A graphical anatomical database of neural connectivity.

We describe a graphical anatomical database program, called XANAT (so named because it was developed under the X window system in UNIX), that allows the results of numerous studies on neuroanatomical connections to be stored, compared and analysed in a standardized format. Data are entered into the database by drawing injection and label sites from a particular tracer study directly onto canonical representations of the neuroanatomical structures of interest, along with providing descriptive text information. Searches may then be performed on the data by querying the database graphically, for example by specifying a region of interest within the brain for which connectivity information is desired, or via text information, such as keywords describing a particular brain region, or an author name or reference. Analyses may also be performed by accumulating data across multiple studies and displaying a colour-coded map that graphically represents the total evidence for connectivity between regions. Thus, data may be studied and compared free of areal boundaries (which often vary from one laboratory to the next), and instead with respect to standard landmarks, such as the position relative to well-known neuro-anatomical substrates or stereotaxic coordinates. If desired, areal boundaries may also be defined by the user to facilitate the interpretation of results. We demonstrate the application of the database to the analysis of pulvinar-cortical connections in the macaque monkey, for which the results of over 120 neuro-anatomical experiments were entered into the database. We show how these techniques can be used to elucidate connectivity trends and patterns that may otherwise go unnoticed.

Natural image statistics and neural representation.

It has long been assumed that sensory neurons are adapted, through both evolutionary and developmental processes, to the statistical properties of the signals to which they are exposed. Attneave (1954)Barlow (1961) proposed that information theory could provide a link between environmental statistics and neural responses through the concept of coding efficiency. Recent developments in statistical modeling, along with powerful computational tools, have enabled researchers to study more sophisticated statistical models for visual images, to validate these models empirically against large sets of data, and to begin experimentally testing the efficient coding hypothesis for both individual neurons and populations of neurons.

Differential effects of apamin- and charybdotoxin-sensitive K+ conductances on spontaneous discharge patterns of developing retinal ganglion cells.

The spontaneous discharge patterns of developing retinal ganglion cells are thought to play a crucial role in the refinement of early retinofugal projections. To investigate the contributions of intrinsic membrane properties to the spontaneous activity of developing ganglion cells, we assessed the effects of blocking large and small calcium-activated potassium conductances on the temporal pattern of such discharges by means of patch-clamp recordings from the intact retina of developing ferrets. Application of apamin and charybdotoxin (CTX), which selectively block the small and large calcium-activated potassium channels, respectively, resulted in significant changes in spontaneous firings. In cells recorded from the oldest animals [postnatal day 30 (P30)-P45], which manifested relatively sustained discharge patterns, application of either blocker induced bursting activity. With CTX the bursts were highly periodic, short in duration, and of high frequency. In contrast, with apamin the interburst intervals were longer, less regular, and lower in overall spike frequency. These differences between the effects of the two blockers on spontaneous activity were documented by spectral analysis of discharge patterns. Filling cells from which recordings were made with Lucifer yellow revealed that these effects were obtained in all three morphological classes of cells: alpha, beta, and gamma. These findings provide the first evidence that apamin- and CTX-sensitive K+ conductances can have differential effects on the spontaneous discharge patterns of retinal ganglion cells. Remarkably, the bursts of activity obtained after apamin application in more mature neurons appeared very similar to the spontaneous bursting patterns observed in developing neurons. These findings suggest that the maturation of calcium-activated potassium channels, particularly the apamin-sensitive conductance, may contribute to the changes in spontaneous firings exhibited by retinal ganglion cells during the course of normal development.

Sparse coding with an overcomplete basis set: a strategy employed by V1?

The spatial receptive fields of simple cells in mammalian striate cortex have been reasonably well described physiologically and can be characterized as being localized, oriented, and bandpass, comparable with the basis functions of wavelet transforms. Previously, we have shown that these receptive field properties may be accounted for in terms of a strategy for producing a sparse distribution of output activity in response to natural images. Here, in addition to describing this work in a more expansive fashion, we examine the neurobiological implications of sparse coding. Of particular interest is the case when the code is overcomplete--i.e., when the number of code elements is greater than the effective dimensionality of the input space. Because the basis functions are non-orthogonal and not linearly independent of each other, sparsifying the code will recruit only those basis functions necessary for representing a given input, and so the input-output function will deviate from being purely linear. These deviations from linearity provide a potential explanation for the weak forms of non-linearity observed in the response properties of cortical simple cells, and they further make predictions about the expected interactions among units in response to naturalistic stimuli.

Emergence of simple-cell receptive field properties by learning a sparse code for natural images.

The receptive fields of simple cells in mammalian primary visual cortex can be characterized as being spatially localized, oriented and bandpass (selective to structure at different spatial scales), comparable to the basis functions of wavelet transforms. One approach to understanding such response properties of visual neurons has been to consider their relationship to the statistical structure of natural images in terms of efficient coding. Along these lines, a number of studies have attempted to train unsupervised learning algorithms on natural images in the hope of developing receptive fields with similar properties, but none has succeeded in producing a full set that spans the image space and contains all three of the above properties. Here we investigate the proposal that a coding strategy that maximizes sparseness is sufficient to account for these properties. We show that a learning algorithm that attempts to find sparse linear codes for natural scenes will develop a complete family of localized, oriented, bandpass receptive fields, similar to those found in the primary visual cortex. The resulting sparse image code provides a more efficient representation for later stages of processing because it possesses a higher degree of statistical independence among its outputs.

Natural image statistics and efficient coding.

Natural images contain characteristic statistical regularities that set them apart from purely random images. Understanding what these regularities are can enable natural images to be coded more efficiently. In this paper, we describe some of the forms of structure that are contained in natural images, and we show how these are related to the response properties of neurons at early stages of the visual system. Many of the important forms of structure require higher-order (i.e. more than linear, pairwise) statistics to characterize, which makes models based on linear Hebbian learning, or principal components analysis, inappropriate for finding efficient codes for natural images. We suggest that a good objective for an efficient coding of natural scenes is to maximize the sparseness of the representation, and we show that a network that learns sparse codes of natural scenes succeeds in developing localized, oriented, bandpass receptive fields similar to those in the mammalian striate cortex.

A nonlinear Hebbian network that learns to detect disparity in random-dot stereograms.

An intrinsic limitation of linear, Hebbian networks is that they are capable of learning only from the linear pairwise correlations within an input stream. To explore what higher forms of structure could be learned with a nonlinear Hebbian network, we constructed a model network containing a simple form of nonlinearity and we applied it to the problem of learning to detect the disparities present in random-dot stereograms. The network consists of three layers, with nonlinear sigmoidal activation functions in the second-layer units. The nonlinearities allow the second layer to transform the pixel-based representation in the input layer into a new representation based on coupled pairs of left-right inputs. The third layer of the network then clusters patterns occurring on the second-layer outputs according to their disparity via a standard competitive learning rule. Analysis of the network dynamics shows that the second-layer units' nonlinearities interact with the Hebbian learning rule to expand the region over which pairs of left-right inputs are stable. The learning rule is neurobiologically inspired and plausible, and the model may shed light on how the nervous system learns to use coincidence detection in general.

A multiscale dynamic routing circuit for forming size- and position-invariant object representations.

We describe a neural model for forming size- and position-invariant representations of visual objects. The model is based on a previously proposed dynamic routing circuit that remaps selected portions of an input array into an object-centered reference frame. Here, we show how a multiscale representation may be incorporated at the input stage of the model, and we describe the control architecture and dynamics for a hierarchical, multistage routing circuit. Specific neurobiological substrates and mechanisms for the model are proposed, and a number of testable predictions are described.

A neurobiological model of visual attention and invariant pattern recognition based on dynamic routing of information.

We present a biologically plausible model of an attentional mechanism for forming position- and scale-invariant representations of objects in the visual world. The model relies on a set of control neurons to dynamically modify the synaptic strengths of intracortical connections so that information from a windowed region of primary visual cortex (V1) is selectively routed to higher cortical areas. Local spatial relationships (i.e., topography) within the attentional window are preserved as information is routed through the cortex. This enables attended objects to be represented in higher cortical areas within an object-centered reference frame that is position and scale invariant. We hypothesize that the pulvinar may provide the control signals for routing information through the cortex. The dynamics of the control neurons are governed by simple differential equations that could be realized by neurobiologically plausible circuits. In preattentive mode, the control neurons receive their input from a low-level "saliency map" representing potentially interesting regions of a scene. During the pattern recognition phase, control neurons are driven by the interaction between top-down (memory) and bottom-up (retinal input) sources. The model respects key neurophysiological, neuroanatomical, and psychophysical data relating to attention, and it makes a variety of experimentally testable predictions.