Theory of Interaction of Memory Patterns in Layered Associative Networks

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

From spiking neurons to rate models: a cascade model as an approximation to spiking neuron models with refractoriness

A neuron that is stimulated repeatedly by the same time-dependent stimulus exhibits slightly different spike timing at each trial. We compared the exact solution of the time-dependent firing rate for a stochastically spiking neuron model with refractoriness (spike response model) with that of an inhomogeneous Poisson process subject to the same stimulus. To arrive at a mapping between the two models we used alternatively (i) a systematic parameter-free Volterra expansion of the exact solution or (ii) a linear filter combined with nonlinear Poisson rate model (linear-nonlinear Poisson cascade model) with a single free parameter. Both the cascade model and the second-order Volterra model showed excellent agreement with the exact rate dynamics of the spiking neuron model with refractoriness even for strong and rapidly changing input. Cascade rate models are widely used in systems neuroscience. Our method could help to connect experimental rate measurements to the theory of spiking neurons

Sparse and Dense Encoding in Layered Associative Network of Spiking Neurons

A synfire chain is a simple neural network model which can propagate stable synchronous spikes called a pulse packet and widely researched. 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 in which connection we embed memory patterns by the Hebbian Learning. We analyzed their activity by the Fokker-Planck method. In our previous report, when a half of neurons belongs to each memory pattern (memory 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 ($F<0.5$), synchronous firings of the memory pattern are promoted. On the contrary, the densely connected network ($F>0.5$) inhibit synchronous firings. The sparseness and denseness also effect the basin of attraction and the storage capacity of the embedded memory patterns. We show that the sparsely(densely) connected networks enlarge(shrink) the basion of attraction and increase(decrease) the storage capacity

Signal Propagation in Feedforward Neuronal Networks with Unreliable Synapses

In this paper, we systematically investigate both the synfire propagation and firing rate propagation in feedforward neuronal network coupled in an all-to-all fashion. In contrast to most earlier work, where only reliable synaptic connections are considered, we mainly examine the effects of unreliable synapses on both types of neural activity propagation in this work. We first study networks composed of purely excitatory neurons. Our results show that both the successful transmission probability and excitatory synaptic strength largely influence the propagation of these two types of neural activities, and better tuning of these synaptic parameters makes the considered network support stable signal propagation. It is also found that noise has significant but different impacts on these two types of propagation. The additive Gaussian white noise has the tendency to reduce the precision of the synfire activity, whereas noise with appropriate intensity can enhance the performance of firing rate propagation. Further simulations indicate that the propagation dynamics of the considered neuronal network is not simply determined by the average amount of received neurotransmitter for each neuron in a time instant, but also largely influenced by the stochastic effect of neurotransmitter release. Second, we compare our results with those obtained in corresponding feedforward neuronal networks connected with reliable synapses but in a random coupling fashion. We confirm that some differences can be observed in these two different feedforward neuronal network models. Finally, we study the signal propagation in feedforward neuronal networks consisting of both excitatory and inhibitory neurons, and demonstrate that inhibition also plays an important role in signal propagation in the considered networks.Comment: 33pages, 16 figures; Journal of Computational Neuroscience (published

A Comprehensive Workflow for General-Purpose Neural Modeling with Highly Configurable Neuromorphic Hardware Systems

In this paper we present a methodological framework that meets novel requirements emerging from upcoming types of accelerated and highly configurable neuromorphic hardware systems. We describe in detail a device with 45 million programmable and dynamic synapses that is currently under development, and we sketch the conceptual challenges that arise from taking this platform into operation. More specifically, we aim at the establishment of this neuromorphic system as a flexible and neuroscientifically valuable modeling tool that can be used by non-hardware-experts. We consider various functional aspects to be crucial for this purpose, and we introduce a consistent workflow with detailed descriptions of all involved modules that implement the suggested steps: The integration of the hardware interface into the simulator-independent model description language PyNN; a fully automated translation between the PyNN domain and appropriate hardware configurations; an executable specification of the future neuromorphic system that can be seamlessly integrated into this biology-to-hardware mapping process as a test bench for all software layers and possible hardware design modifications; an evaluation scheme that deploys models from a dedicated benchmark library, compares the results generated by virtual or prototype hardware devices with reference software simulations and analyzes the differences. The integration of these components into one hardware-software workflow provides an ecosystem for ongoing preparative studies that support the hardware design process and represents the basis for the maturity of the model-to-hardware mapping software. The functionality and flexibility of the latter is proven with a variety of experimental results

OP0291 TOFACITINIB FOR THE TREATMENT OF POLYARTICULAR COURSE JUVENILE IDIOPATHIC ARTHRITIS: RESULTS OF A PHASE 3, RANDOMISED, DOUBLE-BLIND, PLACEBO-CONTROLLED WITHDRAWAL STUDY

Background:Tofacitinib is an oral JAK inhibitor that is being investigated for JIA.Objectives:To assess tofacitinib efficacy and safety in JIA patients (pts).Methods:This was a Phase 3, randomised, double-blind (DB), placebo (PBO)-controlled withdrawal study in pts aged 2−<18 years with polyarticular course JIA (pcJIA), PsA or ERA (NCT02592434). In the 18-week open-label Part 1, pts received weight-based tofacitinib doses (5 mg BID or lower). Pts with ≥JIA ACR30 response at Week (W)18 were randomised 1:1 in the DB Part 2 (W18−44) to continue tofacitinib or switch to PBO. Primary endpoint: disease flare rate by W44. Key secondary endpoints: JIA ACR50/30/70 response rates; change from Part 2 baseline (Δ) in CHAQ-DI at W44. Other efficacy endpoints: time to disease flare in Part 2; JADAS27-CRP in Parts 1 and 2. PsA/ERA pts were excluded from these efficacy analyses. Safety was evaluated in all pts up to W44.Results:225 enrolled pts with pcJIA (n=184), PsA (n=20) or ERA (n=21) received tofacitinib in Part 1. At W18, 173/225 (76.9%) pts entered Part 2 (pcJIA n=142, PsA n=15, ERA n=16). In pcJIA pts, disease flare rate in Part 2 was significantly lower with tofacitinib vs PBO by W44 (p=0.0031; Fig 1a). JIA ACR50/30/70 response rates (Fig 1b) and ΔCHAQ-DI (Fig 1c) at W44, and time to disease flare in Part 2 (Fig 2a), were improved with tofacitinib vs PBO. Tofacitinib reduced JADAS27-CRP in Part 1; this effect was sustained in Part 2 (Fig 2b). Overall, safety was similar with tofacitinib or PBO (Table): 77.3% and 74.1% had adverse events (AEs); 1.1% and 2.4% had serious AEs. In Part 1, 2 pts had herpes zoster (non-serious) and 3 pts had serious infections (SIs). In Part 2, SIs occurred in 1 tofacitinib pt and 1 PBO pt. No pts died.Conclusion:In pcJIA pts, tofacitinib vs PBO resulted in significantly fewer disease flares, and improved time to flare, disease activity and physical functioning. Tofacitinib safety was consistent with that in RA pts.Table.Safety in all ptsPart 1Part 2TofacitinibaN=225TofacitinibaN=88PBO N=85Pts with events, n (%)AEs153 (68.0)68 (77.3)63 (74.1)SAEs7 (3.1)1 (1.1)2 (2.4)Permanent discontinuations due to AEs26 (11.6)16 (18.2)29 (34.1)AEs of special interest Death000 Gastrointestinal perforationb000 Hepatic eventb3 (1.3)00 Herpes zoster (non-serious and serious)2 (0.9)c00 Interstitial lung diseaseb000 Major adverse cardiovascular eventsb000 Malignancy (including non-melanoma skin cancer)b000 Macrophage activation syndromeb000 Opportunistic infectionb000 SI3 (1.3)1 (1.1)d1 (1.2) Thrombotic event (deep vein thrombosis, pulmonary embolismbor arterial thromboembolism)000 Tuberculosisb000a5 mg BID or equivalent weight-based lower dose in pts <40 kgbAdjudicated eventscBoth non-seriousdOne SAE of pilonidal cyst repair was coded to surgical procedures instead of infections, and was inadvertently not identified as an SI. Following adjudication, the SAE did not meet opportunistic infection criteria; it is also included in the table as an SIAE, adverse event; BID, twice daily; PBO, placebo; pts, patients; SAE, serious AE; SI, serious infectionAcknowledgments:Study sponsored by Pfizer Inc. Medical writing support was provided by Sarah Piggott of CMC Connect and funded by Pfizer Inc.Disclosure of Interests:Nicolino Ruperto Grant/research support from: Bristol-Myers Squibb, Eli Lily, F Hoffmann-La Roche, GlaxoSmithKline, Janssen, Novartis, Pfizer, Sobi (paid to institution), Consultant of: Ablynx, AbbVie, AstraZeneca-Medimmune, Biogen, Boehringer Ingelheim, Bristol-Myers Squibb, Eli Lily, EMD Serono, GlaxoSmithKline, Hoffmann-La Roche, Janssen, Merck, Novartis, Pfizer, R-Pharma, Sanofi, Servier, Sinergie, Sobi, Takeda, Speakers bureau: Ablynx, AbbVie, AstraZeneca-Medimmune, Biogen, Boehringer Ingelheim, Bristol-Myers Squibb, Eli Lily, EMD Serono, GlaxoSmithKline, Hoffmann-La Roche, Janssen, Merck, Novartis, Pfizer, R-Pharma, Sanofi, Servier, Sinergie, Sobi, Takeda, Olga Synoverska Speakers bureau: Sanofi, Tracy Ting: None declared, Carlos Abud-Mendoza Speakers bureau: Eli Lilly, Pfizer Inc, Alberto Spindler Speakers bureau: Eli Lilly, Yulia Vyzhga Grant/research support from: Pfizer Inc, Katherine Marzan Grant/research support from: Novartis, Vladimir Keltsev: None declared, Irit Tirosh: None declared, Lisa Imundo: None declared, Rita Jerath: None declared, Daniel Kingsbury: None declared, Betül Sözeri: None declared, Sheetal Vora: None declared, Sampath Prahalad Grant/research support from: Novartis, Elena Zholobova Grant/research support from: Novartis and Pfizer Inc, Speakers bureau: AbbVie, Novartis, Pfizer Inc and Roche, Yonatan Butbul Aviel: None declared, Vyacheslav Chasnyk: None declared, Melissa Lerman Grant/research support from: Amgen, Kabita Nanda Grant/research support from: Abbott, AbbVie, Amgen and Roche, Heinrike Schmeling Grant/research support from: Janssen, Pfizer Inc, Roche and USB Bioscience, Heather Tory: None declared, Yosef Uziel Speakers bureau: Pfizer Inc, Diego O Viola Grant/research support from: Bristol-Myers Squibb, GSK, Janssen and Pfizer Inc, Speakers bureau: AbbVie and Bristol-Myers Squibb, Holly Posner Shareholder of: Pfizer Inc, Employee of: Pfizer Inc, Keith Kanik Shareholder of: Pfizer Inc, Employee of: Pfizer Inc, Ann Wouters Shareholder of: Pfizer Inc, Employee of: Pfizer Inc, Cheng Chang Shareholder of: Pfizer Inc, Employee of: Pfizer Inc, Richard Zhang Shareholder of: Pfizer Inc, Employee of: Pfizer Inc, Irina Lazariciu Consultant of: Pfizer Inc, Employee of: IQVIA, Ming-Ann Hsu Shareholder of: Pfizer Inc, Employee of: Pfizer Inc, Ricardo Suehiro Shareholder of: Pfizer Inc, Employee of: Pfizer Inc, Alberto Martini Consultant of: AbbVie, Eli Lily, EMD Serono, Janssen, Novartis, Pfizer, UCB, Daniel J Lovell Consultant of: Abbott (consulting and PI), AbbVie (PI), Amgen (consultant and DSMC Chairperson), AstraZeneca, Boehringer Ingelheim, Bristol-Myers Squibb (PI), Celgene, Forest Research (DSMB Chairman), GlaxoSmithKline, Hoffman-La Roche, Janssen (co-PI), Novartis (consultant and PI), Pfizer (consultant and PI), Roche (PI), Takeda, UBC (consultant and PI), Wyeth, Employee of: Cincinnati Children's Hospital Medical Center, Speakers bureau: Wyeth, Hermine Brunner Consultant of: Hoffman-La Roche, Novartis, Pfizer, Sanofi Aventis, Merck Serono, AbbVie, Amgen, Alter, AstraZeneca, Baxalta Biosimilars, Biogen Idec, Boehringer, Bristol-Myers Squibb, Celgene, EMD Serono, Janssen, MedImmune, Novartis, Pfizer, and UCB Biosciences, Speakers bureau: GSK, Roche, and Novarti

KRAS-mutation incidence and prognostic value are metastatic site-specific in lung adenocarcinoma: poor prognosis in patients with KRAS-mutation and bone metastasis

Current guidelines lack comprehensive information on the metastatic site-specific role of KRAS mutation in lung adenocarcinoma (LADC). We investigated the effect of KRAS mutation on overall survival (OS) in this setting. In our retrospective study, 500 consecutive Caucasian metastatic LADC patients with known KRAS mutational status were analyzed after excluding 32 patients with EGFR mutations. KRAS mutation incidence was 28.6%. The most frequent metastatic sites were lung (45.6%), bone (26.2%), adrenal gland (17.4%), brain (16.8%), pleura (15.6%) and liver (11%). Patients with intrapulmonary metastasis had significantly increased KRAS mutation frequency compared to those with extrapulmonary metastases (35% vs 26.5%, p=0.0125). In contrast, pleural dissemination and liver involvement were associated with significantly decreased KRAS mutation incidence (vs all other metastatic sites; 17% (p<0.001) and 16% (p=0.02) vs 33%, respectively). Strikingly, we found a significant prognostic effect of KRAS status only in the bone metastatic subcohort (KRAS-wild-type vs KRAS-mutant; median OS 9.7v 3.7 months; HR, 0.49; 95% CI, 0.31 to 0.79; p =0.003). Our study suggests that KRAS mutation frequency in LADC patients shows a metastatic site dependent variation and, moreover, that the presence of KRAS mutation is associated with significantly worse outcome in bone metastatic cases.(VLID)469049

Spike-Timing Theory of Working Memory

Working memory (WM) is the part of the brain's memory system that provides temporary storage and manipulation of information necessary for cognition. Although WM has limited capacity at any given time, it has vast memory content in the sense that it acts on the brain's nearly infinite repertoire of lifetime long-term memories. Using simulations, we show that large memory content and WM functionality emerge spontaneously if we take the spike-timing nature of neuronal processing into account. Here, memories are represented by extensively overlapping groups of neurons that exhibit stereotypical time-locked spatiotemporal spike-timing patterns, called polychronous patterns; and synapses forming such polychronous neuronal groups (PNGs) are subject to associative synaptic plasticity in the form of both long-term and short-term spike-timing dependent plasticity. While long-term potentiation is essential in PNG formation, we show how short-term plasticity can temporarily strengthen the synapses of selected PNGs and lead to an increase in the spontaneous reactivation rate of these PNGs. This increased reactivation rate, consistent with in vivo recordings during WM tasks, results in high interspike interval variability and irregular, yet systematically changing, elevated firing rate profiles within the neurons of the selected PNGs. Additionally, our theory explains the relationship between such slowly changing firing rates and precisely timed spikes, and it reveals a novel relationship between WM and the perception of time on the order of seconds