001044235 001__ 1044235
001044235 005__ 20250722202239.0
001044235 037__ $$aFZJ-2025-03123
001044235 041__ $$aEnglish
001044235 1001_ $$0P:(DE-Juel1)186076$$aOberste-Frielinghaus, Jonas$$b0$$ufzj
001044235 1112_ $$a34th Annual Computational Neuroscience Meeting$$cFlorence$$d2025-07-05 - 2025-07-09$$gCNS2025$$wItaly
001044235 245__ $$aTime-to-first-spike encoding in layered networks evokes label-specific synfire chain activity
001044235 260__ $$c2025
001044235 3367_ $$033$$2EndNote$$aConference Paper
001044235 3367_ $$2BibTeX$$aINPROCEEDINGS
001044235 3367_ $$2DRIVER$$aconferenceObject
001044235 3367_ $$2ORCID$$aCONFERENCE_POSTER
001044235 3367_ $$2DataCite$$aOutput Types/Conference Poster
001044235 3367_ $$0PUB:(DE-HGF)24$$2PUB:(DE-HGF)$$aPoster$$bposter$$mposter$$s1753162439_26033$$xAfter Call
001044235 520__ $$aINTRODUCTIONWhile 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.
001044235 536__ $$0G:(DE-HGF)POF4-5231$$a5231 - Neuroscientific Foundations (POF4-523)$$cPOF4-523$$fPOF IV$$x0
001044235 536__ $$0G:(EU-Grant)785907$$aHBP SGA2 - Human Brain Project Specific Grant Agreement 2 (785907)$$c785907$$fH2020-SGA-FETFLAG-HBP-2017$$x1
001044235 536__ $$0G:(EU-Grant)945539$$aHBP SGA3 - Human Brain Project Specific Grant Agreement 3 (945539)$$c945539$$fH2020-SGA-FETFLAG-HBP-2019$$x2
001044235 536__ $$0G:(DE-Juel-1)iBehave-20220812$$aAlgorithms of Adaptive Behavior and their Neuronal Implementation in Health and Disease (iBehave-20220812)$$ciBehave-20220812$$x3
001044235 536__ $$0G:(DE-Juel1)JL SMHB-2021-2027$$aJL SMHB - Joint Lab Supercomputing and Modeling for the Human Brain (JL SMHB-2021-2027)$$cJL SMHB-2021-2027$$x4
001044235 7001_ $$0P:(DE-Juel1)176776$$aKurth, Anno$$b1$$ufzj
001044235 7001_ $$0P:(DE-HGF)0$$aGöltz, Julian$$b2
001044235 7001_ $$0P:(DE-HGF)0$$aKriener, Laura$$b3
001044235 7001_ $$0P:(DE-Juel1)144576$$aIto, Junji$$b4$$eCorresponding author$$ufzj
001044235 7001_ $$0P:(DE-HGF)0$$aPetrovici, Mihai A.$$b5
001044235 7001_ $$0P:(DE-Juel1)144168$$aGrün, Sonja$$b6$$ufzj
001044235 909CO $$ooai:juser.fz-juelich.de:1044235$$popenaire$$pVDB$$pec_fundedresources
001044235 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)186076$$aForschungszentrum Jülich$$b0$$kFZJ
001044235 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)176776$$aForschungszentrum Jülich$$b1$$kFZJ
001044235 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)144576$$aForschungszentrum Jülich$$b4$$kFZJ
001044235 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)144168$$aForschungszentrum Jülich$$b6$$kFZJ
001044235 9131_ $$0G:(DE-HGF)POF4-523$$1G:(DE-HGF)POF4-520$$2G:(DE-HGF)POF4-500$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-5231$$aDE-HGF$$bKey Technologies$$lNatural, Artificial and Cognitive Information Processing$$vNeuromorphic Computing and Network Dynamics$$x0
001044235 9141_ $$y2025
001044235 9201_ $$0I:(DE-Juel1)IAS-6-20130828$$kIAS-6$$lComputational and Systems Neuroscience$$x0
001044235 9201_ $$0I:(DE-Juel1)INM-10-20170113$$kINM-10$$lJara-Institut Brain structure-function relationships$$x1
001044235 980__ $$aposter
001044235 980__ $$aVDB
001044235 980__ $$aI:(DE-Juel1)IAS-6-20130828
001044235 980__ $$aI:(DE-Juel1)INM-10-20170113
001044235 980__ $$aUNRESTRICTED