2024-06-26_Perrinet24FENS.bib

@article{Grimaldi22polychronies,
 abstract = {Why do neurons communicate through spikes? By definition, spikes are all-or-none neural events which occur at continuous times. In other words, spikes are on one side binary, existing or not without further details, and on the other can occur at any asynchronous time, without the need for a centralized clock. This stands in stark contrast to the analog representation of values and the discretized timing classically used in digital processing and at the base of modern-day neural networks. As neural systems almost systematically use this so-called event-based representation in the living world, a better understanding of this phenomenon remains a fundamental challenge in neurobiology in order to better interpret the profusion of recorded data. With the growing need for intelligent embedded systems, it also emerges as a new computing paradigm to enable the efficient operation of a new class of sensors and event-based computers, called neuromorphic, which could enable significant gains in computation time and energy consumption, a major societal issue in the era of the digital economy and global warming. In this review paper, we provide evidence from biology, theory and engineering that the precise timing of spikes plays a crucial role in our understanding of the efficiency of neural networks.},
 author = {Grimaldi, Antoine and Gruel, Amelie and Besnainou, Camille and Jeremie, Jean-Nicolas and Martinet, Jean and Perrinet, Laurent U},
 bdsk-url-1 = {https://laurentperrinet.github.io/publication/grimaldi-22-polychronies/},
 bdsk-url-2 = {https://doi.org/10.3390/brainsci13010068},
 doi = {10.3390/brainsci13010068},
 grants = {aprovis3D,anr-anr,polychronies},
 journal = {Brain Sciences},
 title = {Precise spiking motifs in neurobiological and neuromorphic data},
 url = {https://laurentperrinet.github.io/publication/grimaldi-22-polychronies/},
 url_hal = {https://hal-amu.archives-ouvertes.fr/hal-03918338},
 year = {2022}
}

@article{mainen_reliability_1995,
	title = {Reliability of {Spike} {Timing} in {Neocortical} {Neurons}},
	volume = {268},
	issn = {0036-8075, 1095-9203},
	doi = {10.1126/science.7770778},
	language = {en},
	number = {5216},
	journal = {Science},
	author = {Mainen, Zachary F. and Sejnowski, Terrence J.},
	month = jun,
	year = {1995},
	pages = {1503--1506},
}

@article{izhikevich_polychronization_2006,
	title = {Polychronization: {Computation} with {Spikes}},
	volume = {18},
	issn = {0899-7667},
	shorttitle = {Polychronization},
	doi = {10.1162/089976606775093882},
	abstract = {We present a minimal spiking network that can polychronize, that is, exhibit reproducible time-locked but not synchronous firing patterns with millisecond precision, as in synfire braids. The network consists of cortical spiking neurons with axonal conduction delays and spike-timing-dependent plasticity (STDP); a ready-to-use MATLAB code is included. It exhibits sleeplike oscillations, gamma (40 Hz) rhythms, conversion of firing rates to spike timings, and other interesting regimes. Due to the interplay between the delays and STDP, the spiking neurons spontaneously self-organize into groups and generate patterns of stereotypical polychronous activity. To our surprise, the number of coexisting polychronous groups far exceeds the number of neurons in the network, resulting in an unprecedented memory capacity of the system. We speculate on the significance of polychrony to the theory of neuronal group selection (TNGS, neural Darwinism), cognitive neural computations, binding and gamma rhythm, mechanisms of attention, and consciousness as “attention to memories.”},
	number = {2},
	journal = {Neural Computation},
	author = {Izhikevich, Eugene M.},
	month = feb,
	year = {2006},
	pages = {245--282},
}

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@article{Grimaldi23BC,
 author = {Grimaldi, Antoine and Perrinet, Laurent U},
 grants = {aprovis3D,anr-anr,polychronies},
 journal = {Biological Cybernetics},
 title = {Learning heterogeneous delays in a layer of spiking neurons for fast motion detection},
 url = {https://laurentperrinet.github.io/publication/grimaldi-23-bc/},
 year = {2023}
}


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@inproceedings{Fois24FENS,
	abstract = {Timing is essential for neural processing, but evidence for such temporal precision is still lacking. We have developed a theoretical model of representation based on spatio-temporal spiking motifs. Our goal is to develop a self-supervised learning method for optimal detection of such motifs in neurobiological data. To detect such motifs, we have extended the K-Means algorithm to process temporal data using a convolutional operator. A second pooling layer ensures that only one motif is used per time step. The results were improved by ensuring that the detected motifs are equiprobably activated using a homeostatic mechanism. We applied this algorithm to the Spiking Heidelberg database, which consists of the output of a realistic cochlear model to spoken digits. Qualitatively, the filters show a structure similar to the receptive fields found in the auditory cortex. Based on these promising results on this realistic yet synthetic dataset, future work will aim to apply his algorithm to neurological data to challenge the hypothesis of the role of precise spike timing in neural processes.},
	author = {Fois, Adrien and Perrinet, Laurent U},
	grants = {polychronies},
	booktitle = {FENS Forum 2024},
	location = {Vienna (Austria)},
	title = {Self-Supervised Learning of Spiking Motifs in Neurobiological Data},
	url = {https://laurentperrinet.github.io/publication/fois-24-fens/},
	date = {2024-06-27},
}

@inproceedings{Perrinet23ICANN,
	abstract = {Recently, interest has grown in exploring the hypothesis that neural activity conveys information through precise spiking motifs. To investigate this phenomenon, various algorithms have been proposed to detect such motifs in Single Unit Activity (SUA) recorded from populations of neurons. In this study, we present a novel detection model based on the inversion of a generative model of raster plot synthesis. Using this generative model, we derive an optimal detection procedure that takes the form of logistic regression combined with temporal convolution. A key advantage of this model is its differentiability, which allows us to formulate a supervised learning approach using a gradient descent on the binary cross-entropy loss. To assess the model's ability to detect spiking motifs in synthetic data, we first perform numerical evaluations. This analysis highlights the advantages of using spiking motifs over traditional firing rate based population codes. We then successfully demonstrate that our learning method can recover synthetically generated spiking motifs, indicating its potential for further applications. In the future, we aim to extend this method to real neurobiological data, where the ground truth is unknown, to explore and detect spiking motifs in a more natural and biologically relevant context.},
	author = {Perrinet, Laurent U},
	grants = {polychronies},
	booktitle = {ICANN 2023 -  Special Session on Recent Advances in Spiking Neural Networks},
	location = {Heraklion (Crete, Greece)},
	title = {Accurate Detection of Spiking Motifs by Learning Heterogeneous Delays of a Spiking Neural Network},
	url = {https://laurentperrinet.github.io/publication/perrinet-23-icann/},
	url_code = {https://github.com/laurentperrinet/2023-09-27_HDSNN-ICANN},
	date = {2023-09-27},
	}