Neuronal Assembly Dynamics in Supervised and Unsupervised Learning Scenarios

Moioli, Renan Cipriano; Husbands, Phil

doi:10.1162/neco_a_00502

Cited by 4 publications

(3 citation statements)

References 92 publications

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“…Oscillatory neural dynamics are prevalent in many brain areas and appear to underlie numerous mechanisms involved in information processing and the generation of behaviour [18,22,121]. This observation led to the development of various artificial neural network architectures based on coupled oscillators that have been successfully employed as robust control systems for various kinds of robots [11,55,86,102]. Complex, often chaotic, dynamics are observed in biological nervous systems.…”

Section: Literature Reviewmentioning

confidence: 99%

Recent advances in evolutionary and bio-inspired adaptive robotics: Exploiting embodied dynamics

et al. 2021

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This paper explores current developments in evolutionary and bio-inspired approaches to autonomous robotics, concentrating on research from our group at the University of Sussex. These developments are discussed in the context of advances in the wider fields of adaptive and evolutionary approaches to AI and robotics, focusing on the exploitation of embodied dynamics to create behaviour. Four case studies highlight various aspects of such exploitation. The first exploits the dynamical properties of a physical electronic substrate, demonstrating for the first time how component-level analog electronic circuits can be evolved directly in hardware to act as robot controllers. The second develops novel, effective and highly parsimonious navigation methods inspired by the way insects exploit the embodied dynamics of innate behaviours. Combining biological experiments with robotic modeling, it is shown how rapid route learning can be achieved with the aid of navigation-specific visual information that is provided and exploited by the innate behaviours. The third study focuses on the exploitation of neuromechanical chaos in the generation of robust motor behaviours. It is demonstrated how chaotic dynamics can be exploited to power a goal-driven search for desired motor behaviours in embodied systems using a particular control architecture based around neural oscillators. The dynamics are shown to be chaotic at all levels in the system, from the neural to the embodied mechanical. The final study explores the exploitation of the dynamics of brain-body-environment interactions for efficient, agile flapping winged flight. It is shown how a multi-objective evolutionary algorithm can be used to evolved dynamical neural controllers for a simulated flapping wing robot with feathered wings. Results demonstrate robust, stable, agile flight is achieved in the face of random wind gusts by exploiting complex asymmetric dynamics partly enabled by continually changing wing and tail morphologies.

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Section: Literature Reviewmentioning

confidence: 99%

Recent advances in evolutionary and bio-inspired adaptive robotics: Exploiting embodied dynamics

et al. 2021

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“…There have been several previous investigations in this general area (Asai et al, 2003;Moioli et al, 2012;Moioli & Husbands, 2013;Lee et al, 2018;Zagha et al, 2013;Shim & Husbands, 2015), but here we introduce a different perspective. To our knowledge this is the first attempt to precisely quantify the relative contributions of sensory input and SSA to the evoked signals that are driving a sensorimotor behavior.…”

Section: Introductionmentioning

confidence: 99%

Active Role of Self-Sustained Neural Activity on Sensory Input Processing: A Minimal Theoretical Model

Santos¹,

Gomes²,

Barandiaran³

et al. 2022

Neural Computation

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A growing body of work has demonstrated the importance of ongoing oscillatory neural activity in sensory processing and the generation of sensorimotor behaviors. It has been shown, for several different brain areas, that sensory-evoked neural oscillations are generated from the modulation by sensory inputs of inherent self-sustained neural activity (SSA). This letter contributes to that strand of research by introducing a methodology to investigate how much of the sensory-evoked oscillatory activity is generated by SSA and how much is generated by sensory inputs within the context of sensorimotor behavior in a computational model. We develop an abstract model consisting of a network of three Kuramoto oscillators controlling the behavior of a simulated agent performing a categorical perception task. The effects of sensory inputs and SSAs on sensory-evoked oscillations are quantified by the cross product of velocity vectors in the phase space of the network under different conditions (disconnected without input, connected without input, and connected with input). We found that while the agent is carrying out the task, sensory-evoked activity is predominantly generated by SSA (93.10%) with much less influence from sensory inputs (6.90%). Furthermore, the influence of sensory inputs can be reduced by 10.4% (from 6.90% to 6.18%) with a decay in the agent's performance of only 2%. A dynamical analysis shows how sensory-evoked oscillations are generated from a dynamic coupling between the level of sensitivity of the network and the intensity of the input signals. This work may suggest interesting directions for neurophysiological experiments investigating how self-sustained neural activity influences sensory input processing, and ultimately affects behavior.

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“…Hence a number of researchers have implemented ensemble models that attempt to reflect aspects of the biology while borrowing ideas and methods from machine learning. These include low-level models concentrating on the oscillatory properties of neuron ensembles, showing how synchronisation dynamics between ensembles can underpin supervised and unsupervised adaptation in a variety of scenarios [ 14 , 21 – 23 ], and higher-level models proposing information processing architectures that can be used to coordinate and organise learning in ensembles in the brain [ 5 , 6 ]. In the latter category, mixture of experts (MoE) type architectures [ 24 ] have been proposed as an interesting candidate for ensemble learning in the cortex and other areas.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised Learning in an Ensemble of Spiking Neural Networks Mediated by ITDP

et al. 2016

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We propose a biologically plausible architecture for unsupervised ensemble learning in a population of spiking neural network classifiers. A mixture of experts type organisation is shown to be effective, with the individual classifier outputs combined via a gating network whose operation is driven by input timing dependent plasticity (ITDP). The ITDP gating mechanism is based on recent experimental findings. An abstract, analytically tractable model of the ITDP driven ensemble architecture is derived from a logical model based on the probabilities of neural firing events. A detailed analysis of this model provides insights that allow it to be extended into a full, biologically plausible, computational implementation of the architecture which is demonstrated on a visual classification task. The extended model makes use of a style of spiking network, first introduced as a model of cortical microcircuits, that is capable of Bayesian inference, effectively performing expectation maximization. The unsupervised ensemble learning mechanism, based around such spiking expectation maximization (SEM) networks whose combined outputs are mediated by ITDP, is shown to perform the visual classification task well and to generalize to unseen data. The combined ensemble performance is significantly better than that of the individual classifiers, validating the ensemble architecture and learning mechanisms. The properties of the full model are analysed in the light of extensive experiments with the classification task, including an investigation into the influence of different input feature selection schemes and a comparison with a hierarchical STDP based ensemble architecture.

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Neuronal Assembly Dynamics in Supervised and Unsupervised Learning Scenarios

Cited by 4 publications

References 92 publications

Recent advances in evolutionary and bio-inspired adaptive robotics: Exploiting embodied dynamics

Recent advances in evolutionary and bio-inspired adaptive robotics: Exploiting embodied dynamics

Active Role of Self-Sustained Neural Activity on Sensory Input Processing: A Minimal Theoretical Model

Unsupervised Learning in an Ensemble of Spiking Neural Networks Mediated by ITDP

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