An Introduction to Cognidynamics

• URL: http://arxiv.org/abs/2408.13112v1
• Date: Sun, 18 Aug 2024 05:40:07 GMT
• Title: An Introduction to Cognidynamics
• Authors: Marco Gori,
• Abstract summary: textitCognidynamics is to the dynamics of cognitive systems driven by optimal objectives imposed over time. We show the crucial role of energy dissipation and its links with focus of attention mechanisms and conscious behavior.
• Score: 11.337163242503166
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper gives an introduction to \textit{Cognidynamics}, that is to the dynamics of cognitive systems driven by optimal objectives imposed over time when they interact either with a defined virtual or with a real-world environment. The proposed theory is developed in the general framework of dynamic programming which leads to think of computational laws dictated by classic Hamiltonian equations. Those equations lead to the formulation of a neural propagation scheme in cognitive agents modeled by dynamic neural networks which exhibits locality in both space and time, thus contributing the longstanding debate on biological plausibility of learning algorithms like Backpropagation. We interpret the learning process in terms of energy exchange with the environment and show the crucial role of energy dissipation and its links with focus of attention mechanisms and conscious behavior.

Related papers

• 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)
• Exploiting Chaotic Dynamics as Deep Neural Networks [1.9282110216621833]
  We show that the essence of chaos can be found in various state-of-the-art deep neural networks. Our framework presents superior results in terms of accuracy, convergence speed, and efficiency. This study offers a new path for the integration of chaos, which has long been overlooked in information processing.
  arXiv  Detail & Related papers  (2024-05-29T22:03:23Z)
• Exploring neural oscillations during speech perception via surrogate gradient spiking neural networks [59.38765771221084]
  We present a physiologically inspired speech recognition architecture compatible and scalable with deep learning frameworks. We show end-to-end gradient descent training leads to the emergence of neural oscillations in the central spiking neural network. Our findings highlight the crucial inhibitory role of feedback mechanisms, such as spike frequency adaptation and recurrent connections, in regulating and synchronising neural activity to improve recognition performance.
  arXiv  Detail & Related papers  (2024-04-22T09:40:07Z)
• Reasoning Algorithmically in Graph Neural Networks [1.8130068086063336]
  We aim to integrate the structured and rule-based reasoning of algorithms with adaptive learning capabilities of neural networks. This dissertation provides theoretical and practical contributions to this area of research.
  arXiv  Detail & Related papers  (2024-02-21T12:16:51Z)
• Discrete, compositional, and symbolic representations through attractor dynamics [51.20712945239422]
  We introduce a novel neural systems model that integrates attractor dynamics with symbolic representations to model cognitive processes akin to the probabilistic language of thought (PLoT) Our model segments the continuous representational space into discrete basins, with attractor states corresponding to symbolic sequences, that reflect the semanticity and compositionality characteristic of symbolic systems through unsupervised learning, rather than relying on pre-defined primitives. This approach establishes a unified framework that integrates both symbolic and sub-symbolic processing through neural dynamics, a neuroplausible substrate with proven expressivity in AI, offering a more comprehensive model that mirrors the complex duality of cognitive operations
  arXiv  Detail & Related papers  (2023-10-03T05:40:56Z)
• A Novel Neural-symbolic System under Statistical Relational Learning [50.747658038910565]
  We propose a general bi-level probabilistic graphical reasoning framework called GBPGR. In GBPGR, the results of symbolic reasoning are utilized to refine and correct the predictions made by the deep learning models. Our approach achieves high performance and exhibits effective generalization in both transductive and inductive tasks.
  arXiv  Detail & Related papers  (2023-09-16T09:15:37Z)
• Learning low-dimensional dynamics from whole-brain data improves task capture [2.82277518679026]
  We introduce a novel approach to learning low-dimensional approximations of neural dynamics by using a sequential variational autoencoder (SVAE) Our method finds smooth dynamics that can predict cognitive processes with accuracy higher than classical methods. We evaluate our approach on various task-fMRI datasets, including motor, working memory, and relational processing tasks.
  arXiv  Detail & Related papers  (2023-05-18T18:43:13Z)
• 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)
• Backprop-Free Reinforcement Learning with Active Neural Generative Coding [84.11376568625353]
  We propose a computational framework for learning action-driven generative models without backpropagation of errors (backprop) in dynamic environments. We develop an intelligent agent that operates even with sparse rewards, drawing inspiration from the cognitive theory of planning as inference. The robust performance of our agent offers promising evidence that a backprop-free approach for neural inference and learning can drive goal-directed behavior.
  arXiv  Detail & Related papers  (2021-07-10T19:02:27Z)
• 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.