Efficient temporal processing with biologically realistic dynamic synapses.

Synapses play a central role in neural computation: the strengths of synaptic connections determine the function of a neural circuit. In conventional models of computation, synaptic strength is assumed to be a static quantity that changes only on the slow timescale of learning. In biological systems, however, synaptic strength undergoes dynamic modulation on rapid timescales through mechanisms such as short term facilitation and depression. Here we describe a general model of computation that exploits dynamic synapses, and use a backpropagation-like algorithm to adjust the synaptic parameters. We show that such gradient descent suffices to approximate a given quadratic filter by a rather small neural system with dynamic synapses. We also compare our network model to artificial neural networks designed for time series processing. Our numerical results are complemented by theoretical analyses which show that even with just a single hidden layer such networks can approximate a surprisingly large class of nonlinear filters: all filters that can be characterized by Volterra series. This result is robust with regard to various changes in the model for synaptic dynamics.

Neural representation and the cortical code.

The principle function of the central nervous system is to represent and transform information and thereby mediate appropriate decisions and behaviors. The cerebral cortex is one of the primary seats of the internal representations maintained and used in perception, memory, decision making, motor control, and subjective experience, but the basic coding scheme by which this information is carried and transformed by neurons is not yet fully understood. This article defines and reviews how information is represented in the firing rates and temporal patterns of populations of cortical neurons, with a particular emphasis on how this information mediates behavior and experience.

Dynamic stochastic synapses as computational units.

In most neural network models, synapses are treated as static weights that change only with the slow time scales of learning. It is well known, however, that synapses are highly dynamic and show use-dependent plasticity over a wide range of time scales. Moreover, synaptic transmission is an inherently stochastic process: a spike arriving at a presynaptic terminal triggers the release of a vesicle of neurotransmitter from a release site with a probability that can be much less than one. We consider a simple model for dynamic stochastic synapses that can easily be integrated into common models for networks of integrate-and-fire neurons (spiking neurons). The parameters of this model have direct interpretations in terms of synaptic physiology. We investigate the consequences of the model for computing with individual spikes and demonstrate through rigorous theoretical results that the computational power of the network is increased through the use of dynamic synapses.

Natural scene statistics at the centre of gaze.

Early stages of visual processing may exploit the characteristic structure of natural visual stimuli. This structure may differ from the intrinsic structure of natural scenes, because sampling of the environment is an active process. For example, humans move their eyes several times a second when looking at a scene. The portions of a scene that fall on the fovea are sampled at high spatial resolution, and receive a disproportionate fraction of cortical processing. We recorded the eye positions of human subjects while they viewed images of natural scenes. We report that active selection affected the statistics of the stimuli encountered by the fovea, and also by the parafovea up to eccentricities of 4 degrees. We found two related effects. First, subjects looked at image regions that had high spatial contrast. Second, in these regions, the intensities of nearby image points (pixels) were less correlated with each other than in images selected at random. These effects could serve to increase the information available to the visual system for further processing. We show that both of these effects can be simply obtained by constructing an artificial ensemble comprised of the highest-contrast regions of images.

Efficient discrimination of temporal patterns by motion-sensitive neurons in primate visual cortex.

Although motion-sensitive neurons in macaque middle temporal (MT) area are conventionally characterized using stimuli whose velocity remains constant for 1-3 s, many ecologically relevant stimuli change on a shorter time scale (30-300 ms). We compared neuronal responses to conventional (constant-velocity) and time-varying stimuli in alert primates. The responses to both stimulus ensembles were well described as rate-modulated Poisson processes but with very high precision (approximately 3 ms) modulation functions underlying the time-varying responses. Information-theoretic analysis revealed that the responses encoded only approximately 1 bit/s about constant-velocity stimuli but up to 29 bits/s about the time-varying stimuli. Analysis of local field potentials revealed that part of the residual response variability arose from "noise" sources extrinsic to the neuron. Our results demonstrate that extrastriate neurons in alert primates can encode the fine temporal structure of visual stimuli.

Impact of synaptic unreliability on the information transmitted by spiking neurons.

The spike generating mechanism of cortical neurons is highly reliable, able to produce spikes with a precision of a few milliseconds or less. The excitatory synapses driving these neurons are by contrast much less reliable, subject both to release failures and quantal fluctuations. This suggests that synapses represent the primary bottleneck limiting the faithful transmission of information through cortical circuitry. How does the capacity of a neuron to convey information depend on the properties of its synaptic drive? We address this question rigorously in an information theoretic framework. We consider a model in which a population of independent unreliable synapses provides the drive to an integrate-and-fire neuron. Within this model, the mutual information between the synaptic drive and the resulting output spike train can be computed exactly from distributions that depend only on a single variable, the interspike interval. The reduction of the calculation to dependence on only a single variable greatly reduces the amount of data required to obtain reliable information estimates. We consider two factors that govern the rate of information transfer: the synaptic reliability and the number of synapses connecting each presynaptic axon to its postsynaptic target (i.e., the connection redundancy, which constitutes a special form of input synchrony). The information rate is a smooth function of both mechanisms; no sharp transition is observed from an "unreliable" to a "reliable" mode. Increased connection redundancy can compensate for synaptic unreliability, but only under the assumption that the fine temporal structure of individual spikes carries information. If only the number of spikes in some relatively long-time window carries information (a "mean rate" code), an increase in the fidelity of synaptic transmission results in a seemingly paradoxical decrease in the information available in the spike train. This suggests that the fine temporal structure of spike trains can be used to maintain reliable transmission with unreliable synapses.

Input synchrony and the irregular firing of cortical neurons.

Cortical neurons in the waking brain fire highly irregular, seemingly random, spike trains in response to constant sensory stimulation, whereas in vitro they fire regularly in response to constant current injection. To test whether, as has been suggested, this high in vivo variability could be due to the postsynaptic currents generated by independent synaptic inputs, we injected synthetic synaptic current into neocortical neurons in brain slices. We report that independent inputs cannot account for this high variability, but this variability can be explained by a simple alternative model of the synaptic drive in which inputs arrive synchronously. Our results suggest that synchrony may be important in the neural code by providing a means for encoding signals with high temporal fidelity over a population of neurons.

Synaptic transmission: noisy synapses and noisy neurons.

The finding that the synapses relaying sensory input to the cortex may have different properties than intracortical synapses has implications not only for sensory processing, but for the role of noise in neural computation as well.

Electrotonic architecture of hippocampal CA1 pyramidal neurons based on three-dimensional reconstructions.

1. The spread of electrical signals in pyramidal neurons from the CA1 field of rat hippocampus was investigated through multicompartmental modeling based on three-dimensional morphometric reconstructions of four of these cells. These models were used to dissect the electrotonic architecture of these neurons, and to evaluate the equivalent cylinder approach that this laboratory and others have previously applied to them. Robustness of results was verified by the use of wide ranges of values of specific membrane resistance (Rm) and cytoplasmic resistivity. 2. The anatomy exhibited extreme departures from a key assumption of the equivalent cylinder model, the so-called "3/2 power law." 3. The compartmental models showed that the frequency distribution of steady-state electrotonic distances between the soma and the dendritic terminations was multimodal, with a large range and a sizeable coefficient of variation. This violated another central assumption of the equivalent cylinder model, namely, that all terminations are electronically equidistant from the soma. This finding, which was observed both for "centrifugal" (away from the soma) and "centripetal" (toward the soma) spread of electrical signals, indicates that the concept of an equivalent electrotonic length for the whole dendritic tree is not appropriate for these neurons. 4. The multiple peaks in the electrotonic distance distributions, whether for centrifugal or centripetal voltage transfer, were clearly related to the laminar organization of synaptic afferents in the CA1 region. 5. The results in the three preceding paragraphs reveal how little of the electrotonic architecture of these neurons is captured by a simple equivalent cylinder model. The multicompartmental model is more appropriate for exploring synaptic signaling and transient events in CA1 pyramidal neurons. 6. There was significant attenuation of synaptic potential, current, and charge as they spread from the dendritic tree to the soma. Charge suffered the least and voltage suffered the most attenuation. Attenuation depended weakly on Rm and strongly on synaptic location. Delay of time to peak was more distorted for voltage than for current and was more affected by Rm. 7. Adequate space clamp is not possible for most of the synapses on these cells. Application of a somatic voltage clamp had no significant effect on voltage transients in the subsynaptic membrane. 8. The possible existence of steep voltage gradients within the dendritic tree is consistent with the idea that there can be some degree of local processing and that different regions of the neuron may function semiautonomously. These spatial gradients are potentially relevant to synaptic plasticity in the hippocampus, and they also suggest caution in interpreting some neurophysiological results.

The morphoelectrotonic transform: a graphical approach to dendritic function.

Electrotonic structure of dendrites plays a critical role in neuronal computation and plasticity. In this article we develop two novel measures of electrotonic structure that describe intraneuronal signaling in dendrites of arbitrary geometry. The log-attenuation Lij measures the efficacy, and the propagation delay Pij the speed, of signal transfer between any two points i and j. These measures are additive, in the sense that if j lies between i and k, the total distance Lik is just the sum of the partial distances: Lik = Lij + Ljk, and similarly Pik = Pij + Pjk. This property serves as the basis for the morphoelectrotonic transform (MET), a graphical mapping from morphological into electrotonic space. In a MET, either Pij or Lij replace anatomical distance as the fundamental unit and so provide direct functional measures of intraneuronal signaling. The analysis holds for arbitrary transient signals, even those generated by nonlinear conductance changes underlying both synaptic and action potentials. Depending on input location and the measure of interest, a single neuron admits many METs, each emphasizing different functional consequences of the dendritic electrotonic structure. Using a single layer 5 cortical pyramidal neuron, we illustrate a collection of METs that lead to a deeper understanding of the electrical behavior of its dendritic tree. We then compare this cortical cell to representative neurons from other brain regions (cortical layer 2/3 pyramidal, region CA1 hippocampal pyramidal, and cerebellar Purkinje). Finally, we apply the MET to electrical signaling in dendritic spines, and extend this analysis to calcium signaling within spines. Our results demonstrate that the MET provides a powerful tool for obtaining a rapid and intuitive grasp of the functional properties of dendritic trees.

Linearized models of calcium dynamics: formal equivalence to the cable equation.

The dynamics of calcium and other diffusible second messengers play an important role in intracellular signaling. We show here the conditions under which nonlinear equations governing the diffusion, extrusion, and buffering of calcium can be linearized. Because the resulting partial differential equation is formally identical to the one-dimensional cable equation, quantities analogous to the input resistance, space constant, and time constant--familiar from the study of passive electrical propagation--can be defined. Using simulated calcium dynamics in an infinite cable and in a dendritic spine as examples, we bound the errors due to the linearization, and show that parameter uncertainty is so large that most nonlinearities can usually be ignored: robust phenomena in the nonlinear model are also present in the linear model.

[Gluing of sterile abdominal benign cyst walls with tissue adhesives (Tissucol) (preliminary report)]. / Steril, benignus hasi folyadékgyülemek falainak összeragasztása szöveti ragasztóval (tissucol) (elözetes közlemény).

The authors, endeavouring to stick the walls of percutaneously emptied cysts together had applied the two-component Tissucol fibrin glue in the treatment of abdominal and thoracic cysts. Their ten cases, however, may be considered only as preliminary experiences. It seems that the method can be recommended for an attempt to cease these formations in a fast and simple way. Nevertheless, this method must not be the subject of treatment in infected and malignant fluid collections, but seems successful in pancreatic pseudocysts, in spite of their higher proteolytic activity.