Faculty Bibliography

Animals process information about many stimulus features simultaneously, swiftly (in a few 100 ms), and robustly (even when individual neurons do not themselves respond reliably). When the brain carries, codes, and certainly when it decodes information, it must do so through some coarse-grained projection mechanism. How can a projection retain information about network dynamics that covers multiple features, swiftly and robustly? Here, by a coarse-grained projection to event trees and to the event chains that comprise these trees, we propose a method of characterizing dynamic information of neuronal networks by using a statistical collection of spatial-temporal sequences of relevant physiological observables (such as sequences of spiking multiple neurons). We demonstrate, through idealized point neuron simulations in small networks, that this event tree analysis can reveal, with high reliability, information about multiple stimulus features within short realistic observation times. Then, with a large-scale realistic computational model of V1, we show that coarse-grained event trees contain sufficient information, again over short observation times, for fine discrimination of orientation, with results consistent with recent experimental observation

We present a detailed theoretical framework for statistical descriptions of neuronal networks and derive (1+1)-dimensional kinetic equations, without introducing any new parameters, directly from conductance-based integrate-and-fire neuronal networks. We describe the details of derivation of our kinetic equation, proceeding from the simplest case of one excitatory neuron, to coupled networks of purely excitatory neurons, to coupled networks consisting of both excitatory and inhibitory neurons. The dimension reduction in our theory is achieved via novel moment closures. We also describe the limiting forms of our kinetic theory in various limits, such as the limit of mean-driven dynamics and the limit of infinitely fast conductances. We establish accuracy of our kinetic theory by comparing its prediction with the full simulations of the original point-neuron networks. We emphasize that our kinetic theory is dynamically accurate, i.e., it captures very well the instantaneous statistical proper-ties of neuronal networks under time-inhomogeneous inputs

Within a large-scale neuronal network model of macaque primary visual cortex, we examined how intrinsic dynamic fluctuations in synaptic currents modify the effect of strong recurrent excitation on orientation selectivity. Previously, we showed that, using a strong network inhibition countered by feedforward and recurrent excitation, the cortical model reproduced many observed properties of simple and complex cells. However, that network's complex cells were poorly selective for orientation, and increasing cortical self-excitation led to network instabilities and unrealistically high firing rates. Here, we show that a sparsity of connections in the network produces large, intrinsic fluctuations in the cortico-cortical conductances that can stabilize the network and that there is a critical level of fluctuations (controllable by sparsity) that allows strong cortical gain and the emergence of orientation-selective complex cells. The resultant sparse network also shows near contrast invariance in its selectivity and, in agreement with recent experiments, has extracellular tuning properties that are similar in pinwheel center and iso-orientation regions, whereas intracellular conductances show positional dependencies. Varying the strength of synaptic fluctuations by adjusting the sparsity of network connectivity, we identified a transition between the dynamics of bistability and without bistability. In a network with strong recurrent excitation, this transition is characterized by a near hysteretic behavior and a rapid rise of network firing rates as the synaptic drive or stimulus input is increased. We discuss the connection between this transition and orientation selectivity in our model of primary visual cortex

In order to identify and understand mechanistically the cortical circuitry of sensory information processing estimates are needed of synaptic input fields that drive neurons. From intracellular in vivo recordings one would like to estimate net synaptic conductance time courses for excitation and inhibition, g(E)(t) and g(I)(t), during time-varying stimulus presentations. However, the intrinsic conductance transients associated with neuronal spiking can confound such estimates, and thereby jeopardize functional interpretations. Here, using a conductance-based pyramidal neuron model we illustrate errors in estimates when the influence of spike-generating conductances are not reduced or avoided. A typical estimation procedure involves approximating the current-voltage relation at each time point during repeated stimuli. The repeated presentations are done in a few sets, each with a different steady bias current. From the trial-averaged smoothed membrane potential one estimates total membrane conductance and then dissects out estimates for g(E)(t) and g(I)(t). Simulations show that estimates obtained during phases without spikes are good but those obtained from phases with spiking should be viewed with skeptism. For the simulations, we consider two different synaptic input scenarios, each corresponding to computational network models of orientation tuning in visual cortex. One input scenario mimics a push-pull arrangement for g(E)(t) and g(I)(t) and idealized as specified smooth time courses. The other is taken directly from a large-scale network simulation of stochastically spiking neurons in a slab of cortex with recurrent excitation and inhibition. For both, we show that spike-generating conductances cause serious errors in the estimates of g(E) and g(I). In some phases for the push-pull examples even the polarity of g(I) is mis-estimated, indicating significant increase when g(I) is actually decreased. Our primary message is to be cautious about forming interpretations based on estimates developed during spiking phases

Hydrogels are frequently employed as medical device biomaterials due to their advantageous biological properties, e.g. resistance to infection and encrustation, biocompatibility; however, their poor mechanical properties generally limit the scope of application to coatings of medical devices. To address this limitation, this study described the formulation of sequential interpenetrating polymer networks (IPN) of poly(-caprolactone) (PCL) and poly(hydroxyethylmethacrylate) (p(HEMA)). IPN containing 20% w/w PCL, p(HEMA), both in the presence or absence of ethyleneglycol dimethacrylate (EGDMA 1% w/w), were prepared by free radical polymerisation. Following preparation the degradation and the mechanical and surface properties of the biomaterials and, in addition, the resistances to microbial adherence and encrustation in vitro were examined. In comparison to p(HEMA) the various IPN exhibited substantially greater tensile properties (ultimate tensile strength, % elongation, Young's modulus) that were accredited to the discrete distribution of PCL within the hydrogel network. The IPN exhibited two glass transition temperatures that were statistically similar to those of the individual components, thereby providing evidence of the immiscible nature of the two polymers. The IPN possessed higher receding contact angles and lower equilibrium water contents in comparison to p(HEMA), whereas the limited degradation of the IPN at both pH 7 and 9 was deemed suitable for clinical usage for periods of at least 4 weeks. The resistances of the various IPN to bacterial adherence and urinary encrustation were examined using in vitro models. Importantly the resistance of the IPN to encrustation was, in general, similar to that of p(HEMA) but greater than that of PCL whereas, the resistance of the IPN to bacterial adherence was frequently greater than that of p(HEMA) and PCL. Therefore, this study has shown that the mechanical properties of p(HEMA) may be substantially increased by the formation of IPN with PCL whilst maintaining other appropriate physicochemical properties and resistances to urinary encrustation and bacterial adherence. It is suggested that these IPN may be suitable for device fabrication thereby expanding the manufacturing application of hydrogels without compromising their potential clinical efficacy

To investigate the existence and the characteristics of possible cortical operating points of the primary visual cortex, as manifested by the coherent spontaneous ongoing activity revealed by real-time optical imaging based on voltage-sensitive dyes, we studied numerically a very large-scale ( approximately 5 x 10(5)) conductance-based, integrate-and-fire neuronal network model of an approximately 16-mm(2) patch of 64 orientation hypercolumns, which incorporates both isotropic local couplings and lateral orientation-specific long-range connections with a slow NMDA component. A dynamic scenario of an intermittent desuppressed state (IDS) is identified in the computational model, which is a dynamic state of (i) high conductance, (ii) strong inhibition, and (iii) large fluctuations that arise from intermittent spiking events that are strongly correlated in time as well as in orientation domains, with the correlation time of the fluctuations controlled by the NMDA decay time scale. Our simulation results demonstrate that the IDS state captures numerically many aspects of experimental observation related to spontaneous ongoing activity, and the specific network mechanism of the IDS may suggest cortical mechanisms and the cortical operating point underlying observed spontaneous activity