Multifunctionality in a Connectome-Based Reservoir Computer

• URL: http://arxiv.org/abs/2306.01885v1
• Date: Fri, 2 Jun 2023 19:37:38 GMT
• Title: Multifunctionality in a Connectome-Based Reservoir Computer
• Authors: Jacob Morra, Andrew Flynn, Andreas Amann, Mark Daley
• Abstract summary: The 'fruit fly RC' (FFRC) exhibits multifunctionality using the'seeing double' problem as a benchmark test. Compared to the widely-used Erd"os-Renyi Reservoir Computer (ERRC), we report that the FFRC exhibits a greater capacity for multifunctionality.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multifunctionality describes the capacity for a neural network to perform multiple mutually exclusive tasks without altering its network connections; and is an emerging area of interest in the reservoir computing machine learning paradigm. Multifunctionality has been observed in the brains of humans and other animals: particularly, in the lateral horn of the fruit fly. In this work, we transplant the connectome of the fruit fly lateral horn to a reservoir computer (RC), and investigate the extent to which this 'fruit fly RC' (FFRC) exhibits multifunctionality using the 'seeing double' problem as a benchmark test. We furthermore explore the dynamics of how this FFRC achieves multifunctionality while varying the network's spectral radius. Compared to the widely-used Erd\"os-Renyi Reservoir Computer (ERRC), we report that the FFRC exhibits a greater capacity for multifunctionality; is multifunctional across a broader hyperparameter range; and solves the seeing double problem far beyond the previously observed spectral radius limit, wherein the ERRC's dynamics become chaotic.

Related papers

• Renormalized Connection for Scale-preferred Object Detection in Satellite Imagery [51.83786195178233]
  We design a Knowledge Discovery Network (KDN) to implement the renormalization group theory in terms of efficient feature extraction. Renormalized connection (RC) on the KDN enables synergistic focusing'' of multi-scale features. RCs extend the multi-level feature's divide-and-conquer'' mechanism of the FPN-based detectors to a wide range of scale-preferred tasks.
  arXiv  Detail & Related papers  (2024-09-09T13:56:22Z)
• Multilinear Operator Networks [60.7432588386185]
  Polynomial Networks is a class of models that does not require activation functions. We propose MONet, which relies solely on multilinear operators.
  arXiv  Detail & Related papers  (2024-01-31T16:52:19Z)
• Leveraging Low-Rank and Sparse Recurrent Connectivity for Robust Closed-Loop Control [63.310780486820796]
  We show how a parameterization of recurrent connectivity influences robustness in closed-loop settings. We find that closed-form continuous-time neural networks (CfCs) with fewer parameters can outperform their full-rank, fully-connected counterparts.
  arXiv  Detail & Related papers  (2023-10-05T21:44:18Z)
• Seeing double with a multifunctional reservoir computer [0.0]
  Multifunctional biological neural networks exploit multistability in order to perform multiple tasks without changing any network properties. We study how a reservoir computer reconstructs a coexistence of attractors when there is an overlap between them. A bifurcation analysis reveals how multifunctionality emerges and is destroyed as the RC enters a chaotic regime.
  arXiv  Detail & Related papers  (2023-05-09T23:10:29Z)
• Exploring the limits of multifunctionality across different reservoir computers [0.0]
  We explore the performance of a continuous-time, leaky-integrator, and next-generation reservoir computer' (RC) We train each RC to reconstruct a coexistence of chaotic attractors from different dynamical systems. We examine the critical effects that certain parameters can have in each RC to achieve multifunctionality.
  arXiv  Detail & Related papers  (2022-05-23T15:06:38Z)
• Cooperative Multi-Agent Reinforcement Learning Based Distributed Dynamic Spectrum Access in Cognitive Radio Networks [46.723006378363785]
  Dynamic spectrum access (DSA) is a promising paradigm to remedy the problem of inefficient spectrum utilization. In this paper, we investigate the distributed DSA problem for multi-user in a typical cognitive radio network. We employ the deep recurrent Q-network (DRQN) to address the partial observability of the state for each cognitive user.
  arXiv  Detail & Related papers  (2021-06-17T06:52:21Z)
• Recursive Multi-model Complementary Deep Fusion forRobust Salient Object Detection via Parallel Sub Networks [62.26677215668959]
  Fully convolutional networks have shown outstanding performance in the salient object detection (SOD) field. This paper proposes a wider'' network architecture which consists of parallel sub networks with totally different network architectures. Experiments on several famous benchmarks clearly demonstrate the superior performance, good generalization, and powerful learning ability of the proposed wider framework.
  arXiv  Detail & Related papers  (2020-08-07T10:39:11Z)
• Multi-Stage Hybrid Federated Learning over Large-Scale D2D-Enabled Fog Networks [61.30171206892684]
  We develop a hybrid of intra- and inter-layer model learning that considers the network as a multi-layer cluster-based structure. MH-FL considers the topology structures among the nodes in the clusters, including local networks formed via device-to-device (D2D) communications. It orchestrates the devices at different network layers in a collaborative/cooperative manner to form local consensus on the model parameters.
  arXiv  Detail & Related papers  (2020-07-18T20:03:07Z)
• A Framework for Automatic Behavior Generation in Multi-Function Swarms [1.290382979353427]
  A framework for automatic behavior generation in multi-function swarms is proposed. The framework is tested on a scenario with three simultaneous tasks. The effect of noise on the behavior characteristics in MAP-elites is investigated.
  arXiv  Detail & Related papers  (2020-07-11T20:50:52Z)

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.