Search

Our technical ability to collect data about biological systems far outpaces our ability to understand them. Historically, for example, we have had complete and explicit genomes for almost two decades, but we still have no idea what many genes do. More recently a similar situation has arisen, where we can reconstruct huge neural circuits, and/or watch them operate in the brain, but still don't know how they work. This talk covers this second and newer problem, understanding neural circuits. We introduce a variety of computational tools currently being used to attack this data-rich, understanding-poor problems. Examples include dimensionality reduction for nonlinear systems, looking for known and proposed circuits, and using machine learning for parameter estimation. One general theme is the use of biological priors, to help fill in unknowns, see if proposed solutions are feasible, and more generally aid understanding. [ABSTRACT FROM AUTHOR]

Network on Chip (NoC) is a communication framework for interconnection of processing element (PE's). The excessive and parallel communication requirements of heterogeneous PE's in NoC have made the communication structure very complex. The size of the devices are scaled down to support the complexity but the size of interconnects remains the same. To support the complexity of NoC many fault tolerant routing techniques have been proposed to cope with the faulty nodes and interconnects which occur during the communication. All the routing algorithms lack the adaptiveness, robustness and they also have the drawbacks of congestion, delayed communication and latency issues. In this paper a synaptogenesis bases bio-inspired algorithm is proposed for NoC. This algorithm is fault tolerant as it adapts the mechanism of biological brain. The results show that the bio-inspired algorithm quickly recovers from the fault and it is robust and fault tolerant. The average packet network latency was increased by 11.64%, average bandwidth was reduced by 0.07% and throughput is drop to 43.30% during the network recovery from faults. While comparing with literature technique the average throughput and accepted traffic is increased by 76.92%. [ABSTRACT FROM PUBLISHER]

This work presents a CMOS technique for designing and implementing a biologically inspired neuron which will accept multiple synaptic inputs. The circuit accepts synapses as inputs and generates a pulse width modulated output waveform of constant frequency depending on the level of activation. Next, the behavior of this implementation has been presented, and the realization of various basic logic gates through this combination has been realized. [ABSTRACT FROM PUBLISHER]

Memristors have been rediscovered recently and then gained increasing attentions. Their unique properties, such as high density, nonvolatility, and recording historic behavior of current (or voltage) profile, have inspired the creation of memristor-based neuromorphic computing architecture. Rather than the existing crossbar-based neuron network designs, we focus on memristor-based synapse and the corresponding training circuit to mimic the real biological system. In this paper, first, the basic synapse design is presented. On top of it, we will discuss the training sharing scheme and explore design implication on multi-synapse neuron system. Energy saving method such as self-training is also investigated. [ABSTRACT FROM PUBLISHER]

Chemical synaptic transmisssion involves the release of a neurotransmitter that diffuses in the extracellular space and interacts with specific receptors located on the postsynaptic membrane. Computer simulation approaches provide fundamental tools for exploring various aspects of the synaptic transmission under different conditions. In particular, Monte Carlo methods can track the stochastic movements of neurotransmitter molecules and their interactions with other discrete molecules, the receptors. We have developed synaptic models that had a simple geometry as far as shape is concerned, but had a variable set of parameters that specified the dimensions of the structures involved in the synaptic junction. We have analyzed simulations based on these idealized models of excitatory synapses where AMPA receptors are present and the neurotransmitter involved is glutamate. [ABSTRACT FROM PUBLISHER]

A simple electronic circuit is proposed as an analog of the postsynaptic membrane of neuron. In this circuit, the variable conductance of postsynaptic membrane of neuron dependence on the acetylcholine-receptor binding activity is represented by enzyme field effect transistor (ENFET) sensitive to acetylcholine. ENFET functions not only as a voltage controlled conductance but can also provide a means of measurement of specific neurotransmitters that bind with the receptor sites of postsynaptic membrane. The simulated sodium conductance is reproduced satisfactorily and the simulated excitatory post synaptic potential is very similar to the experimentally recorded one. Since this analog contains only a few electronic elements, it is small and inexpensive to build. The simplicity may make it an ideal unit to build complex neuron networks. [ABSTRACT FROM PUBLISHER]

A variant of the generalized delta rule training algorithm is presented for unsupervized learning in a single-layer network using Gaussian function nonlinearities. An alternative to direct competition between the output nodes that allows linear instead of quadratic complexity in the connections of the output nodes is proposed. The connecting weights are adaptively modified with each presentation of an input pattern and converge towards values that are representative of the clustering structure of the input data. In order to calculate the weight increment, the training algorithm uses locally available information at the synapse and its connecting neuron. The training algorithm, coupled with the alternative competition approach, eliminates the need for an orienting subsystem such as that needed in the ART1 model of G.A. Carpenter and S. Grossberg (1988) [ABSTRACT FROM PUBLISHER]

The traditional feedforward neural network is a static structure which simply maps input to output. To better reflect the dynamics in a biological system, a network structure which models each synapse by a finite-impulse response (FIR) linear filter is proposed. An efficient-gradient descent algorithm which is shown to be a temporal generalization of the familiar backpropagation algorithm is derived. By modeling each synapse as a linear filter, the neural network as a whole may be thought of as an adaptive system with its own internal dynamics. Equivalently, one may think of the network as a complex nonlinear filter. Applications should thus include areas of pattern recognition where there is an inherent temporal quality to the data, such as in speech recognition. The networks should also find a natural use in areas of nonlinear control, and other adaptive signal processing and filtering applications such as noise cancellation or equalization [ABSTRACT FROM PUBLISHER]