Neuropsychology of AI: Relationship Between Activation Proximity and Categorical Proximity Within Neural Categories of Synthetic Cognition

• URL: http://arxiv.org/abs/2410.11868v1
• Date: Tue, 08 Oct 2024 12:34:13 GMT
• Title: Neuropsychology of AI: Relationship Between Activation Proximity and Categorical Proximity Within Neural Categories of Synthetic Cognition
• Authors: Michael Pichat, Enola Campoli, William Pogrund, Jourdan Wilson, Michael Veillet-Guillem, Anton Melkozerov, Paloma Pichat, Armanouche Gasparian, Samuel Demarchi, Judicael Poumay,
• Abstract summary: This study focuses on synthetic neural cog nition as a new type of study object within cognitive psychology. The goal is to make artificial neural networks of language models more explainable. This approach involves transposing concepts from cognitive psychology to the interpretive construction of artificial neural cognition.
• Score: 0.11235145048383502
• License:
• Abstract: Neuropsychology of artificial intelligence focuses on synthetic neural cog nition as a new type of study object within cognitive psychology. With the goal of making artificial neural networks of language models more explainable, this approach involves transposing concepts from cognitive psychology to the interpretive construction of artificial neural cognition. The human cognitive concept involved here is categorization, serving as a heuristic for thinking about the process of segmentation and construction of reality carried out by the neural vectors of synthetic cognition.

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)
• Neuropsychology and Explainability of AI: A Distributional Approach to the Relationship Between Activation Similarity of Neural Categories in Synthetic Cognition [0.11235145048383502]
  We propose an approach to explainability of artificial neural networks that involves using concepts from human cognitive tokens. We show that the categorical segment created by a neuron is actually the result of a superposition of categorical sub-dimensions within its input vector space.
  arXiv  Detail & Related papers  (2024-10-23T05:27:09Z)
• Artificial Kuramoto Oscillatory Neurons [65.16453738828672]
  We introduce Artificial Kuramotoy Neurons (AKOrN) as a dynamical alternative to threshold units. We show that this idea provides performance improvements across a wide spectrum of tasks. We believe that these empirical results show the importance of our assumptions at the most basic neuronal level of neural representation.
  arXiv  Detail & Related papers  (2024-10-17T17:47:54Z)
• Psychology of Artificial Intelligence: Epistemological Markers of the Cognitive Analysis of Neural Networks [0.0]
  The psychology of artificial intelligence, as predicted by Asimov (1950), aims to study this AI probing and explainability-sensitive matter. A prerequisite for examining the latter is to clarify some milestones regarding the cognitive status we can attribute to its phenomenology.
  arXiv  Detail & Related papers  (2024-07-04T12:53:05Z)
• Neuromorphic Correlates of Artificial Consciousness [1.4957306171002251]
  The concept of neural correlates of consciousness (NCC) suggests that specific neural activities are linked to conscious experiences. This paper explores the potential for artificial consciousness by merging neuromorphic design and architecture with brain simulations.
  arXiv  Detail & Related papers  (2024-05-03T09:27:51Z)
• 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)
• A Neuro-mimetic Realization of the Common Model of Cognition via Hebbian Learning and Free Energy Minimization [55.11642177631929]
  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.
  arXiv  Detail & Related papers  (2023-10-14T23:28:48Z)
• Contrastive-Signal-Dependent Plasticity: Self-Supervised Learning in Spiking Neural Circuits [61.94533459151743]
  This work addresses the challenge of designing neurobiologically-motivated schemes for adjusting the synapses of spiking networks. Our experimental simulations demonstrate a consistent advantage over other biologically-plausible approaches when training recurrent spiking networks.
  arXiv  Detail & Related papers  (2023-03-30T02:40:28Z)
• Rejecting Cognitivism: Computational Phenomenology for Deep Learning [5.070542698701158]
  We propose a non-representationalist framework for deep learning relying on a novel method: computational phenomenology. We reject the modern cognitivist interpretation of deep learning, according to which artificial neural networks encode representations of external entities.
  arXiv  Detail & Related papers  (2023-02-16T20:05:06Z)
• Constraints on the design of neuromorphic circuits set by the properties of neural population codes [61.15277741147157]
  In the brain, information is encoded, transmitted and used to inform behaviour. Neuromorphic circuits need to encode information in a way compatible to that used by populations of neuron in the brain.
  arXiv  Detail & Related papers  (2022-12-08T15:16:04Z)
• An Introductory Review of Spiking Neural Network and Artificial Neural Network: From Biological Intelligence to Artificial Intelligence [4.697611383288171]
  A kind of spiking neural network with biological interpretability is gradually receiving wide attention. This review hopes to attract different researchers and advance the development of brain-inspired intelligence and artificial intelligence.
  arXiv  Detail & Related papers  (2022-04-09T09:34:34Z)

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.