Movement Prediction from Real-World Images Using a Liquid State Machine

Burgsteiner, Harald; Kroll, Mark W.; Leopold, Alexander; Steinbauer, Gerald

doi:10.1007/11504894_19

Cited by 16 publications

(11 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, XOR functions are not easily relatable to functions like perception and memory under more natural con-ditions. To go beyond XOR, other reservoir computing studies have demonstrated the usefulness of complex dynamics for learning more naturalistic tasks, such as speech recognition, 81 syntactic processing, 82 visual object motion tracking, 83 motor control, 84 and navigation. 85 In fact, a precursor to Kello's critical branching model 1 was applied to visual motion processing.…”

Section: Roles Of Complex Spike Dynamics In Neural Plasticitymentioning

confidence: 99%

Plasticity, Learning, and Complexity in Spiking Networks

Kello

Rodny

Warlaumont

et al. 2012

Crit Rev Biomed Eng

View full text Add to dashboard Cite

Complexity is widespread in neuronal spike trains and propagation of spike activity, in that variations in measurements of neural activity are irregular, heterogeneous, non-stationary, transient, and scale-free. There are numerous possible reasons for this complexity, and numerous possible consequences for neural and behavioral function. The present review is focused on relationships among neural plasticity, learning, and complex spike dynamics in animal nervous systems, including those of humans. The literature on complex spike dynamics and mechanisms of synaptic plasticity are reviewed for the purpose of considering the roles that each might play for the other. That is, the roles of complex spike dynamics in learning and regulatory functions are considered, as well as the roles of learning and regulatory functions in generating complex spike dynamics. Experimental and computational studies from a range of disciplines and perspectives are discussed, and it is concluded that cognitive science and neuroscience have much to gain from investigating the adaptive aspects of complex spike dynamics for neural and cognitive function.

show abstract

Section: Roles Of Complex Spike Dynamics In Neural Plasticitymentioning

confidence: 99%

Plasticity, Learning, and Complexity in Spiking Networks

Kello

Rodny

Warlaumont

et al. 2012

Crit Rev Biomed Eng

View full text Add to dashboard Cite

show abstract

“…Feedforward networks compute only in one direction in contrast to RNNs which have cyclic dependencies (cf. [2]).…”

Section: Recurrent Neural Networkmentioning

confidence: 99%

Fast Classification Learning with Neural Networks and Conceptors for Speech Recognition and Car Driving Maneuvers

Krause,

Otto,

Stolzenburg

2021

Preprint

View full text Add to dashboard Cite

Recurrent neural networks are a powerful means in diverse applications. We show that, together with so-called conceptors, they also allow fast learning, in contrast to other deep learning methods. In addition, a relatively small number of examples suffices to train neural networks with high accuracy. We demonstrate this with two applications, namely speech recognition and detecting car driving maneuvers. We improve the state-of-the art by application-specific preparation techniques: For speech recognition, we use mel frequency cepstral coefficients leading to a compact representation of the frequency spectra, and detecting car driving maneuvers can be done without the commonly used polynomial interpolation, as our evaluation suggests.

show abstract

“…This solution is exploited through the paradigm of liquid state machines for the control of a two-jointed arm [41], of a roving robot [42], and for movement prediction [43].…”

Section: A Remarks On Navigation With Visual Cuesmentioning

confidence: 99%

Learning Anticipation via Spiking Networks: Application to Navigation Control

Arena

Fortuna

Frasca

et al. 2009

IEEE Trans. Neural Netw.

View full text Add to dashboard Cite

In this paper, we introduce a network of spiking neurons devoted to navigation control. Three different examples, dealing with stimuli of increasing complexity, are investigated. In the first one, obstacle avoidance in a simulated robot is achieved through a network of spiking neurons. In the second example, a second layer is designed aiming to provide the robot with a target approaching system, making it able to move towards visual targets. Finally, a network of spiking neurons for navigation based on visual cues is introduced. In all cases, the robot was assumed to rely on some a priori known responses to low-level sensors (i.e., to contact sensors in the case of obstacles, to proximity target sensors in the case of visual targets, or to the visual target for navigation with visual cues). Based on their knowledge, the robot has to learn the response to high-level stimuli (i.e., range finder sensors or visual input). The biologically plausible paradigm of spike-timing-dependent plasticity (STDP) is included in the network to make the system able to learn high-level responses that guide navigation through a simple unstructured environment. The learning procedure is based on classical conditioning.

show abstract

Movement Prediction from Real-World Images Using a Liquid State Machine

Cited by 16 publications

References 6 publications

Plasticity, Learning, and Complexity in Spiking Networks

Plasticity, Learning, and Complexity in Spiking Networks

Fast Classification Learning with Neural Networks and Conceptors for Speech Recognition and Car Driving Maneuvers

Learning Anticipation via Spiking Networks: Application to Navigation Control

Contact Info

Product

Resources

About