Automatic Construction of Predictive Neuron Models through Large Scale Assimilation of Electrophysiological Data.

We report on the construction of neuron models by assimilating electrophysiological data with large-scale constrained nonlinear optimization. The method implements interior point line parameter search to determine parameters from the responses to intracellular current injections of zebra finch HVC neurons. We incorporated these parameters into a nine ionic channel conductance model to obtain completed models which we then use to predict the state of the neuron under arbitrary current stimulation. Each model was validated by successfully predicting the dynamics of the membrane potential induced by 20-50 different current protocols. The dispersion of parameters extracted from different assimilation windows was studied. Differences in constraints from current protocols, stochastic variability in neuron output, and noise behave as a residual temperature which broadens the global minimum of the objective function to an ellipsoid domain whose principal axes follow an exponentially decaying distribution. The maximum likelihood expectation of extracted parameters was found to provide an excellent approximation of the global minimum and yields highly consistent kinetics for both neurons studied. Large scale assimilation absorbs the intrinsic variability of electrophysiological data over wide assimilation windows. It builds models in an automatic manner treating all data as equal quantities and requiring minimal additional insight

Graphite-polydimethylsiloxane strain sensors for embedded structural health monitoring

Here we describe the synthesis and testing of miniature sensors for structural health monitoring. The sensors were incorporated into building materials to detect strain relaxation events within the region of elastic deformation prior to the onset of plastic deformation. Our sensors consisted of thin composite films of polydimethylsiloxane and graphite nanoparticles that conduct via tunnelling percolation. Tunnelling-percolation through elastomers with low Young's modulus is highly sensitive to deformation and gives a large piezoresistance which we use to infer the local strain. The response of sensors embedded in calcium aluminate mortar, Portland cement mortar, and very fine sand columns was compared under stress. We show that the sensors can detect micro-events where strain is redistributed locally from one direction to another and may thus be used to monitor structural integrity and provide early warning of material disaggregation.</p

Inhibition Delay Increases Neural Network Capacity through Stirling Transform

Inhibitory neural networks are found to encode high volumes of information through delayed inhibition. We show that inhibition delay increases storage capacity through a Stirling transform of the minimum capacity which stabilizes locally coherent oscillations. We obtain both the exact and asymptotic formulas for the total number of dynamic attractors. Our results predict a (ln 2) −N -fold increase in capacity for an N-neuron network and demonstrate high-density associative memories which host a maximum number of oscillations in analog neural devices

Experimental observation of multi-stability and dynamic attractors in silicon central pattern generators

We report on the multistability of chaotic networks of silicon neurons and demonstrate how spatiotemporal sequences of voltage oscillations are selected with timed current stimuli. A 3 neuron central pattern generator was built by interconnecting Hodgkin-Huxley neurons with mutually inhibitory links mimicking gap junctions. By systematically varying the timing of current stimuli applied to individual neurons, we generate the phase lag maps of neuronal oscillators and study their dependence on the network connectivity. We identify up to 6 attractors consisting of triphasic sequences of unevenly spaced pulses propagating clockwise and anti-clockwise. While confirming theoretical predictions, our experiments reveal more complex oscillatory patterns shaped by the ratio of the pulse width to the oscillation period. Our work contributes to validating the command neuron hypothesis.<br/