Biomedical subjects

L M Optican

Publications and source records attributed to L M Optican.

At least 19 recordsLinked to original sources

PubMed1997

Detection, classification, and superposition resolution of action potentials in multiunit single-channel recordings by an on-line real-time neural network.

Determination of single-unit spike trains from multiunit recordings obtained during extracellular recording has been the focus of many studies over the last two decades. In multiunit recordings, superpositions can occur with high frequency if the firing rates of the neurons are high or correlated, making superposition resolution imperative for accurate spike train determination. In this work, a connectionist neural network (NN) was applied to the spike sorting challenge. A novel training scheme was developed which enabled the NN to resolve some superpositions using single-channel recordings. Simulated multiunit spike trains were constructed from templates and noise segments that were extracted from real extracellular recordings. The simulations were used to determine the performances of the NN and a simple matched template filter (MTF), which was used as a basis for comparison. The network performed as well as the MTF in identifying nonoverlapping spikes, and was significantly better in resolving superpositions and rejecting noise. An on-line, real-time implementation of the NN discriminator, using a high-speed digital signal processor mounted inside an IBM-PC, is now in use in six laboratories.

Action Potentials↗

PubMed1996

Primate striate and prestriate cortical neurons during discrimination. I. simultaneous temporal encoding of information about color and pattern.

1. We recorded the responses of neurons in cortical areas V1, V2, and V4 to a set of 36 colored patterns while monkeys discriminated among the stimuli on the basis of their color or their pattern. In the discrimination task a colored square or a black and white pattern was presented foveally as a cue stimulus. The monkey was required to choose, by making a saccade, which of three peripheral targets had the same property as the cue. One of the peripheral targets was centered on the receptive field of the neuron, and the other two were positioned at equally distant points around the circumference of an imaginary circle centered on the cue and passing through the receptive field. 2. An examination of the responses to the stimuli showed that there was a complex interaction between the effects of color and of pattern on the neuronal responses. Because of these interactions, we tested sensitivity to color and pattern by sorting the responses to all stimuli according to the color or pattern of the stimulus. We found that the number of spikes in the responses was affected by only one or the other of the stimulus parameters, but that the temporal distribution of spikes was affected by both stimulus parameters. We quantified the relative sensitivities of each neuron to color and pattern by dividing the amount of information the neuron transmitted about color by the amount of information the neuron transmitted about pattern. The distributions of information ratios assuming a spike count code were broad, indicating that many neurons were sensitive to only one stimulus parameter or the other. In contrast, the distributions of information ratios assuming a wave-form code were narrow and centered near 1.0, indicating nearly equal sensitivities to both stimulus parameters. 3. In our initial experiments, it appeared that the color or pattern used as the cue for the discrimination task affected the responses of many neurons to stimuli on the receptive field. To determine whether the cue effect was due to simple visual interactions or to the cognitive requirements of the discrimination task, we performed a control experiment in which the cue was turned on 80 ms after the peripheral stimuli. For many of the neurons in the control experiment, an effect related to the cue appeared in the response before the cue had been turned on. Thus the effect we observed must have been due to visual interactions with the distractor targets, even though these were outside the neuron's classically defined receptive field. 4. We compared the rate at which color and pattern information developed in the response over time assuming either a spike count or a waveform code. The spike count code gained more of its information in the first 20ms of the response than did the waveform code, but thereafter the information carried by the spike count code developed more slowly and reached a lower asymptote than did the information carried by the waveform code. 5. The waveform codes carried nearly equal amounts of information about color and pattern, but the messages about these two parameters did not develop at the same rate in all areas. The messages about color and pattern developed at the same rate in area V1, but messages about color developed more slowly than did the messages about pattern in areas V2 and V4. 6. These results offer a neurophysiological basis for both the psychological separateness of color and pattern, and the binding of color and pattern into a unified percept. We propose that the separateness of color and form arises not by virtue of their being encoded by different populations of neurons, but by virtue of their being encoded by separable waveform codes in the responses of single neurons. We propose that the binding of color and form occurs by virtue of their codes being multiplexed on the same neurons.

Animals↗

PubMed1996

Primate striate and prestriate cortical neurons during discrimination. II. separable temporal codes for color and pattern.

1. In the previous paper we reported our analysis of the responses of neurons in cortical areas V1, V2, and V4 to a set of stimuli that consisted of all 36 combinations of six colors and six patterns. Neurons in all three cortical areas simultaneously encoded information about both the color and pattern of the stimulus in the number and temporal distribution of spikes in their responses. To account for this ability, we propose that a neuron's response consists of separable temporal codes representing the color and pattern of the stimulus that are multiplexed together. 2. We used nonlinear regression to fit the model parameters to the data. We used the responses to 30 of the 36 stimuli as a training set to estimate the parameters of the model and the responses to the remaining 6 stimuli as a test set. After training, the model fitted the responses to stimuli in the training sets very well and predicted the responses to stimuli in the test sets. Thus neuronal responses to colored patterns contain separate temporal codes representing color and pattern. 3. After establishing the model parameters, we obtained the waveforms that represented each neuron's temporal codes for the six colors and six patterns of our stimulus set. We then proceeded with a series of analyses to determine whether these waveforms were viable candidates for neuronal codes. Cluster analysis revealed that there were only a few different classes of waveforms representing each color and pattern, and there were many neurons in each class. Further, neurons that used similar waveforms to represent one color or pattern also tended to use similar waveforms to represent other colors or patterns. The waveforms representing five of the six colors and three of the six patterns were similar in the two monkeys used in this study. 4. We compared the shapes of the code waveforms across cortical areas and found no differences among areas in the shapes of the waveforms representing four of the six colors. In contrast, we found that there were differences among areas in the shapes of the waveforms representing all six patterns. These results suggest that messages about color are encoded at an early level and are then propagated upward, but that messages about pattern are altered in each successive cortical area. 5. Our results offer a neurophysiological explanation for the psychophysical evidence that color and form are processed by different channels. We propose that the psychophysical channels for color and pattern arise from the separability of the temporal codes for color and pattern in the responses of single neurons. This hypothesis implies that psychophysical channels correspond to classes of temporal codes rather than to classes of neurons.

Animals↗

PubMed1996

Involuntary attentional shifts due to orientation differences.

We tested the ability of orientation differences to cause involuntary shifts of visual attention and found that these attentional shifts can occur in response to an orientation "pop-out" display. Texture-like cue stimuli consisting of discrete oriented bars, with either uniform orientation or containing a noninformative orthogonally oriented bar, were presented for a variable duration. Subsequent to or partially coincident with the cue stimulus was the target display of a localization or two-interval forced-choice task, followed by a mask display. Naive subjects consistently showed greater accuracy in trials with the target at the location of the orthogonal orientation compared with trials with uniformly oriented bars, with only 100 msec between the cue and mask onsets. Discriminating these orientations required a stimulus onset asynchrony (SOA) of 50-70 msec. The attentional facilitation is transient, in most cases absent with a cue-mask SOA of 250 msec [corrected]. These results suggest that the preattentive character of some texture discrimination tasks with SOAs of only 100 msec is vitiated by the involuntary attentional shifts that are caused by orientation differences.

Adolescent↗

PubMed1995

A field theory of saccade generation: temporal-to-spatial transform in the superior colliculus.

Recent models have placed the superior colliculus inside the local feedback loop that generates the pulse of innervation needed to make a saccade. Such closed-loop models need to take into account the different coordinate systems of visual and motor signals. This paper presents a computational model showing how the superior colliculus can bring the visual and motor information together in a common reference frame.

Animals↗

PubMed1994

Superior colliculus cell types and models of saccade generation.

Recent experiments on the cat and monkey have revealed several different cell types within the superior colliculus, including fixation, burst, and build up cells. During primate saccades, activity remains fixed at one location in burst cells, but spreads across the colliculus in build up cells. New models based on the activity of these cell types suggest their functional roles in saccade generation.

Animals↗

PubMed1993

Cluster method for analysis of transmitted information in multivariate neuronal data.

A new method for quantifying the transmitted information and channel capacity of high-dimensional data, based on cluster formation, is described. The method's ability to handle high-dimensional data allows for a complete measurement of information transmitted by neuronal data. It is computationally efficient in terms of both processing time and memory storage. Application of the method to the responses of a VI neuron shows that more information was transmitted about the pattern of stimuli than about their color.

Animals↗

PubMed1993

Visually induced cross-axis postsaccadic eye drift.

1. It has been previously shown that, if a visual pattern is transiently moved just after every saccade, it is possible to induce horizontal, postsaccadic, ocular drift after horizontal saccades that persists in the dark. In this study we show that horizontal ocular drift can also be created after vertical saccades. Five human subjects viewed binocularly the interior of a full-field hemisphere filled with a random-dot pattern. They were encouraged to make frequent vertical saccades. During training, eye movements were recorded by the electrooculogram. A computer detected the end of every saccade and immediately moved the pattern to the left after up saccades and right after down saccades. The motion was exponential, its amplitude was 25% of the vertical component of the antecedent saccade, its time constant was 50 ms. Before and after 2-3 h of training, movements of both eyes were measured by the eye-coil/magnetic-field method while subjects were instructed to make vertical saccades in the dark, in the presence of the movable adapting pattern, and between stationary targets for calibration. 2. After training (approximately 20,000 saccades) all subjects developed a zero-latency, exponential ocular drift to the left after up saccades and to the right after down saccades. The amplitude of the horizontal drift, expressed as a percentage of the vertical component of the preceding saccade, was 2.7% in the dark. This rose to 10.2% in the presence of the movable adapting stimulus. The latter rise is not due to visual following systems but to a zero-latency increase in initial drift velocity. 3. The horizontal drifts were usually unequal between the two eyes, indicating the presence of disconjugate movements. We measured intrasaccadic disconjugate horizontal movements of all subjects. In agreement with studies by others of saccades in the light, we measured a divergence during up saccades (1.3 degrees) and a convergence for down (0.4 degrees), but in this case for spontaneous saccades in the dark. After training, these values increased for saccades in the dark but decreased in the light in the presence of the adapting stimulus. These changes were largely idiosyncratic and statistically significant in only a few subjects. 4. The cross-axis postsaccadic drifts were separated into their conjugate and disconjugate components. The disconjugate components were small and idiosyncratic, and the means were small for saccades in the dark. The only consistent trend was in the presence of the adapting stimulus where up saccades were often followed by convergence.(ABSTRACT TRUNCATED AT 400 WORDS)

Adaptation, Physiological↗

PubMed1992

Role of inferior temporal neurons in visual memory. I. Temporal encoding of information about visual images, recalled images, and behavioral context.

1. Lesions of the inferior temporal (IT) cortex selectively hamper monkeys in tasks requiring visual memory. A system that recognizes images must be able to encode a current stimulus, recall the code of a previous stimulus, compare the codes of the two stimuli, and make a decision on the basis of the outcome of the comparison. Therefore, IT neurons must be involved in at least one of these processes. To determine the specific role of IT neurons in visual memory, we measured the information conveyed in the neuronal responses about current patterns, recalled patterns, and behavioral context. 2. Two monkeys were trained to perform a sequential matching task using a set of 32 black and white Walsh patterns. In the course of an experiment, each pattern was presented repeatedly in sample, match, and nonmatch behavioral contexts. While the monkeys were performing the task, we recorded the activity of 76 neurons from area TE of IT. The neuronal responses to the stimuli were converted to spike density functions, and the resultant waveforms were quantified using their principal components. The relationships between the responses and the stimuli were studied using analysis of variance and information theory. 3. The analysis of variance was applied to the neuronal response waveforms using the context (sample, match, or nonmatch) and the patterns of the stimuli as independent variables and the spike count or the coefficients of the principal components as the dependent variables. We found that the waveforms of most neurons were significantly modulated by both the pattern and the context of the stimulus presentation. 4. We also analyzed the stimulus-response relationships using information theory. The input codes were based on the pattern and context of the stimuli, and the output codes were based on the spike count or the principal components of the responses. The neuronal response waveforms were found to convey significant amounts of information about both the pattern and context of the stimuli. Transmitted information was greatest when the response of a neuron was interpreted as a message about the combination of pattern and context. Nevertheless, there was information about context independent of pattern and vice versa. 5. We also used information theory to determine whether the neuronal responses to the second, or test, stimulus conveyed information about the pattern of the first, or sample, stimulus. The input codes were based on the patterns of the sample stimuli, and the output codes were based on the responses to the nonmatch test stimuli.(ABSTRACT TRUNCATED AT 400 WORDS)

Analysis of Variance↗

PubMed1992

Role of inferior temporal neurons in visual memory. II. Multiplying temporal waveforms related to vision and memory.

1. In the companion paper we reported on the activity of neurons in the inferior temporal (IT) cortex during a sequential pattern matching task. In this task a sample stimulus was followed by a test stimulus that was either a match or a nonmatch. Many of the neurons encoded information about the patterns of both current and previous stimuli in the temporal modulation of their responses. 2. A simple information processing model of visual memory can be formed with just four steps: 1) encode the current stimulus; 2) recall the code of a remembered stimulus; 3) compare the two codes; 4) and decide whether they are similar or different. The analysis presented in the first paper suggested that some IT neurons were performing the comparison step of visual memory. 3. We propose that IT neurons participate in the comparison of temporal waveforms related to vision and memory by multiplying them together. This product could form the basis of a crosscorrelation-based comparison. 4. We tested our hypothesis by fitting a simple multiplicative model to data from IT neurons. The model generated waveforms in separate memory and visual channels. The waveforms arising from the two channels were then multiplied on a point by point basis to yield the output waveform. The model was fitted to the actual neuronal data by a gradient descent method to find the best fit waveforms that also had the lowest total energy. 5. The multiplicative model fit the neuronal responses quite well. The multiplicative model made consistently better predictions of the actual response waveforms than did an additive model. Furthermore, the fit was better when the actual relationship between the responses and the sample and test stimuli were preserved than when that relationship was randomized. 6. We infer from the superior fit of the multiplicative model that IT neurons are multiplying temporally modulated waveforms arising from separate visual and memory systems in the comparison step of visual memory.

Animals↗

PubMed1992

Saccade-vergence interactions in humans.

1. We recorded eye movements in four normal human subjects during refixations between targets calling for various combinations of saccades and vergence. We confirmed and extended prior observations of 1) transient changes in horizontal ocular alignment during both pure horizontal saccades (relative divergence followed by relative convergence) and pure vertical saccades (usually divergence for upward and convergence for downward saccades); 2) occasional, high-frequency (20-25 Hz), conjugate oscillations along the axis orthogonal to the main saccade; and 3) the speeding up of horizontal vergence by both horizontal and vertical saccades. 2. To interpret these findings, we developed a hypothesis for the generation of vergence to step changes in target depth, both with and without associated saccades. The essential features of this hypothesis are 1) the transient changes in horizontal ocular alignment during pure horizontal saccades reflect asymmetries in the mechanical properties of the lateral and medial rectus muscles causing adduction to lag abduction; 2) pure vergence movements in response to step changes in target depth are generated by a neural network that uses a desired change in vergence position as its input command and instantaneous vergence motor error (the difference between the desired change and the actual change in vergence) to drive vergence premoter neurons; and 3) the facilitation of horizontal vergence by saccades arises from nonlinear interactions in central premotor circuits. 3. The hypothetical network for generating pure vergence to step changes in target depth is analogous in structure to the local feedback model for the generation of saccades and has the same conceptual appeal. With the assumption of a single nonlinearity describing the relationship between a vergence motor error signal and the output of the neurons that generate promoter vergence velocity commands, this model generates pure vergence movements with peak velocity-amplitude relationships and trajectories that closely match those of experimental data. 4. Several types of models are proposed for the central, nonlinear interaction that occurs when saccades and vergence are combined. Common to all models is the idea that omnidirectional pause neurons (OPN), which are thought to gate activity for saccade burst neurons, also gate activity for saccade-related vergence. In one model we hypothesize the existence of a separate class of saccade-related vergence burst neurons, which generate premotor horizontal vergence commands but only during saccades. In a second model we hypothesize separate right eye and left eye saccadic burst neurons that receive not only conjugate, but also equal but oppositely directed vergence error signals.(ABSTRACT TRUNCATED AT 400 WORDS)

Adult↗

PubMed1991

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.

Animals↗

PubMed1991

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.

Animals↗

PubMed1991

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)

Analysis of Variance↗

PubMed1991

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.

Animals↗

PubMed1991

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)

Animals↗

PubMed1991

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.

Animals↗

PubMed1991

Superior colliculus neurons mediate the dynamic characteristics of saccades.

1. The locus of activity within the superior colliculus (SC) is related to the desired displacement of the eye. Current hypotheses suggest that the location of this locus of activity determines the amplitude of the saccade and that the level of activity at this locus determines eye velocity. We present evidence that suggests that, although the locus determines the amplitude of the saccade, the level of activity in the colliculus encodes dynamic motor error (the difference between desired and current eye displacement). 2. We categorized 86 neurons in the intermediate and deep layers of the superior colliculus of two rhesus monkeys by their activity in relation to the end of saccadic eye movements. In 36% of the cells (n = 31), activity was completely cut off by the end of the saccade (clipped cells). For 53% of cells (n = 46), the major burst of activity ceased by the end of the saccade, but activity continued for 30-100 ms after the end of the movement (partially clipped cells). The remaining 10% of the cells (n = 9) had no clear burst of activity (unclipped cells) but rather had activity that increased gradually before the saccade and then slowly decreased for up to 100 ms after the saccade. These categories were part of a continuum of cell types rather than discrete classes of cells. 3. We first determined whether this new categorization of cells revealed a special relation between the discharge of clipped and partially clipped cells and saccadic amplitude and peak velocity. As expected, we found a steady increase in spike count as saccadic amplitude increased up to the center of the movement field, and an increase in peak spike discharge as peak velocity increased up to a maximum radial eye velocity. Variability in the cell discharge was substantially greater than the variability of saccadic amplitude or peak velocity. We concluded that these single point or averaged measures did not reveal any new functional relationship of these cells. 4. We then examined the relationship of the temporal pattern of discharge of clipped and partially clipped cells to instantaneous changes in radial error and radial velocity. There was a monotonic decay in spike discharge with declining radial error. In contrast, there was a complex, multivalued relationship between spike discharge and radial velocity; collicular cells produced two different values of spike discharge for the same velocity, one during acceleration and the other during deceleration of the eye during a saccade.(ABSTRACT TRUNCATED AT 400 WORDS)

Animals↗