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@INPROCEEDINGS{ObersteFrielinghaus:1044235,
      author       = {Oberste-Frielinghaus, Jonas and Kurth, Anno and Göltz,
                      Julian and Kriener, Laura and Ito, Junji and Petrovici,
                      Mihai A. and Grün, Sonja},
      title        = {{T}ime-to-first-spike encoding in layered networks evokes
                      label-specific synfire chain activity},
      reportid     = {FZJ-2025-03123},
      year         = {2025},
      abstract     = {INTRODUCTIONWhile artificial neural networks (ANNs) have
                      achieved remarkable success in various tasks, they lack two
                      major characteristic features of biological neural networks:
                      spiking activity and operation in continuous time.This makes
                      it difficult to leverage knowledge about ANNs to gain
                      insights into the computational principles of the real
                      brains.However, training methods for spiking neural networks
                      (SNNs) have recently been developed to create functional SNN
                      models [1].In this study we analyze the activity of a
                      multilayer feedforward SNN trained for image classification
                      and uncover the structures in both connectivity and dynamics
                      that underlie its functional performance.METHODSOur network
                      is composed of an input layer (784 neurons), 4 hidden layers
                      (300 excitatory and 100 inhibitory neurons in each layer),
                      and an output layer (10 neurons).We trained it with
                      backpropagation to classify the MNIST dataset, based on
                      time-to-first-spike coding: each neuron encodes information
                      in the timing of its first spike; the first neuron to spike
                      in the output layer defines the inferred input image class
                      [1].The MNIST input is also provided as spike timing: dark
                      pixels spike early, lighter pixels later. Based on the
                      connection weights after training, neurons that have strong
                      excitatory effects on each of the output neurons are
                      identified in each layer. Note that one neuron can have
                      strong effects on multiple output neurons.RESULTSIn response
                      to a sample, the input layer generates a volley of spikes,
                      identified as a pulse packet (PP) [2], which propagates
                      through the hidden layers (Fig. 1).In deeper layers, spikes
                      in a PP get more synchronized and the neurons providing
                      spikes to the PP become more specific to the sample
                      label.This leads to a characteristic sparse representation
                      of the sample label in deep layers.The analysis of
                      connection weights reveals that a correct classification is
                      achieved by propagating spikes through a specific pathway
                      across layers, composed of neurons with strong excitatory
                      effects on the correct output neuron.Pathways for different
                      output neurons become more separate in deeper layers, with
                      less overlap of neurons between pathways.DISCUSSIONThe
                      revealed connectivity structure and the propagation of
                      spikes as a PP agree with the notion of the synfire chain
                      (SFC) [3,4].To our knowledge, this is the first example of
                      SFC formation by training of a functional network. In our
                      network, multiple parallel SFCs emerge through the training
                      for MNIST classification, representing each input label by
                      activation of one particular SFC.Such a representation
                      naturally leads to sparser encoding of the input label in
                      deeper layers, and also increases the linear separability of
                      layer-wise activity.Thus, the use of SFCs for information
                      representation can have multiple advantages for achieving
                      efficient computation, besides the stable transmission of
                      information through the network.REFERENCES1.​ Göltz et
                      al. (2021). Fast and energy-efficient neuromorphic deep
                      learning with first-spike times. Nature Machine
                      Intelligence, 3(9),
                      823–835.https://doi.org/10.1038/s42256-021-00388-x2.​
                      Diesmann, Gewaltig, $\&$ Aertsen (1999). Stable propagation
                      of synchronous spiking in cortical neural networks. Nature,
                      402(6761), 529–533. https://doi.org/10.1038/9901013.​
                      Abeles (1982). Local Cortical Circuits: An
                      Electrophysiological Study. Springer-Verlag.4.​ Abeles
                      (1991). Corticonics: Neural Circuits of the Cerebral Cortex.
                      Cambridge University Press.},
      month         = {Jul},
      date          = {2025-07-05},
      organization  = {34th Annual Computational Neuroscience
                       Meeting, Florence (Italy), 5 Jul 2025 -
                       9 Jul 2025},
      subtyp        = {After Call},
      cin          = {IAS-6 / INM-10},
      cid          = {I:(DE-Juel1)IAS-6-20130828 / I:(DE-Juel1)INM-10-20170113},
      pnm          = {5231 - Neuroscientific Foundations (POF4-523) / HBP SGA2 -
                      Human Brain Project Specific Grant Agreement 2 (785907) /
                      HBP SGA3 - Human Brain Project Specific Grant Agreement 3
                      (945539) / Algorithms of Adaptive Behavior and their
                      Neuronal Implementation in Health and Disease
                      (iBehave-20220812) / JL SMHB - Joint Lab Supercomputing and
                      Modeling for the Human Brain (JL SMHB-2021-2027)},
      pid          = {G:(DE-HGF)POF4-5231 / G:(EU-Grant)785907 /
                      G:(EU-Grant)945539 / G:(DE-Juel-1)iBehave-20220812 /
                      G:(DE-Juel1)JL SMHB-2021-2027},
      typ          = {PUB:(DE-HGF)24},
      url          = {https://juser.fz-juelich.de/record/1044235},
}