The Role of Local and Large-Scale Neuronal Synchronization in Human Cognition

Palva, Satu

doi:10.1007/978-3-319-32265-0_4

Cited by 7 publications

(5 citation statements)

References 108 publications

(116 reference statements)

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“…More generally, relative spike times are reflected in phase synchronization between neuronal populations, which can be observed both in individual-neuron studies [5] and in macroscale recording of brain activity [79]. Such synchronization has been shown to be reliable across different neural stages [5], [7], [8], [80], particularly in dynamic contexts [81].…”

Section: Discussionmentioning

confidence: 99%

Temporal Coding in Spiking Neural Networks With Alpha Synaptic Function: Learning With Backpropagation

Comşa

Potempa

Versari

et al. 2022

IEEE Trans. Neural Netw. Learning Syst.

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Section: Discussionmentioning

confidence: 99%

Temporal Coding in Spiking Neural Networks With Alpha Synaptic Function: Learning With Backpropagation

Comşa

Potempa

Versari

et al. 2022

IEEE Trans. Neural Netw. Learning Syst.

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“…More generally, relative spike times are reflected in phase synchronisation between neuronal populations, which can be observed both in individual-neuron studies [5] and in macroscale recording of brain activity [72]. Such synchronisation has been shown to be reliable across different neural stages [5,7,8,73], particularly in dynamic contexts [74].…”

Section: Discussionmentioning

confidence: 99%

Temporal Coding in Spiking Neural Networks with Alpha Synaptic Function

Comşa

Fischbacher

Potempa

et al. 2020

ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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The timing of individual neuronal spikes is essential for biological brains to make fast responses to sensory stimuli. However, conventional artificial neural networks lack the intrinsic temporal coding ability present in biological networks. We propose a spiking neural network model that encodes information in the relative timing of individual spikes. In classification tasks, the output of the network is indicated by the first neuron to spike in the output layer. This temporal coding scheme allows the supervised training of the network with backpropagation, using locally exact derivatives of the postsynaptic spike times with respect to presynaptic spike times. The network operates using a biologically-plausible alpha synaptic transfer function. Additionally, we use trainable synchronisation pulses that provide bias, add flexibility during training and exploit the decay part of the alpha function. We show that such networks can be successfully trained on noisy Boolean logic tasks and on the MNIST dataset encoded in time. We show that the spiking neural network outperforms comparable spiking models on MNIST and achieves similar quality to fully connected conventional networks with the same architecture. The spiking network spontaneously discovers two operating modes, mirroring the accuracy-speed trade-off observed in human decision-making: a highly accurate but slow regime, and a fast but slightly lower-accuracy regime. These results demonstrate the computational power of spiking networks with biological characteristics that encode information in the timing of individual neurons. By studying temporal coding in spiking networks, we aim to create building blocks towards energy-efficient, state-based and more complex biologically-inspired neural architectures.

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“…Such high integration requires long-range correlations and complex temporal patterns of coordination. In other words, functional binding between distinct neural assembles has to be highly flexible, enabling the functional cluster to move through a sequence of distinct states without losing its synchronization ( Koch et al, 2016 ; Palva, 2016 ; Ward, 2016 ; cf. Nakatani et al, 2013 ).…”

Section: Synchronization In Psychological Processesmentioning

confidence: 99%

Functional Synchronization: The Emergence of Coordinated Activity in Human Systems

et al. 2017

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The topical landscape of psychology is highly compartmentalized, with distinct phenomena explained and investigated with recourse to theories and methods that have little in common. Our aim in this article is to identify a basic set of principles that underlie otherwise diverse aspects of human experience at all levels of psychological reality, from neural processes to group dynamics. The core idea is that neural, behavioral, mental, and social structures emerge through the synchronization of lower-level elements (e.g., neurons, muscle movements, thoughts and feelings, individuals) into a functional unit—a coherent structure that functions to accomplish tasks. The coherence provided by the formation of functional units may be transient, persisting only as long as necessary to perform the task at hand. This creates the potential for the repeated assembly and disassembly of functional units in accordance with changing task demands. This perspective is rooted in principles of complexity science and non-linear dynamical systems and is supported by recent discoveries in neuroscience and recent models in cognitive and social psychology. We offer guidelines for investigating the emergence of functional units in different domains, thereby honoring the topical differentiation of psychology while providing an integrative foundation for the field.

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The Role of Local and Large-Scale Neuronal Synchronization in Human Cognition

Cited by 7 publications

References 108 publications

Temporal Coding in Spiking Neural Networks With Alpha Synaptic Function: Learning With Backpropagation

Temporal Coding in Spiking Neural Networks With Alpha Synaptic Function: Learning With Backpropagation

Temporal Coding in Spiking Neural Networks with Alpha Synaptic Function

Functional Synchronization: The Emergence of Coordinated Activity in Human Systems

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