001031974 001__ 1031974
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001031974 037__ $$aFZJ-2024-05898
001031974 041__ $$aEnglish
001031974 1001_ $$0P:(DE-Juel1)144576$$aIto, Junji$$b0$$eCorresponding author$$ufzj
001031974 1112_ $$aBernstein Conference 2024$$cFrankfurt$$d2024-09-29 - 2024-10-02$$wGermany
001031974 245__ $$aSynfire chains in random weight threshold unit network
001031974 260__ $$c2024
001031974 3367_ $$033$$2EndNote$$aConference Paper
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001031974 520__ $$aSynfire chains have been postulated as a model for stable propagation of synchronous spikes through the cortical networks [1,2,3]. Synfire-chain-like activity can also be found in spiking artificial neural networks trained for a classification task [4]. Understanding the mechanism for generating such activity would provide better insights into the functioning of real brains and artificial neural networks. Here we consider an analytically tractable network of binary units to study the conditions for the emergence of synchronous spikes and their stable propagation.Our network is organized in layers of $N$ threshold units, each taking a state $x\in\{0,1\}$ depending on its input $I$ as $x=H(I-\theta)$ ($H$: Heaviside step function, $\theta$: threshold). The connections from layer $l$ to $l+1$ are represented by a matrix $W^l$, whose elements are Gaussian IID random variables with mean 0 and variance $1/N$. States of all units are initially set to 0. Then a fraction $P^1$ of layer 1 units are activated (their states set to 1) at different timings. We interpret the state change of a unit as a spike generation by that unit. The spikes generated in layer $l$ are propagated to layer $l+1$ through the matrix $W^l$, providing time-varying inputs to activate layer $l+1$ units and generate their spikes.Based on the formalism laid out in [5], we derive a relation between the fraction $p^l(t)$ and $p^{l+1}(t)$ of active units at time $t$ in layer $l$ and $l+1$, respectively, as $p^{l+1}(t)=\mathrm{erfc}\big(\theta/\sqrt{2p^l(t)}\big)/2$ (Eq. 1). Iteratively applying this relation results in the activity converging either to $p^\infty(t)=0$ or to $p^\infty(t)=p_s$, depending on whether $p^1(t) \geq p_u$ or $p^1(t) \leq p_u$, respectively, with $p_s$ and $p_u$ as shown in the figure. Since $p^1(t)$ is a monotonically increasing function of time, this result means that, as the activity propagates through layers, the timing of the state change converges to the timing at which $p^1$ exceeds $p_u$. Hence, the spikes become more synchronous and activate the successive layer more reliably.We also show that, the greater $P^1$ is, the earlier this converging timing becomes, meaning that the network naturally converts the activity level of the initial layer to the timing of the spike pulse packet that propagates through the layers. We demonstrate this in a network with multiple synfire chains embedded and discuss the implications of this effect to cortical information processing.
001031974 536__ $$0G:(DE-HGF)POF4-5231$$a5231 - Neuroscientific Foundations (POF4-523)$$cPOF4-523$$fPOF IV$$x0
001031974 536__ $$0G:(EU-Grant)785907$$aHBP SGA2 - Human Brain Project Specific Grant Agreement 2 (785907)$$c785907$$fH2020-SGA-FETFLAG-HBP-2017$$x1
001031974 536__ $$0G:(EU-Grant)945539$$aHBP SGA3 - Human Brain Project Specific Grant Agreement 3 (945539)$$c945539$$fH2020-SGA-FETFLAG-HBP-2019$$x2
001031974 536__ $$0G:(DE-HGF)ZT-I-0003$$aHAF - Helmholtz Analytics Framework (ZT-I-0003)$$cZT-I-0003$$x3
001031974 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
001031974 536__ $$0G:(DE-Juel-1)iBehave-20220812$$aAlgorithms of Adaptive Behavior and their Neuronal Implementation in Health and Disease (iBehave-20220812)$$ciBehave-20220812$$x5
001031974 536__ $$0G:(GEPRIS)368482240$$aGRK 2416 - GRK 2416: MultiSenses-MultiScales: Neue Ansätze zur Aufklärung neuronaler multisensorischer Integration (368482240)$$c368482240$$x6
001031974 7001_ $$0P:(DE-Juel1)186076$$aOberste-Frielinghaus, Jonas$$b1$$ufzj
001031974 7001_ $$0P:(DE-Juel1)176776$$aKurth, Anno$$b2$$ufzj
001031974 7001_ $$0P:(DE-Juel1)144168$$aGrün, Sonja$$b3$$ufzj
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001031974 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)186076$$aForschungszentrum Jülich$$b1$$kFZJ
001031974 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)176776$$aForschungszentrum Jülich$$b2$$kFZJ
001031974 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)144168$$aForschungszentrum Jülich$$b3$$kFZJ
001031974 9131_ $$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
001031974 9141_ $$y2024
001031974 9201_ $$0I:(DE-Juel1)IAS-6-20130828$$kIAS-6$$lComputational and Systems Neuroscience$$x0
001031974 9201_ $$0I:(DE-Juel1)INM-10-20170113$$kINM-10$$lJara-Institut Brain structure-function relationships$$x1
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