PRINCIPLES OF NEURODYNAMICS. PERCEPTRONS AND THE THEORY OF BRAIN MECHANISMS

By F. Rosenblatt. 1963

Abstract : Part I attempts to review the background, basic sources of data, concepts, and methodology to be employed in the study of perceptrons. In Chapter 2, a brief review of the main alternative approaches to the development of brain models is presented. Chapter 3 considers the physiological and psychological criteria for a suitable model, and attempts to evaluate the empirical evidence which is available on several important issues. Chapter 4 contains basic definitions and some of the notation to be used in later sections are presented. Parts II and III are devoted to a summary of the established theoretical results obtained to date. Part II (Chapters 5 through 14) deals with the theory of three-layer series-coupled perceptrons, on which most work has been done to date. Part III (Chapters 15 through 20) deals with the theory of multi-layer and cross-coupled perceptrons. Part IV is concerned with more speculative models and problems for future analysis. Of necessity, the final chapters become increasingly heuristic in character, as the theory of perceptrons is not yet complete, and new possibilities are continually coming to light.

Subfields: Model Architectures

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Model Architectures

Citations in the corpus

Network In Network (2013, methodology): “ concept modeling can be achieved by replacing the conventional convolutional operation with a nonlinear network structure which we call “micro network”. In this work, we choose multilayer perceptron [4] as the instantiation of the micro network, which is a universal function approximator and a neur”
Relational inductive biases, deep learning, and graph networks (2018, background): “ processing them separately). 2.1 Relational inductive biases in standard deep learning building blocks 2.1.1 Fully connected layers Perhaps the most common building block is a fully connected layer (Rosenblatt, 1961). Typically implemented as a non-linear vector-valued function of vector inputs, ea”
Simplifying Graph Convolutional Networks (2019, background): “, Tianyi Zhang <tz58@cornell.edu>. linear Perceptron (Rosenblatt, 1958) motivated the development of the more complex but also more expressive neural network (or multi-layer Perceptrons, MLPs) (Rosenblatt, 1961).”
Learned-Norm Pooling for Deep Feedforward and Recurrent Neural Networks (2013, background): “A multi-layer perceptron (MLP) is a feedforward neural network consisting of multiple layers of nonlinear neurons [29].”
Kernel Methods for Relation Extraction (2002, methodology): “We next introduce a class of kernel machine learning methods and apply them to relation extraction.”
A Survey of Quantization Methods for Efficient Neural Network Inference (2021, neutral): “traced back to the seminal work of [199, 200], where an identity operator was used to approximate gradient from the binary neurons.”
Avoiding pathologies in very deep networks (2014, methodology): “To shed light on this problem, we analyze the analogous problem of constructing useful priors on compositions of functions.”
Adaptive and Self-Confident On-Line Learning Algorithms (2000, background): “An example is a pair (x;y) , where x 2 R n is called an instance , and y 2 R is the label associated with x .”
Recurrent convolutional neural network for object recognition (2015, background): “The main difference is that the full connections in RMLP are replaced by shared local connections, just as the difference between MLP [40] and CNN.”
Towards Probabilistic Verification of Machine Unlearning (2020, background): “Over the years, various network architectures have been proposed that have shown promise in different target applications – multilayer perceptrons [17], convolutional networks [18], and residual networks [19] that have achieved tremendous success in computer vision, long short-term memory (LSTM)…”
Optimal approximation of piecewise smooth functions using deep ReLU neural networks (2017, background): “in complex patterns. They were inspired by the architecture of the human brain and in that framework probably ﬁrst studied in 1943 in [42]. A special network model is that of a multi-layer perceptron [53, 50], which can, in mathematical terms, be understood as an alternating concatenation of aﬃne-li”