The neural cognitive architecture

The development of a cognitive architecture based on neurons is currently viable. An initial architecture is proposed, and is based around a slow serial system, and a fast parallel system, with additional subsystems for behaviours such as sensing, action and language. Current technology allows us to emulate millions of neurons in real time supporting the development and use of relatively sophisticated systems based on the architecture. While knowledge of biological neural processing and learning rules, and cognitive behaviour is extensive, it is far from complete. This architecture provides a slowly varying neural structure that forms the framework for cognition and learning. It will provide support for exploring biological neural behaviour in functioning animals, and support for the development of artificial systems based on neurons

Programming a cognitive architecture with simulated neurons, Chris Eliasmith. How to Build a Brain: A Neural Architecture for Biological Cognition. Oxford University Press, Oxford (2013). 456 pp., ISBN: 978-0-19-026212-9 [Book review]

This reviews Eliasmith's book

Variable binding by synaptic strength change

Variable binding is a difficult problem for neural networks. Two new mechanisms for binding by synaptic change are presented, and in both, bindings are erased and can be reused. The first is based on the commonly used learning mechanism of permanent change of synaptic weight, and the second on synaptic change which decays. Both are biologically motivated models. Simulations of binding on a paired association task are shown with the first mechanism succeeding with a 97.5% F-Score, and the second performing perfectly. Further simulations show that binding by decaying synaptic change copes with cross talk, and can be used for compositional semantics. It can be inferred that binding by permanent change accounts for these, but it faces the stability plasticity dilemma. Two other existing binding mechanism, synchrony and active links, are compatible with these new mechanisms. All four mechanisms are compared and integrated in a Cell Assembly theory

A neural cognitive architecture

It is difficult to study the mind, but cognitive architectures are one tool. As the mind emerges from the behaviour of the brain, neuropsychological methods are another method to study the mind, though a rather indirect method. A cognitive architecture that is implemented in spiking neurons is a method of studying the mind that can use neuropsychological evidence directly. A neural cognitive architecture, based on rule based systems and associative memory, can be readily implemented, and would provide a good bridge between standard cognitive architectures, such as \Soar, and neuropsychology. This architecture could be implemented in spiking neurons, and made available via the Human Brain Project, which provides a good collaborative environment. The architecture could be readily extended to use spiking neurons for subsystems, such as spatial reasoning, and could evolve over time toward a complete architecture. The theory behind this architecture could evolve over time. Simplifying assumptions, made explicit, such as those behind the rule based system, could gradually be replaced by more neuropsychologically accurate behaviour. The overall task of collaborative architecture development would be eased by direct evidence of the actual neural cognitive architectures in human brains. While the initial architecture is biologically inspired, the ultimate goal is a biological cognitive architecture

Neural constraints and flexibility in language processing

Humans process language with their neurons. Memory in neurons is supported by neural firing, and short and long-term synaptic weight change; the emergent behaviour of neurons, synchronous firing and cell assembly dynamics, is also a form of memory. As the language signal moves to later stages, it is processed with different mechanisms that are slower but more persistent

Dialogue based interfaces for universal access.

Conversation provides an excellent means of communication for almost all people. Consequently, a conversational interface is an excellent mechanism for allowing people to interact with systems. Conversational systems are an active research area, but a wide range of systems can be developed with current technology. More sophisticated interfaces can take considerable effort, but simple interfaces can be developed quite rapidly. This paper gives an introduction to the current state of the art of conversational systems and interfaces. It describes a methodology for developing conversational interfaces and gives an example of an interface for a state benefits web site. The paper discusses how this interface could improve access for a wide range of people, and how further development of this interface would allow a larger range of people to use the system and give them more functionality

Learning categories with spiking nets and spike timing dependent plasticity

An exploratory study of learning a neural network for categorisation shows that commonly used leaky integrate and fire neurons and Hebbian learning can be effective. The system learns with a standard spike timing dependent plasticity Hebbian learning rule. A two layer feed forward topology is used with a presentation mechanism of inputs followed by outputs a simulated ms. later to learn Iris flower and Breast Cancer Tumour Malignancy categorisers. An exploration of parameters indicates how this may be applied to other tasks

Processing with cell assemblies

Cell assemblies (CAs) were posited by Hebb almost 60 years ago as the unit of representation in the brain. Recent results in the field of neuroscience indicate that CAs are likely to exist, at least in the mammalian brain. The CABot project uses simulations of CAs formed from individual neurons as a basis for learning and behaviour. This paper proves that a network of CAs, as described by Hebb and as implemented in CABot, is complete with respect to structured program theory. It follows that it is possible to implement the fundamental operations of program execution in a biological network

Cell Assembly-based Task Analysis (CAbTA)

Based on an Artificial Neural Network model, Cell Assembly-based Task Analysis is a new method that outputs a task performance model composed of integrated mind-brain Cell Assemblies, which are currently believed to be the most plausible, general organisation of the brain and how it supports mental operations. A simplified model of Cell Assemblies and their cognitive architecture is described and then used in the method. A brief sub-task is analysed. The method’s utility to research in Artificial Intelligence, neuroscience and cognitive psychology is discussed and the possibility of a General Theory suggested

Emergence of rules in cell assemblies of fLIF neurons.

Inspired by biological cognition, CABOT project explores the ways symbolic processing can emerge in a system of neural cell assemblies (CAs). Here we show how a stochastic meta–control process can regulate learning of associations between the CAs, the neural basis of symbols. An experiment illustrates the learning between CAs representing conditions actions pairs, which leads to CA–based representations of ‘if–then’ rules