A generalized linear model for estimating spectrotemporal receptive fields from responses to natural sounds

In the auditory system, the stimulus-response properties of single neurons are often described in terms of the spectrotemporal receptive field (STRF), a linear kernel relating the spectrogram of the sound stimulus to the instantaneous firing rate of the neuron. Several algorithms have been used to estimate STRFs from responses to natural stimuli; these algorithms differ in their functional models, cost functions, and regularization methods. Here, we characterize the stimulus-response function of auditory neurons using a generalized linear model (GLM). In this model, each cell's input is described by: 1) a stimulus filter (STRF); and 2) a post-spike filter, which captures dependencies on the neuron's spiking history. The output of the model is given by a series of spike trains rather than instantaneous firing rate, allowing the prediction of spike train responses to novel stimuli. We fit the model by maximum penalized likelihood to the spiking activity of zebra finch auditory midbrain neurons in response to conspecific vocalizations (songs) and modulation limited (ml) noise. We compare this model to normalized reverse correlation (NRC), the traditional method for STRF estimation, in terms of predictive power and the basic tuning properties of the estimated STRFs. We find that a GLM with a sparse prior predicts novel responses to both stimulus classes significantly better than NRC. Importantly, we find that STRFs from the two models derived from the same responses can differ substantially and that GLM STRFs are more consistent between stimulus classes than NRC STRFs. These results suggest that a GLM with a sparse prior provides a more accurate characterization of spectrotemporal tuning than does the NRC method when responses to complex sounds are studied in these neurons.     

Spatial structure of complex cell receptive fields measured with natural images

Neuronal receptive fields (RFs) play crucial roles in visual processing. While the linear RFs of early neurons have been well studied, RFs of cortical complex cells are nonlinear and therefore difficult to characterize, especially in the context of natural stimuli. In this study, we used a nonlinear technique to compute the RFs of complex cells from their responses to natural images. We found that each RF is well described by a small number of subunits, which are oriented, localized, and bandpass. These subunits contribute to neuronal responses in a contrast-dependent, polarity-invariant manner, and they can largely predict the orientation and spatial frequency tuning of the cell. Although the RF structures measured with natural images were similar to those measured with random stimuli, natural images were more effective for driving complex cells, thus facilitating rapid identification of the subunits. The subunit RF model provides a useful basis for understanding cortical processing of natural stimuli.     

Spatiotemporal receptive fields: a dynamical model derived from cortical architectonics

We assume that the mammalian neocortex is built up out of some six layers which differ in their morphology and their external connections. Intrinsic connectivity is largely excitatory, leading to a considerable amount of positive feedback. The majority of cortical neurons can be divided into two main classes: the pyramidal cells, which are said to be excitatory, and local cells (most notably the non-spiny stellate cells), which are said to be inhibitory. The form of the dendritic and axonal arborizations of both groups is discussed in detail. This results in a simplified model of the cortex as a stack of six layers with mutual connections determined by the principles of fibre anatomy. This stack can be treated as a multi-input-multi-output system by means of the linear systems theory of homogeneous layers. The detailed equations for the simulation are derived in the Appendix. The results of the simulations show that the temporal and spatial behaviour of an excitation distribution cannot be treated separately. Further, they indicate specific processing in the different layers and some independence from details of wiring. Finally, the simulation results are applied to the theory of visual receptive fields. This yields some insight into the mechanisms possibly underlying hypercomplexity, putative nonlinearities, lateral inhibition, oscillating cell responses, and velocity-dependent tuning curves.     

A model for the temporal organization of X- and Y-type receptive fields in the primate retina

A model is proposed for the temporal characteristics of X-and Y-type responses of ganglion cells in the primate retina. The main suggestions of the model are: (I) The X-type temporal response is determined primarily by the delay between center and surround contributions. (II) The Y-type response is generated in the inner plexiform layer by a derivativelike operation on the bipolar cell's input, followed by a rectification in the convergence of these inputs onto the Y-ganglion-cell. (III) The derivative-like operation is obtained by recurrent inhibition in the dyad synaptic structure.The X-and Y-type responses predicted by the model, for a variety of stimuli, were examined and compared with available electrophysiological recordings. Finally, certain predictions derived from the model are discussed.     

A computational model of rapid task-related plasticity of auditory cortical receptive fields

Receptive field properties of neurons in A1 can rapidly adapt their shapes during task performance in accord with specific task demands and salient sensory cues (Fritz et al., Hearing Research, 206:159–176, 2005a, Nature Neuroscience, 6: 1216–1223, 2003). Such modulatory changes selectively enhance overall cortical responsiveness to target (foreground) sounds and thus increase the likelihood of detection against the background of reference sounds. In this study, we develop a mathematical model to describe how enhancing discrimination between two arbitrary classes of sounds can lead to the observed receptive field changes in a variety of spectral and temporal discrimination tasks. Cortical receptive fields are modeled as filters that change their spectro-temporal tuning properties so as to respond best to the discriminatory acoustic features between foreground and background stimuli. We also illustrate how biologically plausible constraints on the spectro-temporal tuning of the receptive fields can be used to optimize the plasticity. Results of the model simulations are compared to published data from a variety of experimental paradigms.     

Striate receptive fields mapped with single and bipartite stimuli

In 22 acute experiments with anesthetized and immobilized adult cats, 364 maps of receptive fields (RF) of 47 striate neurons were obtained by means of single local stimuli flashed at different parts of the visual field, or with additional asynchronous activation of the RF excitatory center with oscillating bar of the optimal orientation. Under bipartite stimulation, considerable and significant decrease in the square and weight of the central excitatory RF zone was revealed in more then 75% of the studied cells. Additional excitatory zones appeared in 54% of cases, or the square and weight of the excitatory zones substantially increased, and inhibitory zones developed in 90% of cases. These effects were correlated with the degree of increase in the background firing during transition from the mode of mapping with single stimulation to that with bipartite stimulation. The mechanism and possible functional role of cooperative excitatory and inhibitory intracortical interactions in organization of receptive fields and detection of features of a visual image are discussed.     

Rabbit visual cortical neurons with simple and complex receptive fields

Receptive fields of neurons of the rabbit visual cortex selective for stimulus orientation were investigated. These receptive fields were less well differentiated than those of the analogous neurons of the cat visual cortex (large in size and circular in shape). Two mechanisms of selectivity for stimulus orientation were observed: inhibition between on and off zones of the receptive field (sample type) and oriented lateral inhibition within the same zone of the receptive field (complex type). Lateral inhibition within the same zone of the receptive field also took place in unselective neurons; "complex" selective neurons differed from them in the orientation of this inhibition. A combination of both mechanisms was possible in the receptive field of the same neuron. It is suggested that both simple and complex receptive fields are derivatives of unselective receptive fields and that "complex" neurons are not the basis for a higher level of analysis of visual information than in "simple" neurons.A. N. Severtsov Institute of Evolutionary Morphology and Ecology of Animals, Academy of Sciences of the USSR, Moscow. Translated from Neirofiziologiya, Vol. 10, No. 1, pp. 13–21, January–February, 1978.     

Types of receptive fields in the lateral geniculate body and their functional model

In the Type I receptive fields (RFs) changes of the luminance leads to a shift of the curve relating the response and the stimulus area along the abscissa, in the Type II RFs the maximum of a response does not shift with changes of the luminance (Types I and II on classification by Glezer et al., 1971, 1972). The transient responses were observed in the Type I RFs and sustained responses in the Type II RFs. In the Type I RFs variation of the stimulus area and intensity brings about the change in the temporal and spatial frequency characteristics. This is produced by functional reorganization of the RF. In the Type II RFs there is no functional reorganization. The data obtained indicate that the Type I RFs are non-linear. By contrast, the Type II RFs are linear systems. The analysis of the model has shown that the distinctions in the dynamic characteristics of the responses of RFs belonging to different types is mainly due to different time constants for excitation and inhibition as well as inhibition coefficients. Distinctions in the mode of dependence of the RF response on stimulus parameters have been found to result from different relationship between delay time and stimulus parameters as well as different forms of the spatial weighting functions. It is shown that the Type I RFs transmit higher frequency components of the image spectrum, i.e. they emphasise the temporal and spatial contrasts. The Type II RFs transmit low frequency components of the spectrum including information about the intensity of an input stimulus.     

Types of receptive fields of the lateral geniculate body and their functional model

Functional differences between the type I and II receptive fields of the lateral geniculate body were studied in the cat. Some properties of these fields were shown to coincide with properties of "phasic" (Y type) and "tonic" (X type) of receptive fields. The type I fields have a limited range for transmission of information about the intensity of illumination; the type II fields, on the other hand, have a normal dynamic range of 2 log units. Using the number of spikes in groups as a measure of nervous activity, a neurophysiological scale of brightness corresponding to the psychological scale can be constructed on the basis of responses of the type II receptive field. It is postulated that type I receptive fields serve to transmit information on the shape of the image (spatial and temporal contrasts) and the type II fields transmit information on intensity of illumination. Investigation of the dynamic functional model showed that the type of receptive field is determined by the depth of inhibition through the interneuron. The depth of inhibition is much greater for type I than for type II.     

Responses of receptive fields of frog retinal ganglion cells to the leading and trailing edges of moving stimuli

The position of on- and off-discharge centers in class 1 and 3 receptive fields of the frog retina was determined with the aid of moving bars of different lengths. On- and off-centers of receptive fields of the first group coincide, those of the second are spatially separate, and in fields of the 3rd group the discharge center of one contrast sign occupies the central position and discharge centers of the opposite sign are located at the periphery, to its right and left. Receptive fields of the frog retina thus have features which approximate them to the concentric receptive fields of geniculate neurons and the fields of the cat visual cortex. Asymmetry in the responses was found: during movement in opposite directions the distance between the discharge centers changed, during movement to one side only one of the peripheral centers was revealed, whereas during movement to the other side the second center was revealed on the opposite side of the receptive field. This asymmetry of spatiotemporal relations in the receptive fields is similar to that found in the fields of cortical neurons and is connected with their directional properties.Research Institute of Applied Mathematics and Cybernetics, N. I. Lobachevskii State University, Gor'kii. Translated from Neirofiziologiya, Vol. 12, No. 1, pp. 75–85, January–February, 1980.     

A model of lateral interactions as the origin of multiwhisker receptive fields in rat barrel cortex

Journal of Computational Neuroscience - While cells within barrel cortex respond primarily to deflections of their principal whisker (PW), they also exhibit responses to non-principal, or adjacent,...     

A sparse coding model with synaptically local plasticity and spiking neurons can account for the diverse shapes of V1 simple cell receptive fields

Sparse coding algorithms trained on natural images can accurately predict the features that excite visual cortical neurons, but it is not known whether such codes can be learned using biologically realistic plasticity rules. We have developed a biophysically motivated spiking network, relying solely on synaptically local information, that can predict the full diversity of V1 simple cell receptive field shapes when trained on natural images. This represents the first demonstration that sparse coding principles, operating within the constraints imposed by cortical architecture, can successfully reproduce these receptive fields. We further prove, mathematically, that sparseness and decorrelation are the key ingredients that allow for synaptically local plasticity rules to optimize a cooperative, linear generative image model formed by the neural representation. Finally, we discuss several interesting emergent properties of our network, with the intent of bridging the gap between theoretical and experimental studies of visual cortex.     

A baboon model for endometriosis: implications for fertility

Endometriosis is one of the most common causes of chronic pelvic pain and infertility in women in the reproductive age group. Although the existence of this disease has been known for over 100 years our current knowledge of its pathogenesis and the pathophysiology of its related infertility remains unclear. Several reasons contribute to our lack of knowledge, the most critical being the difficulty in carrying out objective long-term studies in women. Thus, we and others have developed a model of this disease in the non-human primate, the baboon (Papio anubis). Intraperitoneal inoculation of autologous menstrual endometrium results in the development of endometriotic lesions with gross morphological characteristics similar to those seen in the human. Multiple factors have been implicated in endometriosis-associated infertility. We have described aberrant levels of factors involved in multiple pathways important in the establishment of pregnancy, in the endometrium of baboons induced with endometriosis. Specifically, we have observed dysregulation of proteins involved in invasion, angiogenesis, methylation, cell growth, immunomodulation, and steroid hormone action. These data suggest that, in an induced model of endometriosis in the baboon, an increased angiogenic capacity, decreased apoptotic potential, progesterone resistance, estrogen hyper-responsiveness, and an inability to respond appropriately to embryonic signals contribute to the reduced fecundity associated with this disease.     

A symmetric two locus model with viability and fertility selection

A two locus deterministic population genetic model is analysed. One locus is under viability selection, the other under fertility selection with both forms of selection completely symmetric. It is shown that linkage equilibrium may occur at two different equilibrium points. For a two-locus polymorphism to be stable, it is necessary that the viability locus be overdominant but not necessary that the fertility locus, considered separately, be able to support a stable polymorphism. The overlaps in stability are not as complex as under two locus symmetric fertilities, but considerably more complex than with symmetric viabilities. Extensions of the analysis for the central linkage equilibrium point with multiple viability and fertility loci are indicated.Research supported in part by NIH grants GM 28106 and GM 10452     

A Bayesian method for classification of images from electron micrographs

Particle classification is an important component of multivariate statistical analysis methods that has been used extensively to extract information from electron micrographs of single particles. Here we describe a new Bayesian Gibbs sampling algorithm for the classification of such images. This algorithm, which is applied after dimension reduction by correspondence analysis or by principal components analysis, dynamically learns the parameters of the multivariate Gaussian distributions that characterize each class. These distributions describe tilted ellipsoidal clusters that adaptively adjust shape to capture differences in the variances of factors and the correlations of factors within classes. A novel Bayesian procedure to objectively select factors for inclusion in the classification models is a component of this procedure. A comparison of this algorithm with hierarchical ascendant classification of simulated data sets shows improved classification over a broad range of signal-to-noise ratios.     

Application of conditional moment tests to model checking for generalized linear models

Generalized linear models (GLMs) are increasingly being used in daily data analysis. However, model checking for GLMs with correlated discrete response data remains difficult. In this paper, through a case study on marginal logistic regression using a real data set, we illustrate the flexibility and effectiveness of using conditional moment tests (CMTs), along with other graphical methods, to do model checking for generalized estimation equation (GEE) analyses. Although CMTs provide an array of powerful diagnostic tests for model checking, they were originally proposed in the econometrics literature and, to our knowledge, have never been applied to GEE analyses. CMTs cover many existing tests, including the (generalized) score test for an omitted covariate, as special cases. In summary, we believe that CMTs provide a class of useful model checking tools.     

A linear spatio-temporal model of the light-to-bipolar cell system and its response characteristics to moving bars

A linear spatio-temporal model for the visual pathway from receptor to bipolar cell is presented. This model is based on histological and electrophysiological data obtained from previously published work. The model incorporates the salient linear spatio-temporal dynamic characteristics of the light-to-bipolar cell system, and aims at furthering our understanding of the integrated spatio-temporal response characteristics of this system. This model is used to examine the way spatial and temporal dynamic characteristics combine to form the response of a single bipolar and of a combination of two bipolars to a moving bar stimulus. A unimodal speed sensitivity curve is identified than can be used to postulate speed detection schemes based on simple thresholding operations. The dependence of this speed sensitivity curve on specific temporal and spatial parameters of the receptive field is studied, giving rise to some simple speed discrimination schemata.Dr. Curlander is currently with the Jet Propulsion Laboratory of the California Institute of Technology, Pasadena, CA91109, USA     

A cooperation and competition based simple cell receptive field model and study of feed-forward linear and nonlinear contributions to orientation selectivity

We present a model for development of orientation selectivity in layer IV simple cells. Receptive field (RF) development in the model, is determined by diffusive cooperation and resource limited competition guided axonal growth and retraction in geniculocortical pathway. The simulated cortical RFs resemble experimental RFs. The receptive field model is incorporated in a three-layer visual pathway model consisting of retina, LGN and cortex. We have studied the effect of activity dependent synaptic scaling on orientation tuning of cortical cells. The mean value of hwhh (half width at half the height of maximum response) in simulated cortical cells is 58° when we consider only the linear excitatory contribution from LGN. We observe a mean improvement of 22.8° in tuning response due to the non-linear spiking mechanisms that include effects of threshold voltage and synaptic scaling factor.