission through divergingconverging chains | 221 |
64 Appendix | 225 |
65 References | 226 |
Synchronous transmission | 227 |
72 Synfire chains | 232 |
73 Stability of transmission in synfire chains | 235 |
74 Accuracy of timing | 241 |
75 Conversion from synchronous activity to asynchronous activity and vice versa | 249 |
76 Detectability of synfire activity in the cortex | 254 |
77 References | 258 |
Answers and hints | 260 |
277 | |
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Common terms and phrases
action potential apical dendrite arrive autocorrelation function average axonal range basket cells behavior branches Cajal-Retzius cells cell body cells fire cerebral cortex compute cooperative networks cortical areas cortical cells cortical neurons cortical regions cross-correlation depolarization described discontent distributed diverging/converging chain diverging/converging connections effects EPSP equation Example excitation excitatory neurons excitatory synapses Exercise expected number firing neurons firing rate fluctuations Golgi inhibitory neurons jitter layer Let us assume Martinotti cells membrane potential motoneurons multilayered perceptrons multiplicity netlet neural networks neuroblasts neuron fires number of synapses obtain output percent perceptron postsynaptic cell postsynaptic neuron probability of contact probability of finding pyramidal cells receiving node refractory Section sending node shown in Figure smooth stellate cells spike train spikes per second spiny stellate cells stable stained stellate cells synaptic contacts synaptic delay synaptic potentials synaptic strength synfire activity synfire chain threshold tion total number updating visual cortex white matter zero