A Neuro-mimetic Realization of the Common Model of Cognition via Hebbian
Learning and Free Energy Minimization
- URL: http://arxiv.org/abs/2310.15177v2
- Date: Sat, 4 Nov 2023 03:23:20 GMT
- Title: A Neuro-mimetic Realization of the Common Model of Cognition via Hebbian
Learning and Free Energy Minimization
- Authors: Alexander Ororbia, Mary Alexandria Kelly
- Abstract summary: Large neural generative models are capable of synthesizing semantically rich passages of text or producing complex images.
We discuss the COGnitive Neural GENerative system, such an architecture that casts the Common Model of Cognition.
- Score: 55.11642177631929
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Over the last few years, large neural generative models, capable of
synthesizing semantically rich passages of text or producing complex images,
have recently emerged as a popular representation of what has come to be known
as ``generative artificial intelligence'' (generative AI). Beyond opening the
door to new opportunities as well as challenges for the domain of statistical
machine learning, the rising popularity of generative AI brings with it
interesting questions for Cognitive Science, which seeks to discover the nature
of the processes that underpin minds and brains as well as to understand how
such functionality might be acquired and instantianted in biological (or
artificial) substrate. With this goal in mind, we argue that a promising
research program lies in the crafting of cognitive architectures, a
long-standing tradition of the field, cast fundamentally in terms of
neuro-mimetic generative building blocks. Concretely, we discuss the COGnitive
Neural GENerative system, such an architecture that casts the Common Model of
Cognition in terms of Hebbian adaptation operating in service of optimizing a
variational free energy functional.
Related papers
- Brain-like Functional Organization within Large Language Models [58.93629121400745]
The human brain has long inspired the pursuit of artificial intelligence (AI)
Recent neuroimaging studies provide compelling evidence of alignment between the computational representation of artificial neural networks (ANNs) and the neural responses of the human brain to stimuli.
In this study, we bridge this gap by directly coupling sub-groups of artificial neurons with functional brain networks (FBNs)
This framework links the AN sub-groups to FBNs, enabling the delineation of brain-like functional organization within large language models (LLMs)
arXiv Detail & Related papers (2024-10-25T13:15:17Z) - Exploring Biological Neuronal Correlations with Quantum Generative Models [0.0]
We introduce a quantum generative model framework for generating synthetic data that captures the spatial and temporal correlations of biological neuronal activity.
Our model demonstrates the ability to achieve reliable outcomes with fewer trainable parameters compared to classical methods.
arXiv Detail & Related papers (2024-09-13T18:00:06Z) - Simulation of Neural Responses to Classical Music Using Organoid Intelligence Methods [0.0]
Organoid intelligence and deep learning models show promise for simulating and analyzing neural responses to classical music.
We present the PyOrganoid library, an innovative tool that facilitates the simulation of organoid learning models.
arXiv Detail & Related papers (2024-07-25T22:11:30Z) - Enhancing learning in spiking neural networks through neuronal heterogeneity and neuromodulatory signaling [52.06722364186432]
We propose a biologically-informed framework for enhancing artificial neural networks (ANNs)
Our proposed dual-framework approach highlights the potential of spiking neural networks (SNNs) for emulating diverse spiking behaviors.
We outline how the proposed approach integrates brain-inspired compartmental models and task-driven SNNs, bioinspiration and complexity.
arXiv Detail & Related papers (2024-07-05T14:11:28Z) - Brain-Inspired Machine Intelligence: A Survey of
Neurobiologically-Plausible Credit Assignment [65.268245109828]
We examine algorithms for conducting credit assignment in artificial neural networks that are inspired or motivated by neurobiology.
We organize the ever-growing set of brain-inspired learning schemes into six general families and consider these in the context of backpropagation of errors.
The results of this review are meant to encourage future developments in neuro-mimetic systems and their constituent learning processes.
arXiv Detail & Related papers (2023-12-01T05:20:57Z) - CogNGen: Constructing the Kernel of a Hyperdimensional Predictive
Processing Cognitive Architecture [79.07468367923619]
We present a new cognitive architecture that combines two neurobiologically plausible, computational models.
We aim to develop a cognitive architecture that has the power of modern machine learning techniques.
arXiv Detail & Related papers (2022-03-31T04:44:28Z) - Spatiotemporal Patterns in Neurobiology: An Overview for Future
Artificial Intelligence [0.0]
We argue that computational models are key tools for elucidating possible functionalities that emerge from network interactions.
Here we review several classes of models including spiking neurons, integrate and fire neurons.
We hope these studies will inform future developments in artificial intelligence algorithms as well as help validate our understanding of brain processes.
arXiv Detail & Related papers (2022-03-29T10:28:01Z) - Natural Computational Architectures for Cognitive Info-Communication [3.3758186776249928]
Recent comprehensive overview of 40 years of research in cognitive architectures, (Kotseruba and Tsotsos 2020), evaluates modelling of the core cognitive abilities in humans, but only marginally addresses biologically plausible approaches based on natural computation.
We use evolutionary info-computational framework, where natural/ physical/ morphological computation leads to evolution of increasingly complex cognitive systems.
arXiv Detail & Related papers (2021-10-01T18:01:16Z) - Towards a Predictive Processing Implementation of the Common Model of
Cognition [79.63867412771461]
We describe an implementation of the common model of cognition grounded in neural generative coding and holographic associative memory.
The proposed system creates the groundwork for developing agents that learn continually from diverse tasks as well as model human performance at larger scales.
arXiv Detail & Related papers (2021-05-15T22:55:23Z) - A brain basis of dynamical intelligence for AI and computational
neuroscience [0.0]
More brain-like capacities may demand new theories, models, and methods for designing artificial learning systems.
This article was inspired by our symposium on dynamical neuroscience and machine learning at the 6th Annual US/NIH BRAIN Initiative Investigators Meeting.
arXiv Detail & Related papers (2021-05-15T19:49:32Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.