Spatiotemporal Patterns in Neurobiology: An Overview for Future Artificial Intelligence

• URL: http://arxiv.org/abs/2203.15415v1
• Date: Tue, 29 Mar 2022 10:28:01 GMT
• Title: Spatiotemporal Patterns in Neurobiology: An Overview for Future Artificial Intelligence
• Authors: Sean Knight
• Abstract summary: 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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In recent years, there has been increasing interest in developing models and tools to address the complex patterns of connectivity found in brain tissue. Specifically, this is due to a need to understand how emergent properties emerge from these network structures at multiple spatiotemporal scales. We argue that computational models are key tools for elucidating the possible functionalities that can emerge from interactions of heterogeneous neurons connected by complex networks on multi-scale temporal and spatial domains. Here we review several classes of models including spiking neurons, integrate and fire neurons with short term plasticity (STP), conductance based integrate-and-fire models with STP, and population density neural field (PDNF) models using simple examples with emphasis on neuroscience applications while also providing some potential future research directions for AI. These computational approaches allow us to explore the impact of changing underlying mechanisms on resulting network function both experimentally as well as theoretically. Thus we hope these studies will inform future developments in artificial intelligence algorithms as well as help validate our understanding of brain processes based on experiments in animals or humans.

Related papers

• Contrastive Learning in Memristor-based Neuromorphic Systems [55.11642177631929]
  Spiking neural networks have become an important family of neuron-based models that sidestep many of the key limitations facing modern-day backpropagation-trained deep networks. In this work, we design and investigate a proof-of-concept instantiation of contrastive-signal-dependent plasticity (CSDP), a neuromorphic form of forward-forward-based, backpropagation-free learning.
  arXiv  Detail & Related papers  (2024-09-17T04:48:45Z)
• 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)
• 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)
• Probing Biological and Artificial Neural Networks with Task-dependent Neural Manifolds [12.037840490243603]
  We investigate the internal mechanisms of neural networks through the lens of neural population geometry. We quantitatively characterize how different learning objectives lead to differences in the organizational strategies of these models. These analyses present a strong direction for bridging mechanistic and normative theories in neural networks through neural population geometry.
  arXiv  Detail & Related papers  (2023-12-21T20:40:51Z)
• 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)
• The Expressive Leaky Memory Neuron: an Efficient and Expressive Phenomenological Neuron Model Can Solve Long-Horizon Tasks [64.08042492426992]
  We introduce the Expressive Memory (ELM) neuron model, a biologically inspired model of a cortical neuron. Our ELM neuron can accurately match the aforementioned input-output relationship with under ten thousand trainable parameters. We evaluate it on various tasks with demanding temporal structures, including the Long Range Arena (LRA) datasets.
  arXiv  Detail & Related papers  (2023-06-14T13:34:13Z)
• 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)
• Evolving spiking neuron cellular automata and networks to emulate in vitro neuronal activity [0.0]
  We produce spiking neural systems that emulate the patterns of behavior of biological neurons in vitro. Our models were able to produce a level of network-wide synchrony. The genomes of the top-performing models indicate the excitability and density of connections in the model play an important role in determining the complexity of the produced activity.
  arXiv  Detail & Related papers  (2021-10-15T17:55:04Z)
• 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)
• A multi-agent model for growing spiking neural networks [0.0]
  This project has explored rules for growing the connections between the neurons in Spiking Neural Networks as a learning mechanism. Results in a simulation environment showed that for a given set of parameters it is possible to reach topologies that reproduce the tested functions. This project also opens the door to the usage of techniques like genetic algorithms for obtaining the best suited values for the model parameters.
  arXiv  Detail & Related papers  (2020-09-21T15:11:29Z)

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.