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Markus Diesmann - One of the best experts on this subject based on the ideXlab platform.
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High-capacity embedding of Synfire Chains in a cortical network model
Journal of Computational Neuroscience, 2013Co-Authors: Chris Trengove, Cees Leeuwen, Markus DiesmannAbstract:Synfire Chains, sequences of pools linked by feedforward connections, support the propagation of precisely timed spike sequences, or Synfire waves. An important question remains, how Synfire Chains can efficiently be embedded in cortical architecture. We present a model of Synfire Chain embedding in a cortical scale recurrent network using conductance-based synapses, balanced Chains, and variable transmission delays. The network attains substantially higher embedding capacities than previous spiking neuron models and allows all its connections to be used for embedding. The number of waves in the model is regulated by recurrent background noise. We computationally explore the embedding capacity limit, and use a mean field analysis to describe the equilibrium state. Simulations confirm the mean field analysis over broad ranges of pool sizes and connectivity levels; the number of pools embedded in the system trades off against the firing rate and the number of waves. An optimal inhibition level balances the conflicting requirements of stable Synfire propagation and limited response to background noise. A simplified analysis shows that the present conductance-based synapses achieve higher contrast between the responses to Synfire input and background noise compared to current-based synapses, while regulation of wave numbers is traced to the use of variable transmission delays.
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Detecting Synfire Chains in parallel spike data.
Journal of neuroscience methods, 2012Co-Authors: George L. Gerstein, Markus Diesmann, Sonja Grün, Elizabeth R. Williams, Chris TrengoveAbstract:The Synfire Chain model of brain organization has received much theoretical attention since its introduction (Abeles, 1982, 1991). However there has been no convincing experimental demonstration of Synfire Chains due partly to limitations of recording technology but also due to lack of appropriate analytic methods for large scale recordings of parallel spike trains. We have previously published one such method based on intersection of the neural populations active at two different times (Schrader et al., 2008). In the present paper we extend this analysis to deal with higher firing rates and noise levels, and develop two additional tools based on properties of repeating firing patterns. All three measures show characteristic signatures if Synfire Chains underlie the recorded data. However we demonstrate that the detection of repeating firing patterns alone (as used in several papers) is not enough to infer the presence of Synfire Chains. Positive results from all three measures are needed.
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A compositionality machine realized by a hierarchic architecture of Synfire Chains.
Frontiers in computational neuroscience, 2011Co-Authors: Sven Schrader, Markus Diesmann, Abigail MorrisonAbstract:The composition of complex behavior is thought to rely on the concurrent and sequential activation of simpler action components, or primitives. Systems of Synfire Chains have previously been proposed to account for either the simultaneous or the sequential aspects of compositionality; however, the compatibility of the two aspects has so far not been addressed. Moreover, the simultaneous activation of primitives has up until now only been investigated in the context of reactive computations, i.e., the perception of stimuli. In this study we demonstrate how a hierarchical organization of Synfire Chains is capable of generating both aspects of compositionality for proactive computations such as the generation of complex and ongoing action. To this end, we develop a network model consisting of two layers of Synfire Chains. Using simple drawing strokes as a visualization of abstract primitives, we map the feed-forward activity of the upper level Synfire Chains to motion in two-dimensional space. Our model is capable of producing drawing strokes that are combinations of primitive strokes by binding together the corresponding Chains. Moreover, when the lower layer of the network is constructed in a closed-loop fashion, drawing strokes are generated sequentially. The generated pattern can be random or deterministic, depending on the connection pattern between the lower level Chains. We propose quantitative measures for simultaneity and sequentiality, revealing a wide parameter range in which both aspects are fulfilled. Finally, we investigate the spiking activity of our model to propose candidate signatures of Synfire Chain computation in measurements of neural activity during action execution.
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High storage capacity of Synfire Chains in large-scale cortical networks of conductance-based spiking neurons
BMC Neuroscience, 2010Co-Authors: Chris Trengove, Cees Van Leeuwen, Markus DiesmannAbstract:We demonstrate stable dynamics of synchronous pulse packet propagation and global asynchronous irregular (AI) activity in a sparse network of 105 neurons in which excitatory connectivity is derived from a random superposition of Synfire Chains having a total length of up to 80,000 pools. This is the largest amount of Synfire Chains to have been stably embedded in a network model of spiking neurons: about two orders of magnitude great than achieved previously [1-3]. Key features of the model which allow this high storage capacity are: (1) conductance-based rather than current-based synapses; and (2) inhibitory neurons in the Synfire Chain pools. As noted recently [3] the use of conductance over current based results in a narrower membrane potential distribution with a mean closer to threshold, which is more favourable for pulse-packet propagation at realistic levels of AI activity and background input. Recurrent dynamics of the AI state is driven by the propagating pulse packets which deliver random excitatory and inhibitory 'background input' to the rest of the network. The equilibrium dynamical behaviour of the network depends on a critical level of excitatory and inhibitory background input above which Synfire wave propagation ceases to be viable. In our simulation protocol Synfire waves are initiated by ongoing, intermittent delivery of external input pulses. In the stable regime, an upper limit in the number of simultaneously propagating pulse packets is reached, typically ~ 5-30 waves, above which further waves are only successfully initiated at the expense of existing waves. Using a semi-analytical framework, storage capacity, and the number of pulse packets that can propagate simultaneously, is calculated. Mean field analysis is used to estimate the stability and spiking rate of the AI state, combined with a numerical determination of the stability of wave propagation. Results agree qualitatively with simulations and correctly predict the order of magnitude of storage capacity. Departures from mean field theory are observed, most notably the greater fluctuations in the AI state particularly as the upper limit of storage is approached, which impacts upon the stability of pulse packet propagation and reduces the maximum number of simultaneously propagating waves. We also demonstrate in a modified version of this architecture that includes branching Chains how Synfire waves can be generated and maintained endogenously, using the same mechanism which stabilises the number of Synfire waves at upper limit determined by the critical level of background input.
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bifurcation analysis of synchronization dynamics in cortical feed forward networks in novel coordinates
BMC Neuroscience, 2009Co-Authors: Tilo Schwalger, Sven Goedeke, Markus DiesmannAbstract:In a Synfire Chain [1], synchronous activity in one group of neurons can excite neurons of the next group to fire synchronously themselves. If this mechanism repeats itself from group to group, a "pulse packet" of spiking activity can travel down the Chain. For a homogeneous Chain, the spike packet profile of one group is uniquely mapped to the packet profile of the successive group, thereby establishing a map for the packet dynamics in the space of pulse-shaped functions. A stable packet corresponds to a stable fixed point of this infinite-dimensional map.
Kazuyuki Aihara - One of the best experts on this subject based on the ideXlab platform.
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Variable Timescales of Repeated Spike Patterns in Synfire Chain with Mexican-Hat Connectivity
Neural computation, 2007Co-Authors: Kosuke Hamaguchi, Masato Okada, Kazuyuki AiharaAbstract:Repetitions of precise spike patterns observed both in vivo and in vitro have been reported for more than a decade. Studies on the spike volley (a pulse packet) propagating through a homogeneous feedforward network have demonstrated its capability of generating spike patterns with millisecond fidelity. This model is called the Synfire Chain and suggests a possible mechanism for generating repeated spike patterns (RSPs). The propagation speed of the pulse packet determines the temporal property of RSPs. However, the relationship between propagation speed and network structure is not well understood. We studied a feedforward network with Mexican-hat connectivity by using the leaky integrate-and-fire neuron model and analyzed the network dynamics with the Fokker-Planck equation. We examined the effect of the spatial pattern of pulse packets on RSPs in the network with multistability. Pulse packets can take spatially uniform or localized shapes in a multistable regime, and they propagate with different speeds. These distinct pulse packets generate RSPs with different timescales, but the order of spikes and the ratios between interspike intervals are preserved. This result indicates that the RSPs can be transformed into the same template pattern through the expanding or contracting operation of the timescale.
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Stochasticity in localized Synfire Chain
Neurocomputing, 2004Co-Authors: Kosuke Hamaguchi, Masato Okada, Michiko Yamana, Kazuyuki AiharaAbstract:We report on stochastic evolutions of firing states through feedforward neural networks with Mexican-Hat-type connectivity. The variance in connectivity, which depends on the pre-synaptic neuron, generates a common noisy input to post-synaptic neurons. We develop a theory to describe the stochastic evolution of the localized Synfire Chain driven by a common noisy input. The development of a firing state through neural layers does not converge to a certain fixed point but keeps on fluctuating. Stationary firing states except for a non-firing state are lost, but an almost stationary distribution of firing state is observed.
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theory of localized Synfire Chain characteristic propagation speed of stable spike pattern
Neural Information Processing Systems, 2004Co-Authors: Kosuke Hamaguchi, Masato Okada, Kazuyuki AiharaAbstract:Repeated spike patterns have often been taken as evidence for the Synfire Chain, a phenomenon that a stable spike synchrony propagates through a feedforward network. Inter-spike intervals which represent a repeated spike pattern are influenced by the propagation speed of a spike packet. However, the relation between the propagation speed and network structure is not well understood. While it is apparent that the propagation speed depends on the excitatory synapse strength, it might also be related to spike patterns. We analyze a feedforward network with Mexican-Hat-type connectivity (FMH) using the Fokker-Planck equation. We show that both a uniform and a localized spike packet are stable in the FMH in a certain parameter region. We also demonstrate that the propagation speed depends on the distinct firing patterns in the same network.
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NIPS - Theory of localized Synfire Chain: characteristic propagation speed of stable spike pattern
2004Co-Authors: Kosuke Hamaguchi, Masato Okada, Kazuyuki AiharaAbstract:Repeated spike patterns have often been taken as evidence for the Synfire Chain, a phenomenon that a stable spike synchrony propagates through a feedforward network. Inter-spike intervals which represent a repeated spike pattern are influenced by the propagation speed of a spike packet. However, the relation between the propagation speed and network structure is not well understood. While it is apparent that the propagation speed depends on the excitatory synapse strength, it might also be related to spike patterns. We analyze a feedforward network with Mexican-Hat-type connectivity (FMH) using the Fokker-Planck equation. We show that both a uniform and a localized spike packet are stable in the FMH in a certain parameter region. We also demonstrate that the propagation speed depends on the distinct firing patterns in the same network.
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Theory of localized Synfire Chain
arXiv: Neurons and Cognition, 2004Co-Authors: Kosuke Hamaguchi, Masato Okada, Kazuyuki AiharaAbstract:Neuron is a noisy information processing unit and conventional view is that information in the cortex is carried on the rate of neurons spike emission. More recent studies on the activity propagation through the homogeneous network have demonstrated that signals can be transmitted with millisecond fidelity; this model is called the Synfire Chain and suggests the possibility of the spatio-temporal coding. However, the more biologically realistic, structured feedforward network generates spatially distributed inputs. It results in the difference of spike timing. This poses a question on how the spatial structure of a network effect the stability of spatio-temporal spike patterns, and the speed of a spike packet propagation. By formulating the Fokker-Planck equation for the feedforwardly coupled network with Mexican-Hat type connectivity, we show the stability of localized spike packet and existence of Multi-stable phase where both uniform and localized spike packets are stable depending on the initial input structure. The Multi-stable phase enables us to show that a spike pattern, or the information of its own, determines the propagation speed.
Cengiz Pehlevan - One of the best experts on this subject based on the ideXlab platform.
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Statistical structure of the trial-to-trial timing variability in Synfire Chains
2020Co-Authors: Dina Obeid, Jacob A. Zavatone-veth, Cengiz PehlevanAbstract:Timing and its variability are crucial for behavior. Consequently, neural circuits that take part in the control of timing and in the measurement of temporal intervals have been the subject of much research. Here, we provide an analytical and computational account of the temporal variability in what is perhaps the most basic model of a timing circuit, the Synfire Chain. First, we study the statistical structure of trial-to-trial timing variability in a reduced but analytically tractable model: a Chain of single integrate-and-fire neurons. We show that this circuit9s variability is well-described by a generative model consisting of local, global, and jitter components. We relate each of these components to distinct neural mechanisms in the model. Next, we establish in simulations that these results carry over to a noisy homogeneous Synfire Chain. Finally, motivated by the fact that a Synfire Chain is thought to underlie the circuit that takes part in the control and timing of zebra finch song, we present simulations of a biologically realistic Synfire Chain model of the zebra finch timekeeping circuit. We find the structure of trial-to-trial timing variability to be consistent with our previous findings, and to agree with experimental observations of the song9s temporal variability. Our study therefore provides a possible neuronal account of behavioral variability in zebra finches.
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Statistical structure of the trial-to-trial timing variability in Synfire Chains.
Physical review. E, 2020Co-Authors: Dina Obeid, Jacob A. Zavatone-veth, Cengiz PehlevanAbstract:Timing and its variability are crucial for behavior. Consequently, neural circuits that take part in the control of timing and in the measurement of temporal intervals have been the subject of much research. Here we provide an analytical and computational account of the temporal variability in what is perhaps the most basic model of a timing circuit-the Synfire Chain. First we study the statistical structure of trial-to-trial timing variability in a reduced but analytically tractable model: a Chain of single integrate-and-fire neurons. We show that this circuit's variability is well described by a generative model consisting of local, global, and jitter components. We relate each of these components to distinct neural mechanisms in the model. Next we establish in simulations that these results carry over to a noisy homogeneous Synfire Chain. Finally, motivated by the fact that a Synfire Chain is thought to underlie the circuit that takes part in the control and timing of the zebra finch song, we present simulations of a biologically realistic Synfire Chain model of the zebra finch timekeeping circuit. We find the structure of trial-to-trial timing variability to be consistent with our previous findings and to agree with experimental observations of the song's temporal variability. Our study therefore provides a possible neuronal account of behavioral variability in zebra finches.
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flexibility in motor timing constrains the topology and dynamics of pattern generator circuits
Nature Communications, 2018Co-Authors: Cengiz Pehlevan, Bence P OlveczkyAbstract:Temporally precise movement patterns underlie many motor skills and innate actions, yet the flexibility with which the timing of such stereotyped behaviors can be modified is poorly understood. To probe this, we induce adaptive changes to the temporal structure of birdsong. We find that the duration of specific song segments can be modified without affecting the timing in other parts of the song. We derive formal prescriptions for how neural networks can implement such flexible motor timing. We find that randomly connected recurrent networks, a common approximation for how neocortex is wired, do not generally conform to these, though certain implementations can approximate them. We show that feedforward networks, by virtue of their one-to-one mapping between network activity and time, are better suited. Our study provides general prescriptions for pattern generator networks that implement flexible motor timing, an important aspect of many motor skills, including birdsong and human speech. Human speech and bird song requires the generation of precisely timed motor patterns. The authors show that zebra finches can learn to independently modify the duration of individual song segments and find that Synfire Chain networks are ideally suited to implement such flexible motor timing.
Kosuke Hamaguchi - One of the best experts on this subject based on the ideXlab platform.
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Sparse and Dense Encoding in Layered Associative Network of Spiking Neurons
Journal of the Physical Society of Japan, 2007Co-Authors: Kazuya Ishibashi, Kosuke Hamaguchi, Masato OkadaAbstract:A Synfire Chain is a simple neural network model which can transmit stable synchronous spikes called a pulse packet. However how Synfire Chains coexist in one network remains to be elucidated. We have studied the activity of a layered associative network of leaky integrate-and-fire neurons which connections are embedded with memory patterns by the Hebbian learning rule. We analyze their activity by the Fokker–Planck method. In our previous report, when a half of neurons belongs to each memory pattern (pattern rate F =0.5), the temporal profiles of the network activity is split into temporally clustered groups called sublattices under certain input conditions. In this study, we show that when the network is sparsely connected ( F 0.5) inhibit synchronous firings. The basin of attraction and the storage capacity of the embedded memory patterns also depend on the sparseness of the network. We sh...
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Variable Timescales of Repeated Spike Patterns in Synfire Chain with Mexican-Hat Connectivity
Neural computation, 2007Co-Authors: Kosuke Hamaguchi, Masato Okada, Kazuyuki AiharaAbstract:Repetitions of precise spike patterns observed both in vivo and in vitro have been reported for more than a decade. Studies on the spike volley (a pulse packet) propagating through a homogeneous feedforward network have demonstrated its capability of generating spike patterns with millisecond fidelity. This model is called the Synfire Chain and suggests a possible mechanism for generating repeated spike patterns (RSPs). The propagation speed of the pulse packet determines the temporal property of RSPs. However, the relationship between propagation speed and network structure is not well understood. We studied a feedforward network with Mexican-hat connectivity by using the leaky integrate-and-fire neuron model and analyzed the network dynamics with the Fokker-Planck equation. We examined the effect of the spatial pattern of pulse packets on RSPs in the network with multistability. Pulse packets can take spatially uniform or localized shapes in a multistable regime, and they propagate with different speeds. These distinct pulse packets generate RSPs with different timescales, but the order of spikes and the ratios between interspike intervals are preserved. This result indicates that the RSPs can be transformed into the same template pattern through the expanding or contracting operation of the timescale.
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Theory of Interaction of Memory Patterns in Layered Associative Networks
Journal of the Physical Society of Japan, 2006Co-Authors: Kazuya Ishibashi, Kosuke Hamaguchi, Masato OkadaAbstract:A Synfire Chain is a network that can generate repeated spike patterns with millisecond precision. Although Synfire Chains with only one activity propagation mode have been intensively analyzed with several neuron models, those with several stable propagation modes have not been thoroughly investigated. By using the leaky integrate-and-fire neuron model, we constructed a layered associative network embedded with memory patterns. We analyzed the network dynamics with the Fokker-Planck equation. First, we addressed the stability of one memory pattern as a propagating spike volley. We showed that memory patterns propagate as pulse packets. Second, we investigated the activity when we activated two different memory patterns. Simultaneous activation of two memory patterns with the same strength led the propagating pattern to a mixed state. In contrast, when the activations had different strengths, the pulse packet converged to a two-peak state. Finally, we studied the effect of the preceding pulse packet on the following pulse packet. The following pulse packet was modified from its original activated memory pattern, and it converged to a two-peak state, mixed state or non-spike state depending on the time interval.
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Stochasticity in localized Synfire Chain
Neurocomputing, 2004Co-Authors: Kosuke Hamaguchi, Masato Okada, Michiko Yamana, Kazuyuki AiharaAbstract:We report on stochastic evolutions of firing states through feedforward neural networks with Mexican-Hat-type connectivity. The variance in connectivity, which depends on the pre-synaptic neuron, generates a common noisy input to post-synaptic neurons. We develop a theory to describe the stochastic evolution of the localized Synfire Chain driven by a common noisy input. The development of a firing state through neural layers does not converge to a certain fixed point but keeps on fluctuating. Stationary firing states except for a non-firing state are lost, but an almost stationary distribution of firing state is observed.
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theory of localized Synfire Chain characteristic propagation speed of stable spike pattern
Neural Information Processing Systems, 2004Co-Authors: Kosuke Hamaguchi, Masato Okada, Kazuyuki AiharaAbstract:Repeated spike patterns have often been taken as evidence for the Synfire Chain, a phenomenon that a stable spike synchrony propagates through a feedforward network. Inter-spike intervals which represent a repeated spike pattern are influenced by the propagation speed of a spike packet. However, the relation between the propagation speed and network structure is not well understood. While it is apparent that the propagation speed depends on the excitatory synapse strength, it might also be related to spike patterns. We analyze a feedforward network with Mexican-Hat-type connectivity (FMH) using the Fokker-Planck equation. We show that both a uniform and a localized spike packet are stable in the FMH in a certain parameter region. We also demonstrate that the propagation speed depends on the distinct firing patterns in the same network.
Tomoki Fukai - One of the best experts on this subject based on the ideXlab platform.
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Predictive synchrony organized by spike-based Hebbian learning with time-representing Synfire activities
Neural Information Processing: Research and Development, 2004Co-Authors: Katsunori Kitano, Tomoki FukaiAbstract:In this chapter, we introduce a computational model to give a theoretical account for a phenomenon experimentally observed in neural activity of behaving animals. Pairs of neurons in the primary motor cortex exhibit significant increases of coincident spikes at times when a monkey expects behavioral events. The result provides an evidence that such a synchrony has predictive power. To investigate the underlying mechanism of such a predictive synchrony, we construct a computational model based on two known characteristics in the brain: one is the Synfire Chain, the other is spike-timing-dependent plasticity. The Synfire Chain is a model to explain a precisely firing spike sequence observed in frontal parts of the cortex. Synaptic plasticity, which is commonly believed a basic phenomenon underlying learning and memory, has been reported to depend on relative timings of neuronal spikes. In the proposed model, occurrence times of events are embedded in synapses from the Synfire Chains to time-coding neurons through spike-timing-dependent synaptic plasticity. We also discuss the robustness of the proposed mechanism and possible information coding in this cortical region.
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Self-organization of memory activity through spike-timing-dependent plasticity.
Neuroreport, 2002Co-Authors: Katsunori Kitano, Hideyuki Câteau, Tomoki FukaiAbstract:We studied the self-organization of memory-related activity through spike-timing-dependent plasticity (STDP). Relatively short time windows (approximately 10 ms) for the plasticity rule give rise to asynchronous persistent activity of low rates (20-30 Hz), which is typically observed in delay periods of working memory task. We demonstrate some network level effects on the activity regulation that cannot be addressed in single-neuron studies. For longer time windows (approximately 20 ms), the layered cell assemblies that propagate synchronized spikes (Synfire Chain) are self-organized. Synchronous spike propagation was suggested to underlie the precisely timed spikes in the monkey prefrontal cortex. The present results suggest that the two networks for sustained activity are different realizations of the same principle for synaptic wiring.
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Fokker-Planck approach to the pulse packet propagation in Synfire Chain.
Neural Networks, 2001Co-Authors: Hideyuki Câteau, Tomoki FukaiAbstract:Abstract We applied the Fokker–Planck method to the so-called ‘Synfire Chain’ network model and showed how a synchronous population spike (pulse packet) evolves to a narrow pulse packet (width
