Several matrix metalloproteinases (MMPs), including MMP-1, -3, and -9, mediate matrix destruction during chronic inflammatory diseases such as arthritis and atherosclerosis. MMP up-regulation by inflammatory cytokines involves interactions between several transcription factors, including activator protein-1 and nuclear factor B (NF-B). The upstream regulatory pathways are less well understood. We investigated the role of isoforms of protein kinase C (PKC) in basic fibroblast growth factor-and interleukin-1␣-mediated MMP production from cultured rabbit aortic smooth muscle cells. A synthetic PKC inhibitor, RO318220, inhibited MMP-1, -3, and -9 production by 89 ؎ 3, 75 ؎ 18, and 89 ؎ 9%, respectively. However, down-regulation of conventional and novel isoforms did not inhibit but rather increased MMP-9 production by 48 ؎ 16%, implicating an atypical PKC isoform. Consistent with this, PKC protein levels and activity were stimulated 3.3-and 13-fold, respectively, by basic fibroblast growth factor plus interleukin-1␣ and antisense oligonucleotides to PKC significantly decreased MMP-9 formation by 62 ؎ 18% compared with scrambled sequences. Moreover, adenovirus-mediated overexpression of a dominant-negative (DN) PKC reduced MMP-1, -3, and -9 production by 78 ؎ 9, 76 ؎ 8, and 76 ؎ 5%, respectively. DN-PKC inhibited NF-B DNA binding but did not affect ERK1/2 activation or AP-1 binding. Antisense PKC oligonucleotides and DN-PKC stimulated cell proliferation by 89 ؎ 14% (n ‫؍‬ 4) and 305 ؎ 74% (n ‫؍‬ 3), respectively (both p < 0.05). Our results show that PKC is essential for cytokine-induced up-regulation of MMP-1, -3, and -9, most likely by activating NF-B. Selective inhibition of PKC is therefore a possible strategy to inhibit MMP production in inflammatory diseases such as atherosclerosis. Remodeling of the extracellular matrix (ECM)1 occurs physiologically during morphogenesis, and pathologically in a variety of chronic inflammatory diseases, including atherosclerosis (1). Matrix degrading metalloproteinases (MMPs), which are key enzymes in extracellular matrix turnover at physiological pH, play a dual role in atherosclerosis. They are necessary for the smooth muscle cell migration and proliferation that underlie fibrous cap formation but also mediate the degradation of the ECM that underlies atherosclerotic plaque rupture and aneurysm formation (2). The MMP family of more than 20 proteases includes the gelatinases, collagenases, and stromelysins, which interact synergistically to degrade all the ECM components (3). The gelatinases comprise a 72-kDa gelatinase A (MMP-2), which tends to show constitutive pattern of regulation in connective tissue cells, and a 95-kDa gelatinase B (MMP-9) that is inducible by inflammatory cytokines, especially together with connective tissue growth factors. Both degrade the basement membrane around SMC, which permits SMC migration and proliferation (4 -6). On the other hand, the localization of stromelysin (MMP-3) and collagenase (MMP-1) in macrophage-rich shoulder regions of atheroscle...

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This letter presents a spike-based model that employs neurons with functionally distinct dendritic compartments for classifying high-dimensional binary patterns. The synaptic inputs arriving on each dendritic subunit are nonlinearly processed before being linearly integrated at the soma, giving the neuron the capacity to perform a large number of input-output mappings. The model uses sparse synaptic connectivity, where each synapse takes a binary value. The optimal connection pattern of a neuron is learned by using a simple hardware-friendly, margin-enhancing learning algorithm inspired by the mechanism of structural plasticity in biological neurons. The learning algorithm groups correlated synaptic inputs on the same dendritic branch. Since the learning results in modified connection patterns, it can be incorporated into current event-based neuromorphic systems with little overhead. This work also presents a branch-specific spike-based version of this structural plasticity rule. The proposed model is evaluated on benchmark binary classification problems, and its performance is compared against that achieved using support vector machine and extreme learning machine techniques. Our proposed method attains comparable performance while using 10% to 50% less in computational resource than the other reported techniques.

We present an architecture of a spike based multiclass classifier using neurons with non-linear dendrites and sparse synaptic connectivity where each synapse takes a binary value. The learning in this model happens not through weight updates but through structural changes, i.e. a change of connectivity between inputs and dendrites. Hence, it is well suited for implementation in neuromorphic systems using address event representation (AER). We present a new learning rule that allows better generalization of the system to noisy testing data making it feasible to transfer learnt weights in software to a hardware device interfacing with noisy spiking sensors. The new rule improves testing accuracy by 7 − 10% compared to earlier versions. We also present preliminary results for multi-class classification on handwritten digits from the MNIST database and show that our system can attain comparable performance (≈ 3% more error) with other reported spike based classifiers while using at least 50% less synaptic resources.

Hardware efficient, neuromorphic dendritically enhanced readout for liquid state machines