The early development of neutron diffraction: science in the wings of the Manhattan Project.

Although neutron diffraction was first observed using radioactive decay sources shortly after the discovery of the neutron, it was only with the availability of higher intensity neutron beams from the first nuclear reactors, constructed as part of the Manhattan Project, that systematic investigation of Bragg scattering became possible. Remarkably, at a time when the war effort was singularly focused on the development of the atomic bomb, groups working at Oak Ridge and Chicago carried out key measurements and recognized the future utility of neutron diffraction quite independent of its contributions to the measurement of nuclear cross sections. Ernest O. Wollan, Lyle B. Borst and Walter H. Zinn were all able to observe neutron diffraction in 1944 using the X-10 graphite reactor and the CP-3 heavy water reactor. Subsequent work by Wollan and Clifford G. Shull, who joined Wollan's group at Oak Ridge in 1946, laid the foundations for widespread application of neutron diffraction as an important research tool.

Activity of primate V1 cortical neurons during blinks.

Every time we blink our eyes, the image on the retina goes almost completely dark. And yet we hardly notice these interruptions, even though an external darkening is startling. Intuitively it would seem that if our perception is continuous, then the neuronal activity on which our perceptions are based should also be continuous. To explore this issue, we compared the responses of 63 supragranular V1 neurons recorded from two awake monkeys for four conditions: 1) constant stimulus, 2) during a reflex blink, 3) during a gap in the visual stimulus, and 4) during an external darkening when an electrooptical shutter occluded the entire scene. We show here that the activity of neurons in visual cortical area V1 is essentially shut off during a blink. In the 100-ms epoch starting 70 ms after the stimulus was interrupted, the firing rate was 27.2 +/- 2.7 spikes/s (SE) for a constant stimulus, 8.2 +/- 0.9 spikes/s for a reflex blink, 17.3 +/- 1.9 spikes/s for a gap, and 12.7 +/- 1.4 spikes/s for an external darkening. The responses during a blink are less than during an external darkening (P < 0.05, t-test). However, many of these neurons responded with a transient burst of activity to the onset of an external darkening and not to a blink, suggesting that it is the suppression of this transient which causes us to ignore blinks. This is consistent with other studies where the presence of transient bursts of activity correlates with the perceived visibility of a stimulus.

The simultaneous coding of orientation and contrast in the responses of V1 complex cells.

The responses of 30 V1 complex cells were recorded using a complete set of transiently presented, oriented stimuli of different contrasts. A back-propagation neural network was used to predict the multivariate visual stimuli from the neuronal responses on a trial-by-trial basis. For single neurons, the strength of the response was much better at predicting the orientation of a visual stimulus than its contrast. Using the temporal modulation of the response improved the ability to predict the contrast of a stimulus without affecting the ability to predict the orientation. Removing stimulus latency from the responses, by time-shifting the individual responses an amount equal to the average latency, significantly reduced the ability to predict stimulus contrast, demonstrating that the response latency is reliable enough, even for a single neuron and a single trial, for it to be used to help determine stimulus contrast. Pooling the responses from a group of 11 neurons demonstrated that small groups of neurons can accurately represent multivariate stimuli in a single trial.

Temporal coding as a means of information transfer in the primate visual system.

The issue of how neurons communicate with each other through patterns of action potentials, that is, of what is the neural code, is one of the major problems in modern science. Because complex stimuli can be easily and rapidly presented to the visual system, and because vision is both behaviorally important to and occupies a large amount of neural tissue in humans, a great deal of the research on the neuronal code has been done in the primate visual system. One of the more challenging aspects of this research concerns how the time-varying nature of neuronal responses might be used by the nervous system. This review addresses some of the major lines of investigation as to how the temporal variation of a neural response might function in transferring information in the primate visual system.

Neuronal codes: reading them and learning how their structure influences network organization.

Our investigations of the primate visual system show that neuronal responses carry information in a multi-dimensional code that is superimposed onto the response envelope in a slow time varying fashion. The precision of timing is 30 ms or more. In primary visual cortex response latency and response strength are largely independent, with latency more closely coding contrast or visibility and strength more closely coding stimulus orientation, or perhaps shape. Adjacent neurons in both V1 and inferior temporal cortex share only about 10% of their stimulus-related information, which we demonstrate to be consistent with the idea that cortical layers were organized to minimize information loss.

Insensitivity of V1 complex cell responses to small shifts in the retinal image of complex patterns.

An important role for neurons in the early visual system is to convey information about the structure of visual stimuli. However, neuronal responses show substantial variation across presentations of the same stimulus. In awake monkeys, it has been assumed that a great deal of this variation is related to the scatter in eye position (inducing scatter in the retinal position of the stimulus). Here we investigate the implied consequence of this assumption, i.e., that the scatter variation in eye position degrades the decodability of the neural response. We recorded from 50 complex cells in primary visual cortex of fixating monkeys while different complex stimuli were presented. Three types of retinal shifts were considered: natural scatter in the fixation, systematic fixation point shift, and systematic stimulus position shift. The stimulus pattern accounts for >50% of the response variance, always six times that accounted for by the scatter in eye position during fixation. The retinal location of a stimulus had to be shifted by 10-12 min of arc, an amount almost two times larger than the smallest picture element, before the responses changed systematically. Nonetheless, changes of the stimulus at the single pixel level often gave rise to discriminable responses. Thus complex cells convey information about the spatial structure of a stimulus, independent of rigid stimulus displacements on the order of the receptive field size or smaller.

Latency: another potential code for feature binding in striate cortex.

1. We recorded the responses of 37 striate cortical complex cells in fixating monkeys while presenting a set of oriented stimuli that varied in contrast. 2. The two response parameters of strength and latency can be interpreted as a code: the strength defines the stimulus form (here the orientation), and the latency is more a function of the stimulus contrast. 3. Synchronization based on latency could make a strong contribution to the process of organizing the neural responses to different objects, i.e., binding.

Adjacent visual cortical complex cells share about 20% of their stimulus-related information.

The responses of adjacent neurons in inferior temporal (IT) cortex carry signals that are to a large degree independent (Gawne and Richmond, 1993). Adjacent primary visual cortical neurons have similar orientation tuning (Hubel and Wiesel, 1962, 1968), suggesting that their responses might be more redundant than those in IT. We recorded the responses of 26 pairs of adjacent complex cells in the primary visual cortex of two awake monkeys while using both a set of 16 bar-like stimuli, and a more complex set of 128 two-dimensional patterns. Linear regression showed that 40% of the signal variance of one neuron was related to that of the other when the responses to the bar-like stimuli were considered. However, when the responses to the two-dimensional stimuli were included in the analysis, only 19% of the signal variance of one neuron was related to that of the adjacent one, almost exactly the same results as found in IT. An information theoretic analysis gave similar results. We hypothesize that this trend toward independence of information processing by adjacent cortical neurons is a general organizational strategy used to maximize the amount of information carried in local groups.

How independent are the messages carried by adjacent inferior temporal cortical neurons?

There are at least three possibilities for encoding information in a small area of cortex. First, neurons could have identical characteristics, thus conveying redundant information; second, neurons could give different responses to the same stimuli, thus conveying independent information; or third, neurons could cooperate with each other to encode more information jointly than they do separately, that is, synergistically. We recorded from 28 pairs of neurons in inferior temporal cortex of behaving rhesus monkeys. Each pair was recorded from a single microelectrode. Both the magnitude and the temporal modulation of the responses were quantified. We separated the responses into signal (average response to each stimulus) and noise (deviation of each response from the average). Linear regression showed that an average of only 18.7% of the magnitude of the signal carried by one neuron could be predicted from the magnitude of the other, and only 22.0% could be predicted by including the temporal modulation. For the noise, the figures were 5.5% and 6.3%, respectively, even less than for the signal. Information theoretic analysis shows that the pairs of neurons we studied carried an average of 20% redundant information. However, even this relatively small amount of redundancy places a severe upper limit on the information that can be transmitted by a neuronal pool. A pool of neurons for which each pair is mutually redundant to extent y can only carry a maximum of 1/y, here five times, as much information as one neuron alone. Information theoretic analysis gave no evidence for the presence of information as a function of both neurons considered together, that is, synergistic codes. Cross-correlation showed that at least 61% of the neuronal pairs shared connections in some manner. Given these shared connections, if adjacent neurons had had identical characteristics, then the noise on the outputs of these neurons would have been highly correlated, and it would not be possible to separate the signal and noise. The severe impact of correlated noise and information redundancy leads us to propose that the processing carried out by these neurons evolved both to provide a rich description of many stimulus properties and simultaneously to minimize the redundancy in a local group of neurons. These two principles appear to be a major constraint on the organization of inferior temporal, and possibly all, cortex.

Lateral geniculate neurons in behaving primates. I. Responses to two-dimensional stimuli.

1. Using behaving monkeys, we studied the visual responses of single neurons in the parvocellular layers of the lateral geniculate nucleus (LGN) to a set of two-dimensional black and white patterns. We found that monkeys could be trained to make sufficiently reliable and stable fixations to enable us to plot and characterize the receptive fields of individual neurons. A qualitative examination of rasters and a statistical analysis of the data revealed that the responses of neurons were related to the stimuli. 2. The data from 5 of the 13 "X-like" neurons in our sample indicated the presence of antagonistic center and surround mechanisms and linear summation of luminance within center and surround mechanisms. We attribute the lack of evidence for surround antagonism in the eight neurons that failed to exhibit center-surround antagonism either to a mismatch between the size of the pixels in the stimuli and the size of the receptive field or to the lack of a surround mechanism (i.e., the type II neurons of Wiesel and Hubel). 3. The data from five other neurons confirm and extend previous reports indicating that the surround regions of X-like neurons can have nonlinearities. The responses of these neurons were not modulated when a contrast-reversing, bipartite stimulus was centered on the receptive field, which suggests a linear summation within the center and surround mechanisms. However, it was frequently the case for these neurons that stimuli of identical pattern but opposite contrast elicited responses of similar polarity, which indicates nonlinear behavior. 4. We found a wide variety of temporal patterns in the responses of individual LGN neurons, which included differences in the magnitude, width, and number of peaks of the initial on-transient and in the magnitude of the later sustained component. These different temporal patterns were repeatable and clearly different for different visual patterns. These results suggest that visual information may be carried in the shape as well as in the amplitude of the response waveform.

Lateral geniculate neurons in behaving primates. II. Encoding of visual information in the temporal shape of the response.

1. We used the Karhunen-Loève (K-L) transform to quantify the temporal distribution of spikes in the responses of lateral geniculate (LGN) neurons. The basis functions of the K-L transform are a set of waveforms called principal components, which are extracted from the data set. The coefficients of the principal components are uncorrelated with each other and can be used to quantify individual responses. The shapes of each of the first three principal components were very similar across neurons. 2. The coefficient of the first principal component was highly correlated with the spike count, but the other coefficients were not. Thus the coefficient of the first principal component reflects the strength of the response, whereas the coefficients of the other principal components reflect aspects of the temporal distribution of spikes in the response that are uncorrelated with the strength of the response. Statistical analysis revealed that the coefficients of up to 10 principal components were driven by the stimuli. Therefore stimuli govern the temporal distribution as well as the number of spikes in the response. 3. Through the application of information theory, we were able to compare the amount of stimulus-related information carried by LGN neurons when two codes were assumed: first, a univariate code based on response strength alone; and second, a multivariate temporal code based on the coefficients of the first three principal components. We found that LGN neurons were able to transmit an average of 1.5 times as much information using the three-component temporal code as they could using the strength code. 4. The stimulus set we used allowed us to calculate the amount of information each neuron could transmit about stimulus luminance, pattern, and contrast. All neurons transmitted the greatest amount of information about stimulus luminance, but they also transmitted significant amounts of information about stimulus pattern. This pattern information was not a reflection of the luminance or contrast of the pixel centered on the receptive field. 5. In addition to measuring the average amount of information each neuron transmitted about all stimuli, we also measured the amount of information each neuron transmitted about the individual stimuli with both the univariate spike count code and the multivariate temporal code. We then compared the amount of information transmitted per stimulus with the magnitudes of the responses to the individual stimuli. We found that the magnitudes of both the univariate and the multivariate responses to individual stimuli were poorly correlated with the information transmitted about the individual stimuli.(ABSTRACT TRUNCATED AT 400 WORDS)

Lateral geniculate neurons in behaving primates. III. Response predictions of a channel model with multiple spatial-to-temporal filters.

1. For the experiments reported in these papers, we recorded the responses of lateral geniculate (LGN) neurons to a large set of two-dimensional, black and white patterns based on Walsh functions and to a set of test stimuli. In the first two papers we reported that these neurons encode stimulus-related information in both the strength and the shape of the response waveforms and that there are more than two independent components in the response. These results cannot be explained by existing models. This paper provides a model of LGN neurons that not only accounts for the foregoing observations, but also yields predictions confirmed by direct tests. 2. The model represents a neuron as a set of three parallel channels. The input to each channel is an array of pixel luminances. Each channel consists of an input nonlinearity cascaded into a linear spatial-to-temporal filter. The output of each channel is a basic waveform, a principal component. The response of the neuron is the sum of the outputs of the three channels. 3. The model accounted for much of the variance in the coefficients of the first three principal components of the neuronal responses to the set of Walsh stimuli. Using parameters derived from the responses of neurons to the Walsh stimuli only, the model also predicted the responses to "center-surround" annuli of different contrasts and mean luminances, as well as to superpositions of pairs of Walsh patterns. The model made statistically significant predictions of the coefficients of two of the principal components of these responses. 4. After the parameters of the model had been fit to reproduce the responses of neurons to the Walsh stimuli, we found that the input nonlinearity of the model was compressed at both the high and low luminance levels. This compression produced response saturation that closely resembled the response saturation of neurons reported in the first paper in this series. Although not absolutely smooth, the spatial filter for the first channel had a dominant excitatory or inhibitory center and an antagonistic surround. Thus this spatial filter accounted for both the center and the surround structures of previous models of LGN receptive fields. There was greater variety in the structures of the spatial filters for the second and third channels, but none had a center-surround organization. Many of the spatial filters for these higher channels contained oriented ridges or valleys. Other spatial filters were dominated by a bipolar pair of pixels. 5. The model of LGN neurons that we present in this paper represents an extension over previous models in four ways. First, the model is capable of explaining the responses of neurons to a wider range of luminances than previous models. Second, the model is capable of explaining the shapes of the response waveforms as well as their magnitudes. Third, the concept of a single receptive field is extended to a series of spatial-to-temporal filters. Fourth, the model suggests that LGN neurons provide a description of both the brightness and the form of a stimulus in their response waveforms.

Concurrent processing and complexity of temporally encoded neuronal messages in visual perception.

The intrinsic neuronal code that carries visual information and the perceptual mechanism for decoding that information are not known. However, multivariate statistics and information theory show that neurons in four visual areas simultaneously carry multiple, stimulus-related messages by utilizing multiplexed temporal codes. The complexity of these temporal messages increases progressively across the visual system, yet the temporal codes overlap in time. Thus, visual perception may depend on the concurrent processing of multiplexed temporal messages from all visual areas.

Interactive effects among several stimulus parameters on the responses of striate cortical complex cells.

1. Although neurons within the visual system are often described in terms of their responses to particular patterns such as bars and edges, they are actually sensitive to many different stimulus features, such as the luminances making up the patterns and the duration of presentation. Many different combinations of stimulus parameters can result in the same neuronal response, raising the problem of how the nervous system can extract information about visual stimuli from such inherently ambiguous responses. It has been shown that complex cells transmit significant amounts of information in the temporal modulation of their responses, raising the possibility that different stimulus parameters are encoded in different aspects of the response. To find out how much information is actually available about individual stimulus parameters, we examined the interactions among three stimulus parameters in the temporally modulated responses of striate cortical complex cells. 2. Sixteen black and white patterns were presented to two awake monkeys at each of four luminance-combinations and five durations, giving a total of 320 unique stimuli. Complex cells were recorded in layers 2 and 3 of striate cortex, with the stimuli centered on the receptive fields as determined by mapping with black and white bars. 3. An analysis of variance (ANOVA) was applied to these data with the three stimulus parameters of pattern, the luminance-combinations, and duration as the independent variables. The ANOVA was repeated with the magnitude and three different aspects of the temporal modulation of the response as the dependent variables. For the 19 neurons studied, many of the interactions between the different stimulus parameters were statistically significant. For some response measures the interactions accounted for more than one-half of the total response variance. 4. We also analyzed the stimulus-response relationships with the use of information theoretical techniques. We defined input codes on the basis of each stimulus parameter alone, as well as their combinations, and output codes on the basis of response strength, and on three measures of temporal modulation, also taken individually and together. Transmitted information was greatest when the response of a neuron was interpreted as a temporally modulated message about combinations of all three stimulus parameters. The interaction terms of the ANOVA suggest that the response of a complex cell can only be interpreted as a message about combinations of all three stimulus parameters.(ABSTRACT TRUNCATED AT 400 WORDS)

Unbiased measures of transmitted information and channel capacity from multivariate neuronal data.

Two measures from information theory, transmitted information and channel capacity, can quantify the ability of neurons to convey stimulus-dependent information. These measures are calculated using probability functions estimated from stimulus-response data. However, these estimates are biased by response quantization, noise, and small sample sizes. Improved estimators are developed in this paper that depend on both an estimate of the sample-size bias and the noise in the data.

Estimating left ventricular offset volume using dual-frequency conductance catheters.

The measurement of left ventricular volume by the conductance-catheter technique has many advantages, but it is difficult to determine absolute volumes with this method. Current procedure requires that a bolus of concentrated hypertonic saline be injected to measure absolute volume. It also demands that the subject be in a steady state and that measurements only be made at discrete intervals. The saline bolus may affect the cardiovascular state of the subject. This paper describes a new technique for estimating absolute volume utilizing the conductance catheter that relies on the different frequency responses of blood and muscle. Good correlation between the salt-injection method and the dual-frequency method was found in nine closed-chest pigs anesthetized with pentobarbital sodium (r = 0.922). Further refinements may extend the utility of the dual-frequency approach.