A neural cognitive architecture

doi:10.1016/j.cogsys.2019.09.023

Cognitive Systems Research

Volume 59, January 2020, Pages 171-178

https://doi.org/10.1016/j.cogsys.2019.09.023 Get rights and content

Abstract

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.

Introduction

The mind is extraordinarily complex, and to understand it sophisticated tools are needed. One set of tools for studying the human mind, which emerges from the behaviour of the brain, is cognitive architectures. A cognitive architecture is the fixed or slowly varying structure that forms the framework for the immediate processes of cognitive performance and learning (Newell et al., 1990). This paraphrase is a cornerstone of research in cognitive architectures, and is consistent with many architectures (see Section 2.1).

Human cognition is based on the behaviour of neurons. While Newell et al. (1990) based their architecture on a rule based system, he also partitioned cognition into several bands including the neural band. The coarse topology of neurons is slowly varying, so the coarse human neural topology is a cognitive architecture, a neural cognitive architecture.

One of the main goals of the Human Brain Project (HBP) is to devise a complete simulation of the human brain (Markram, 2012). Simulation can happen using a variety of primitives, but the HBP is particularly interested in spiking point neuron models. This simulation will then be used to help decode the function of the human brain (Amunts et al., 2016).

Cognitive architectures are theories of how the mind works, but as typically used they are formal languages that can be used to develop new models that can be executed on a standard computer. Systems written in the architecture’s language are then run and this behaviour is a model of cognitive behaviour. Each of these written systems instantiate the architecture, though no instance to date (or in the foreseeable future) instantiates a full model of a human. As these instances are executable programs, they are verifiable.

The architecture develops over time to become more effective and more closely approximate cognitive behaviour with new versions extending capabilities and adding constraints. Systems developed in these new frameworks more accurately reflect human cognitive behaviour.

This paper will propose a neural cognitive architecture. The proposal will include a sketch of an initial version of the architecture. This will include a development language, making use of spiking point neurons, that could be used in the near future in simulated spiking neurons on, for instance, the HBP’s EBrains platforms. The paper will then propose ways to move forward from the initial architecture to develop more effective versions that more closely approximate neural and cognitive behaviour.

Section snippets

Literature review

Cognitive architectures have been a research area for at least thirty years, and have been productive for furthering understanding of the mind, and for practical applications of agents, task analysis, and cognitive modelling. More recently, neural cognitive architectures have been proposed (see Section 2.2); these show that neurons are capable flexible processing and memory devices. The HBP is a large interdisciplinary project with a primary goal of simulating the entire brain using spiking

The proposed architecture

The long term goal of the proposed architecture is to build a neural network that closely approximates a human architecture in both neural detail and cognitive function. This raises at least three major questions. 1. What is the basic neural unit? 2. How are those units connected? 3. How does that neural topology generate cognitive function?

There are many neural models including simple rate coded neurons, point models and complex compartmental models (Brette et al., 2007). What is the best

Architecture evolution and exploration

The initial architecture is obviously far from a complete architecture. It will need to develop including more refined neural systems that more closely approximate cognitive function. The author does not want to prejudge this evolution, but would like to propose some plausible future steps. Moreover, others are encouraged in participating in the development of this architecture.

One simple neural mechanism to improve is to have better CAs. CAs have several behaviours, implied by biological,

Conclusions

It is difficult to develop systems in neurons that perform tasks. An instantiated complete neural cognitive architecture is an enormous task. There are, however, known features of neural behaviour that are important that can simplify the search. This paper advocates that the neural cognitive architecture make explicit assumptions and note the evidence behind these assumptions. Almost necessarily, systems developed in the near future will not be complete brains as it is difficult to simulate all

Declaration of Competing Interest

None.

Acknowledgements

This work was supported by This work has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 720270 (the Human Brain Project).

References (40)

K. Amunts et al.
The human brain project: creating a european research infrastructure to decode the human brain
Neuron
(2016)
R. Brooks
Intelligence without representation
Artificial Intelligence
(1991)
E. Byrne et al.
Processing with cell assemblies
Neurocomputing
(2010)
S. Harnad
The symbol grounding problem
Physica D
(1990)
J. Laird et al.
Soar: An architecture for general cognition
Artificial Intelligence
(1987)
E. Miller et al.
Working memory 2.0
Neuron
(2018)
F. Pulvermuller
Neural reuse of action perception circuits for language, concepts and communication
Progress in Neurobiology
(2018)
J. Anderson et al.
The atomic components of thought
(1998)
A. Bicanski et al.
A neural-level model of spatial memory and imagery
ELife
(2018)
G. Bi et al.
Synaptic modifications in cultured hippocampal neurons: Dependence on spike timing, synaptic strength, and postsynaptic cell type
Journal of Neuroscience
(1998)

R. Brette et al.

Simulation of networks of spiking neurons: A review of tools and strategies

Journal of Computational Neuroscience

(2007)

N. Carnevale et al.

The NEURON book

(2006)

Davison, A., Brüderle, D., Eppler, J., Muller, E., Pecevski, D., Perrinet, L. & Yqer, P. (2009). PyNN: A common...

C. Eliasmith et al.

A large-scale model of the functioning brain

Science

(2012)

J. Feldman

The neural binding problem (s)

Cognitive Neurodynamics

(2013)

S. Furber et al.

Overview of the SpiNNaker system architecture

IEEE Transactions on Computers

(2013)

M. Gewaltig et al.

NEST (neural simulation tool)

Scholarpedia

(2007)

R. Granger

Engines of the brain: The computational instruction set of human cognition

AI Magazine

(2006)

D.O. Hebb

The organization of behavior: A neuropsychological theory

(1949)

M. Helias et al.

Supercomputers ready for use as discovery machines for neuroscience

Frontiers in Neuroinformatics

(2012)

Cited by (10)

An amalgamation of cognitive aspects in software engineering: A content analysis
2024, Expert Systems with Applications
Understanding the way, developers perform different software development and management tasks with objective measures can support improving potency and performance. Exploring the cognitive skills of programmers in software engineering has been the focus of immense and ongoing research for beyond 30 years. This work aims to combine and analyze different cognitive aspects such as psychology, understanding of program comprehension, and emotions of developers in the software engineering field, by conducting a Systematic Literature Review (SLR). We collected more than 300 articles from 7 major digital libraries and narrowed them down by selecting the 80 most relevant articles. Moreover, we framed six research questions and justified each question with the help of tables and graphs. Furthermore, we generated a classification scheme for each article, based on the justification of 10 quality assessment queries. Our analysis based on research questions shows that most of the research articles (12/80) published in the field of cognitive aspects are after the year 2017. In our SLR 39/80 articles are published in reputed journals. To measure developers’ cognitive abilities, 48/80 studies used particular bio measures. Most of the articles (19/80) employed student datasets in their experiments. In the case of the research methodology used, 23/80 of the articles are validation studies and 22/80 are experimental lab studies. In exploring the sensors, 37 % of the studies in which sensors were used to analyze cognitive features have applied multimodal sensors, while the rest of the studies used individual sensors according to their requirement such as Eye trackers, fMRI, fNIRS, etc. Exploring the range of the participants engaged in the experiments in various studies, 18 % of the total studies involved participants in the range of 11–20 and 27 % of studies did not mention the number of participants. The research implications of this review work will be the assessment of the understanding level of a programmer while developing or comprehending a program. This combined analysis will be helpful for the professionals or field experts to perform a better analysis of different cognitive features of developers in the software engineering field and for that analysis, they will be able to select more optimal bio measures, sensors, and methodologies over different conditions.
Bio-inspired computational object classification model for object recognition
2022, Cognitive Systems Research
Citation Excerpt :
Cognitive Architectures (CAs) are approaches that model the behavior of intelligent agents by dividing the cognitive process into defined parts through the integration of various functional parts for the purpose of achieving certain cognitive behaviors. The structure of a cognitive architecture depends on the approach that inspires it by (Cervantes et al., 2020; Huyck, 2020). Since this model is part of a bio-inspired cognitive architecture (Jaime et al., 2015), it is important to mention how different CAs try to solve the problem of object recognition, as the process of recognition can involve different types of cognitive functions.
Human beings can effortlessly perceive stimuli through their sensory systems to learn, understand, recognize and act on our environment or context. Over the years, efforts have been made to enable cybernetic entities to be close to performing human perception tasks; and in general, to bring artificial intelligence closer to human intelligence.
Neuroscience and other cognitive sciences provide evidence and explanations of the functioning of certain aspects of visual perception in the human brain. Visual perception is a complex process, and its has been divided into several parts. Object classification is one of those parts; it is necessary for carrying out the declarative interpretation of the environment. This article deals with the object classification problem.
In this article, we propose a computational model of visual classification of objects based on neuroscience, it consists of two modular systems: a visual processing system, in charge of the extraction of characteristics; and a perception sub-system, which performs the classification of objects based on the features extracted by the visual processing system.
With the results obtained, a set of aspects are analyzed using similarity and dissimilarity matrices. Also based on the neuroscientific evidence and the results obtained from this research, some aspects are suggested for consideration to improve the work in the future and bring us closer to performing the task of visual classification as humans do.
Involuntary, Limited, and Contiguously Repeating Musical Imagery (InLaCReMI): Reconciling Theory and Data on the Musical Material Acquired by Earworms
2023, Music and Science
Unexpected Aspects of Expectancy in Music: A Spreading Activation Explanation
2023, Psihologijske Teme
The architecture of non-local semantics for artificial general intelligence
2022, International Journal of Applied Systemic Studies
Algorithms for monitoring the functioning of nonequilibrium information processing systems
2021, Journal of Physics: Conference Series

View all citing articles on Scopus

View full text