PubMed Health⌕ Search

Biomedical subjects

L H Finkel

Publications and source records attributed to L H Finkel.

13 recordsLinked to original sources
PubMed2000

Color-opponent receptive fields derived from independent component analysis of natural images.

Independent Component Analysis (ICA) of images of natural scenes has been shown to generate basis functions, or filters, which resemble spatial [Bell & Sejnowski (1997). Vision Research, 37, 3327-3338; van Hateren & van der Schaaf (1998). Proceedings of the Royal Society of London B, 265, 359-366] and spatiotemporal [van Hateren & Ruderman (1998) Proceedings of the Royal Society of London B, 265, 2315-2320] receptive fields of simple cells of the striate cortex. ICA yields statistically independent components which provide for a redundancy-reduced representation of the data. Using one of several published algorithms [Lee (1998). Independent component analysis: theory and applications. Boston; Kluwer Academic], we applied linear ICA to color images of natural scenes. The resulting independent component filters (ICFs) separate into either luminance or color filters. The luminance filters are localized and oriented edge detectors as reported previously. The color filters resemble either blue-yellow or red-green double-opponent receptive fields with various orientations. An equal number of each type of filter (luminance, red-green, and blue-yellow) is obtained. Thus, ICA predicts that spatiochromatic information is coded in statistically independent luminance, blue-yellow, and red-green opponent pathways with a relatively equal representation and specific spatial profiles at the cortical level.

Algorithms↗
PubMed2000

Neuroengineering models of brain disease.

The techniques of computational simulation have begun to be applied to modeling neurological disease and mental illness. Such neuroengineering models provide a conceptual bridge between molecular/cellular pathology and cognitive performance. We consider models of Alzheimer's disease, Parkinson's disease, and schizophrenia. Each of these diseases involves a disorder of neuromodulation coupled with underlying neuronal pathology. Parallels arising between these models suggests that a common set of computational mechanisms may account for functional loss across a spectrum of brain diseases. In particular, we focus on attractor-based network dynamics and how they arise from neural architectures, on mechanisms for linking sequences of attractor states and their role in cognition, and on the role of neuromodulation in controlling these processes. These studies suggest new approaches to understanding the forebrain circuits underlying cognition, and point toward a new tool for dissecting the pathophysiology of brain disease.

Alzheimer Disease↗
PubMed1998

Extraction of perceptually salient contours by striate cortical networks.

We present a cortical-based model for computing the perceptual salience of contours embedded in noisy images. It has been suggested that horizontal intra-cortical connections in primary visual cortex may modulate contrast detection thresholds and pre-attentive "pop-out". In our model, horizontal connections mediate context-dependent facilitatory and inhibitory interactions among oriented cells. Strongly facilitated cells undergo temporal synchronization; and perceptual salience is determined by the level of synchronized activity. The model accounts for a range of reported psychophysical and physiological effects of contour salience. In particular, the model proposes that intrinsic properties of synchronization account for the increased salience of smooth, closed contours. Application of the model to real images is demonstrated.

Contrast Sensitivity↗
PubMed1998

Neuromodulatory control of hippocampal function: towards a model of Alzheimer's disease.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder of cognitive function whose cellular pathology and molecular etiology have been increasingly and dramatically unraveled over the last several years. Despite this substantial knowledge base, the disease remains poorly understood due to a basic lack of understanding of how memories are stored and recalled in the brain. We describe a preliminary attempt at constructing a detailed model of these basic neural mechanisms; in particular, the natural dynamics of neuronal activity in hippocampal region CA3 and the modulation and control of these dynamics by subcortical cholinergic and GABAergic input to the hippocampus. We view the construction of such a model, with sufficient detail at the cellular and subcellular level, to be a necessary first step in understanding the effect of AD pathology on the functional behavior of the underlying neural circuitry. The network is based on the 66-compartment hippocampal pyramidal cell model of Traub and colleagues and their 51-compartment interneuron interconnected with realistic AMPA-, NMDA-, and GABA(A)-mediated synapses. Traub and others have shown that a network composed of these modeled cells is capable of synchronization in the gamma frequency range. We demonstrate here that this synchronization mechanism can implement an attractor-based autoassociative memory. A new input pattern arrives at the beginning of each theta cycle (comprised of 5-10 gamma cycles), and the pattern of activity across the network converges, over several gamma cycles, to a stable attractor that represents the stored memory. In this model, cholinergic deprivation, one of the hallmarks of AD, leads to a slowing of the gamma frequency which reduces the number of "cycles" available to reach an attractor state. We suggest that this may be one mechanism underlying the memory loss and cognitive slowing seen in AD. Our results also support the idea that acetylcholine acts on individual neurons to induce and maintain a transition from intrinsic bursting to spiking in pyramidal cells. These results are consistent with the hypothesis that spiking and bursting in CA3 pyramidal cells mediate separate behavioral functions, and that cholinergic input is required for the transition to and support of behavioral states associated with the online processing and recall of information.

Acetylcholine↗
PubMed1997

Coarse-grain parallel computing for very large scale neural simulations in the NEXUS simulation environment.

We describe a neural simulator designed for simulating very large scale models of cortical architectures. This simulator, NEXUS, uses coarse-grain parallel computing by distributing computation and data onto multiple conventional workstations connected via a local area network. Coarse-grain parallel computing offers natural advantages in simulating functionally segregated neural processes. We partition a complete model into modules with locally dense connections--a module may represent a cortical area, column, layer, or functional entity. Asynchronous data communications among workstations are established through the Network File System, which, together with the implicit modularity, decreases communications overhead, and increases overall performance. Coarse-grain parallelism also benefits from the standardization of conventional workstations and LAN, including portability between generations and vendors.

Computer Communication Networks↗
PubMed1995

Network simulations of retinal and cortical contributions to color constancy.

A biologically-based neural network simulation is used to analyze the contributions to color perception of each of several processing steps in the visual system from the retina to cortical area V4. We consider the effects on color constancy and color induction of adaptation, spectral opponency, non-linearities including saturation and rectification, and spectrally-specific long-range inhibition. This last stage is a novel mechanism based on cells which have been described in V4. The model has been tested with simulations of several well known psychophysical color constancy and color induction experiments. We conclude from these simulations the following: (1) a simple push-pull spectrally specific contrast mechanism, using large surrounds analogous to those found in V4, is very effective in producing general color constancy and color induction behavior; (2) given some spatio-temporal averaging, receptor adaptation can also produce a degree of color constancy; (3) spectrally opponent processes have spatial frequency dependent responses to color and brightness contrast which affect the contribution of the V4 mechanism to color constancy in images with nonuniform backgrounds; and (4) the effect of the V4 mechanism depends on the difference between center and surround while the effect of adaptation depends on the total sum of inputs from both center and surround and therefore the two stages cooperate to increase the range of stimulus conditions under which color constancy can be achieved.

Adaptation, Ocular↗
PubMed1995

Characterization of the spatial-frequency spectrum in the perception of shape from texture.

The major cue to shape from texture is the compression of texture as a function of surface curvature [J. Exp. Psychol. 13, 242 (1987); Vision Res. 33, 827 (1993)]. A number of computational models have been proposed in which compression is measured by detection of changes in the spatial-frequency spectrum [Comput. Graphics Image Process. 5, (1976)]. We propose that the visual system uses a strategy of characterizing the frequency spectrum by a simple set of measures and of tracking the changes in this characterization rather than determining changes in the shape of the actual spectra. Our evidence is based on a number of psychophysical demonstrations that use stimuli with specifically tailored frequency spectra, constructed from white noise filtered in the frequency domain. Our evidence suggests that the visual system determines the average peak frequency of the spectrum and uses this measure as its characterization. Changes in fp are strongly correlated with the degree of surface curvature, and, over a range of stimuli, fp takes account of the variance in local estimates of the frequency spectrum. One computes fp by determining the peak frequency at each spatial location and then averaging these frequency values over a local spatial region. We show that fp is related to the second-order moment but is more biologically plausible and shows superior ability to function in the presence of noise. As a test of this model, we have constructed a neural network architecture for computing shape from texture. Our model is limited to orthographically projected, homogeneous textures without in-surface rotation. The early stages of the model consist of multiple simple-cell units tuned to different orientations and spatial frequencies. We show that these simple cells are inadequate for the determination of compression but that the outputs of complex-cell-like units after normalization generate estimates of surface slant and tilt. The network shows qualitative agreement with human perception of shape from texture over a wide range of real and artificial stimuli.

Form Perception↗
PubMed1989

Integration of distributed cortical systems by reentry: a computer simulation of interactive functionally segregated visual areas.

A computer model based on visual cortex has been constructed to analyze how the operations of multiple, functionally segregated cortical areas can be coordinated and integrated to yield a unified perceptual response. We propose that cortical integration arises through the process of reentry--the ongoing, parallel, recursive signaling between separate maps along ordered anatomical connections. To test the efficacy of this reentrant cortical integration (RCI) model, we have carried out detailed computer simulations of 3 interconnected cortical areas in the striate and extrastriate cortex of the macaque. The simulated networks contained a total of over 222,000 units and 8.5 million connections. The 3 modeled areas, called VOR, VOC, and VMO, incorporate major anatomical and physiological properties of cortical areas V1, V3, and V5 but are vastly simplified compared with monkey visual cortex. Simulated area VOR contains both orientation and directionally selective units; simulated area VMO discriminates the direction of motion of arbitrarily oriented objects; and simulated area VOC responds to both luminance and occlusion boundaries in the stimulus. Area VOC is able to respond to illusory contours (Kanizsa, 1979) by means of the same neural architecture used for the discrimination of occlusion boundaries. This architecture also generates responses to structure-from-motion by virtue of reentrant connections from VMO to VOC. The responses of the simulated networks to these illusions are consistent with the perceptual responses of humans and other species presented with these stimuli. The networks also respond in a consistent manner to a novel illusion that combines illusory contours and structure-from-motion. The response synthesized to this combined illusion provides a strong argument supporting the need for a recursive reentrant process in the cortex. Functional integration of the simulated areas in the RCI model were found to depend upon the combined action of 3 reentrant processes: (1) conflicting responses among segregated areas are competitively eliminated, (2) outputs of each area are used by other areas in their own operations, and (3) outputs of an area are "reentered" back to itself (through lower areas) and can thus be used iteratively to synthesize responses to complex or illusory stimuli. Transection of the reentrant connections selectively abolished these integrative processes and led to failure of figural synthesis. The proposed model of reentry suggests a basis for understanding how multiple visual areas as well as other cortical areas may be integrated within a distributed system.

Animals↗
PubMed1987

Plasticity in the organization of adult cerebral cortical maps: a computer simulation based on neuronal group selection.

Recent experimental evidence from the somatosensory, auditory, and visual systems documents the existence of functional plasticity in topographic map organization in adult animals. This evidence suggests that an ongoing competitive organizing process controls the locations of map borders and the receptive field properties of neurons. A computer model based on the process of neuronal group selection has been constructed that accounts for reported results on map plasticity in somatosensory cortex. The simulations construct a network of locally connected excitatory and inhibitory cells that receives topographic projections from 2 receptor sheets corresponding to the glabrous and dorsal surfaces of the hand (a typical simulation involves approximately 1500 cells, 70,000 intrinsic and 100,000 extrinsic connections). Both intrinsic and extrinsic connections undergo activity-dependent modifications according to a synaptic rule based on heterosynaptic interactions. Repeated stimulation of the receptor sheet resulted in the formation of neuronal groups-local sets of strongly interconnected neurons in the network. Cells in most groups were found to have similar receptive fields: they were exclusively glabrous or dorsal despite equal numbers of anatomical connections from both surfaces. The sharpness of map borders was due to the sharpness of the underlying group structure; shifts in the locations of these borders resulted from competition between groups. Following perturbations of the input, the network underwent changes similar to those observed experimentally in monkey somatosensory cortex. Repeated local tapping on the receptor sheet resulted in a large increase in the magnification factor of the stimulated region. Transection of the connections from a glabrous region resulted in the organization of a new representation of corresponding dorsal region. The detailed simulations provide several insights into the mechanisms of such changes, as well as a series of predictions about cortical behavior for further experimental test.

Brain Mapping↗
PubMed1986

Antibodies to liver cell adhesion molecule perturb inductive interactions and alter feather pattern and structure.

Cell adhesion molecules (CAMs) may act as regulators of morphogenesis by constraining cell motion, forming borders, and controlling intercellular communications that lead to embryonic induction. This postulated causal role of CAMs in inductive events was tested here in an in vitro system of feather induction. In the developing chicken skin, an ectodermal sheet of epithelium interacts with mesodermal cell collectives to form more or less circular feather germs arranged in a hexagonal pattern. Cells of the epidermal epithelium are linked by liver CAM (L-CAM) and mesodermal cells in dermal condensations are linked by neural CAM (N-CAM); neither of these CAMs links cells in one tissue of this inductive couple to cells in the other. After perturbation of the L-CAM linkage in epidermis by antibodies to L-CAM, nonhexagonal striped patterns of dermal condensations were observed in culture. The stripes did not follow straight lines but meandered in lateral and oblique directions. Histological examination of the perturbed tissues showed extensive changes in dermal cell density distributions. After 10 days of culture, the perturbed tissues developed a cobbled or plaque-like morphology resembling scales rather than the feather-like filamentous structures that formed in unperturbed skin cultures. The results indicate that perturbation of CAM binding in tissues linked by one CAM can alter fates and interactions of cells linked by another, presumably by altering the amount or effect of inductive signals crossing the border between the inducing cell collectives. A computer model based on the notion that the response of L-CAM-linked epidermal cells to signals from N-CAM-linked dermal cells depends cooperatively on the degree of L-CAM linkage was found to generate hexagonal patterns for the unperturbed case and stripes after perturbation of L-CAM bonds.

Animals↗
PubMed1985

Interaction of synaptic modification rules within populations of neurons.

A theoretical account is given for long- and short-term changes in populations of neurons subject to independent rules for pre- and postsynaptic modification. The postsynaptic rule proposes that coactivated heterosynaptic inputs to a neuron alter the states of ion channels at a given synapse, thereby changing the susceptibility of these channels to local biochemical alterations. The resultant change in the population distribution of local channel states affects the postsynaptic potential produced at the synapse by subsequent inputs. This postsynaptic rule applies, in general, to short-term changes at specific individual synapses. In contrast, the presynaptic rule applies in general to long-term changes in the whole neuron, resulting in an altered probability of transmitter release. Because of neuroanatomical constraints, the presynaptic rule affects large numbers of synapses defined by the connectivity of that neuron and distributed nonspecifically over the population. We show that the combined action of the two independent rules upon populations of neurons arrayed in interconnected neuronal groups leads to consistent alterations of the probability of firing of certain circuits while maintaining variability in the response of the population to novel input. This "dual rules" model fulfills the requirements of the theory of neuronal group selection.

Electrophysiology↗