Dopaminergic Regulation of Neuronal Circuits in Prefrontal Cortex

Neuromodulators, like dopamine, have considerable influence on the\ud processing capabilities of neural networks. \ud This has for instance been shown in the working memory functions\ud of prefrontal cortex, which may be regulated by altering the\ud dopamine level. Experimental work provides evidence on the biochemical\ud and electrophysiological actions of dopamine receptors, but there are few \ud theories concerning their significance for computational properties \ud (ServanPrintzCohen90,Hasselmo94).\ud We point to experimental data on neuromodulatory regulation of \ud temporal properties of excitatory neurons and depolarization of inhibitory \ud neurons, and suggest computational models employing these effects.\ud Changes in membrane potential may be modelled by the firing threshold,\ud and temporal properties by a parameterization of neuronal responsiveness \ud according to the preceding spike interval.\ud We apply these concepts to two examples using spiking neural networks.\ud In the first case, there is a change in the input synchronization of\ud neuronal groups, which leads to\ud changes in the formation of synchronized neuronal ensembles.\ud In the second case, the threshold\ud of interneurons influences lateral inhibition, and the switch from a \ud winner-take-all network to a parallel feedforward mode of processing.\ud Both concepts are interesting for the modeling of cognitive functions and may\ud have explanatory power for behavioral changes associated with dopamine \ud regulation

SHAREDWEALTH: A CRYPTOCURRENCY TO REWARD MINERS EVENLY

Bitcoin [19] is a decentralized cryptocurrency that has recently gained popularity and has emerged as a popular medium of exchange. The total market capitalization is around 1.5 billion US dollars as of October 2013 [28]. All the operations of Bitcoin are maintained in a distributed public global ledger known as a block chain which consists of all the successful transactions that have ever taken place. The security of a block chain is maintained by a chain of cryptographic puzzles solved by participants called miners, who in return are rewarded with bitcoins. To be successful, the miner has to put in his resources to solve the cryptographic puzzle (also known as a proof of work). The reward structure is an incentive for miners to contribute their computational resources and is also essential to the currency\u27s decentralized nature. One disadvantage of the reward structure is that the payment system is uneven. The reward is always given to one person. Hence people form mining pools where every member of the pool solves the same cryptographic puzzle and irrespective of the person who solved it, the reward is shared evenly among all the members of the pool. The Bitcoin protocol assumes that the miners are honest and they follow the Bitcoin protocol as prescribed. If group of selfish miners comes to lead by forming pools, the currency stops being decentralized and comes under the control of the selfish miners. Such miners can control the whole Bitcoin network [29]. Our goal is to address this problem by creating a distinct peer-to-peer protocol that reduces the incentives for the miners to join large mining pools. The central idea is to pay the “runners-up” who come close to finding a proof, thereby creating a less volatile payout situation. The work done by the “runners-up” can be used by other miners to find the solution of proof of work by building upon their work. Once they find the actual solution they have to include the solution of the other miner in order to get rewarded. The benefit of this protocol is that not only the miners save their computational resources but also the reward is distributed among the miners

Distributed ARTMAP

Distributed coding at the hidden layer of a multi-layer perceptron (MLP) endows the network with memory compression and noise tolerance capabilities. However, an MLP typically requires slow off-line learning to avoid catastrophic forgetting in an open input environment. An adaptive resonance theory (ART) model is designed to guarantee stable memories even with fast on-line learning. However, ART stability typically requires winner-take-all coding, which may cause category proliferation in a noisy input environment. Distributed ARTMAP (dARTMAP) seeks to combine the computational advantages of MLP and ART systems in a real-time neural network for supervised learning. This system incorporates elements of the unsupervised dART model as well as new features, including a content-addressable memory (CAM) rule. Simulations show that dARTMAP retains fuzzy ARTMAP accuracy while significantly improving memory compression. The model's computational learning rules correspond to paradoxical cortical data.Office of Naval Research (N00014-95-1-0409, N00014-95-1-0657

dARTMAP: A Neural Network for Fast Distributed Supervised Learning

Distributed coding at the hidden layer of a multi-layer perceptron (MLP) endows the network with memory compression and noise tolerance capabilities. However, an MLP typically requires slow off-line learning to avoid catastrophic forgetting in an open input environment. An adaptive resonance theory (ART) model is designed to guarantee stable memories even with fast on-line learning. However, ART stability typically requires winner-take-all coding, which may cause category proliferation in a noisy input environment. Distributed ARTMAP (dARTMAP) seeks to combine the computational advantages of MLP and ART systems in a real-time neural network for supervised learning, An implementation algorithm here describes one class of dARTMAP networks. This system incorporates elements of the unsupervised dART model as well as new features, including a content-addressable memory (CAM) rule for improved contrast control at the coding field. A dARTMAP system reduces to fuzzy ARTMAP when coding is winner-take-all. Simulations show that dARTMAP retains fuzzy ARTMAP accuracy while significantly improving memory compression.National Science Foundation (IRI-94-01659); Office of Naval Research (N00014-95-1-0409, N00014-95-0657

An Online Unsupervised Structural Plasticity Algorithm for Spiking Neural Networks

In this article, we propose a novel Winner-Take-All (WTA) architecture employing neurons with nonlinear dendrites and an online unsupervised structural plasticity rule for training it. Further, to aid hardware implementations, our network employs only binary synapses. The proposed learning rule is inspired by spike time dependent plasticity (STDP) but differs for each dendrite based on its activation level. It trains the WTA network through formation and elimination of connections between inputs and synapses. To demonstrate the performance of the proposed network and learning rule, we employ it to solve two, four and six class classification of random Poisson spike time inputs. The results indicate that by proper tuning of the inhibitory time constant of the WTA, a trade-off between specificity and sensitivity of the network can be achieved. We use the inhibitory time constant to set the number of subpatterns per pattern we want to detect. We show that while the percentage of successful trials are 92%, 88% and 82% for two, four and six class classification when no pattern subdivisions are made, it increases to 100% when each pattern is subdivided into 5 or 10 subpatterns. However, the former scenario of no pattern subdivision is more jitter resilient than the later ones.Comment: 11 pages, 10 figures, journa