edictably with stimulus parameters, it is widely held to be the primary variable relating neuronal response to visual experience (Adrian, 1928; Lettvin et al., 1959; Werner and Mountcastle, 1963; Barlow, 1972; Henry et al., 1973). Accordingly, many studies hold a stimulus parameter constant during an experiment, measure large variations in firing frequency across different trials and high within-trial variation in inter-spike intervals, and conclude that the microstructure of spike trains is essentially random (Schiller et al., 1976; Heggelund and Albus, 1978; Tolhurst et al., 1981; Tolhurst et al., 1983; Vogels et al., 1989; Softky and Koch, 1993; Shadlen and Newsome, 1994). A few studies have emphasized that cells in mammalian visual cortex responding to moving patterns show stimulus-locked temporal modulation, sometimes referred to as "grain" response (Tomko and Crapper, 1974; Hammond and MacKay, 1977; Gulyas et al., 1987; Snowden et al., 1992). However, the time scale and stimulus

. In a number of systems including wind detection in the cricket, visual motion perception and coding of arm movement direction in the monkey and place cell response to position in the rat hippocampus, firing rates in a population of tuned neurons are correlated with a vector quantity. We examine and compare several methods that allow the coded vector to be reconstructed from measured firing rates. In cases where the neuronal tuning curves resemble cosines, linear reconstruction methods work as well as more complex statistical methods requiring more detailed information about the responses of the coding neurons. We present a new linear method, the optimal linear estimator (OLE), that on average provides the best possible linear reconstruction. This method is compared with the more familiar vector method and shown to produce more accurate reconstructions using far fewer recorded neurons. Introduction To determine how information is represented by nervous systems, we need to understand ...

During natural vision, the brain efficiently processes views of the external world as the eyes actively scan the environment. To better understand the neural mechanisms underlying this process, we recorded the activity of individual temporal cortical neurons while monkeys looked for and identified familiar targets embedded in natural scenes. We found a group of visual neurons that exhibited stimulus-selective neuronal bursts just before the monkey’s response. Most of these cells showed similar selectivity whether effective targets were viewed in isolation or encountered in the course of exploring complex scenes. In addition, by embedding target stimuli in natural scenes, we could examine the activity of these stimulus-selective cells during visual search and at the time targets were fixated and Convergent evidence from behavioral, neuropsychological, and neurophysiological experiments indicates that, in the primate

We describe a new, computationally simple method for analyzing the dynamics of neuronal spike trains driven by external stimuli. The goal of our method is to test the predictions of simple spike-generating models against extracellularly recorded neuronal responses. Through a new statistic called the power ratio, we distinguish between two broad classes of responses: (1) responses that can be completely characterized by a variable firing rate, (for example, modulated Poisson and gamma spike trains); and (2) responses for which firing rate variations alone are not sufficient to characterize response dynamics (for example, leaky integrate-and-fire spike trains as well as Poisson spike trains with long absolute refractory periods). We show that the responses of many visual neurons in the cat retinal ganglion, cat lateral geniculate nucleus, and macaque primary visual cortex fall into the second class, which

In the primate primary visual cortex (V1), the significance of individual action potentials has been difficult to determine, particularly in light of the considerable trial-to-trial variability of responses to visual stimuli. We show here that the information conveyed by an action potential depends on the duration of the immediately preceding interspike interval (ISI). The interspike intervals can be grouped into several different classes on the basis of reproducible features in the interspike interval histograms. Spikes in different classes bear different relationships to the visual stimulus, both qualitatively (in terms of the average stimulus preceding each spike) and quantitatively (in terms of the amount of information encoded per spike and per second). Spikes preceded by very short intervals (3 msec or less) convey information most efficiently and contribute disproportionately to the overall receptive-field properties of the neuron. Overall, V1

We show that spike timing adds to the information content of spike trains for transiently presented stimuli but not for comparable steady-state stimuli, even if the latter elicit transient responses. Contrast responses of 22 single neurons in macaque V1 to periodic presentation of steady-state stimuli (drifting sinusoidal gratings) and transient stimuli (drifting edges) of optimal spatiotemporal parameters were recorded extracellularly. The responses were analyzed for contrast-dependent clustering in spaces determined by metrics sensitive to the temporal structure of spike trains. Two types of metrics, costbased spike time metrics and metrics based on Fourier harmonics of the response, were used. With both families of metrics, temporal coding of contrast is lacking in responses to drifting sinusoidal gratings of most (simple and complex) V1 A prevailing view of neural coding is that the meaningful signal

ng approach, #(t) is the angular edge velocity of the object and the constant, and # is related to the angular threshold size [# # 1/tan(# thres /2)]. Because LGMD appears to receive distinct input projections, respectively motion- and size-sensitive, this result suggests that a multiplication operation is implemented by LGMD. Thus, LGMD might be an ideal model to investigate the biophysical implementation of a multiplication operation by single neurons. Key words: looming; multiplication; locust; LGMD; DCMD; lobula; collision-avoidance The processing of sensory information by neural circuits is known to depend critically on the implementation of nonlinear operations. Multiplication, for example, is thought to be the elementary building block underlying the detection of visual motion in insects (Reichardt, 1987; Borst and Egelhaaf, 1989) or the generation of gain fields in posterior

words: interval; short-term plasticity; paired-pulse facilitation; paired-pulse depression; timing; temporal processing Our perception of the world is based on the spatiotemporal patterns of neuronal activity produced at sensory layers. By decoding these patterns the brain determines what we see and hear. It is useful to distinguish between the spatial and temporal content of stimuli because f undamentally different mechanisms may underlie each form of processing. Spatial information refers to stimuli defined by the location of active sensory afferents. For instance, vertical and horizontal bars of light activate different retinal ganglion cells arranged in specific spatial patterns. Similarly, 1 and 4 kHz tones activate spatially distinct populations of cochlear hair cells. In both cases there is a place code at the earliest sensory stages. In its simplest form, the generation of neurons that respond selectively to spatial stimuli is a wiring problem: a neuron that responds t

We examine the distributed nature of the neural code for faces represented by the firing of visual neurons in the superior temporal sulcus of monkeys. Both information theory and neural decoding techniques are applied to determine how the capacity to represent faces depends on the number of coding neurons. Using a combination of experimental data and Monte Carlo simulations, we show that the information grows linearly and the capacity to encode stimuli grows exponentially with the number of neurons. By decoding firing rates, we determine that the responses of the 14 recorded neurons can distinguish between 20 face stimuli with approximately 80% accuracy. In general, we find that N neurons of this type can encode approximately 3(2 0:4N ) different faces with 50% discrimination accuracy. These results indicate that the neural code for faces is highly distributed and capable of accurately representing large numbers of stimuli. Introduction The amount of information that can be repres...

space, and object-space representations in the primate hippocampus. J