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B W Mel

Publications and source records attributed to B W Mel.

7 recordsLinked to original sources
PubMed2001

Impact of active dendrites and structural plasticity on the memory capacity of neural tissue.

We consider the combined effects of active dendrites and structural plasticity on the storage capacity of neural tissue. We compare capacity for two different modes of dendritic integration: (1) linear, where synaptic inputs are summed across the entire dendritic arbor, and (2) nonlinear, where each dendritic compartment functions as a separately thresholded neuron-like summing unit. We calculate much larger storage capacities for cells with nonlinear subunits and show that this capacity is accessible to a structural learning rule that combines random synapse formation with activity-dependent stabilization/elimination. In a departure from the common view that memories are encoded in the overall connection strengths between neurons, our results suggest that long-term information storage in neural tissue could reside primarily in the selective addressing of synaptic contacts onto dendritic subunits.

Animals↗
PubMed2000

A model for intradendritic computation of binocular disparity.

Many complex cells in mammalian primary visual cortex are finely tuned to binocular disparity. In the prevailing model, several binocular simple cells drive each disparity-tuned complex cell. However, some cat complex cells receive direct LGN input, and binocular simple cells are rare in macaque. In our biophysically detailed compartmental model, active dendrites of a single neuron perform the multiple simple-cell-like subunit computations that underlie both orientation and disparity tuning. The responses of our detailed model could be predicted by a simple algebraic formula closely related to an 'energy' model. Adding inhibitory synapses led to sharper, more contrast-invariant tuning curves. Thus active dendrites could contribute to binocular-disparity tuning in complex cells.

Animals↗
PubMed2000

Minimizing binding errors using learned conjunctive features.

We have studied some of the design trade-offs governing visual representations based on spatially invariant conjunctive feature detectors, with an emphasis on the susceptibility of such systems to false-positive recognition errors-Malsburg's classical binding problem. We begin by deriving an analytical model that makes explicit how recognition performance is affected by the number of objects that must be distinguished, the number of features included in the representation, the complexity of individual objects, and the clutter load, that is, the amount of visual material in the field of view in which multiple objects must be simultaneously recognized, independent of pose, and without explicit segmentation. Using the domain of text to model object recognition in cluttered scenes, we show that with corrections for the nonuniform probability and nonindependence of text features, the analytical model achieves good fits to measured recognition rates in simulations involving a wide range of clutter loads, word size, and feature counts. We then introduce a greedy algorithm for feature learning, derived from the analytical model, which grows a representation by choosing those conjunctive features that are most likely to distinguish objects from the cluttered backgrounds in which they are embedded. We show that the representations produced by this algorithm are compact, decorrelated, and heavily weighted toward features of low conjunctive order. Our results provide a more quantitative basis for understanding when spatially invariant conjunctive features can support unambiguous perception in multiobject scenes, and lead to several insights regarding the properties of visual representations optimized for specific recognition tasks.

Artificial Intelligence↗
PubMed1998

Translation-invariant orientation tuning in visual "complex" cells could derive from intradendritic computations.

first distinguished "simple" from "complex" cells in visual cortex and proposed a processing hierarchy in which rows of LGN cells are pooled to drive oriented simple cell subunits, which are pooled in turn to drive complex cells. Although parsimonious and highly influential, the pure hierarchical model has since been challenged by results indicating that many complex cells receive excitatory monosynaptic input from LGN cells or do not depend on simple cell input. Alternative accounts of complex cell orientation tuning remain scant, however, and the function of monosynaptic LGN contacts onto complex cell dendrites remains unknown. We have used a biophysically detailed compartmental model to investigate whether nonlinear integration of LGN synaptic inputs within the dendrites of individual pyramidal cells could contribute to complex-cell receptive field structure. We show that an isolated cortical neuron with "active" dendrites, driven only by excitatory inputs from overlapping ON- and OFF-center LGN subfields, can produce clear phase-invariant orientation tuning-a hallmark response characteristic of a complex cell. The tuning is shown to depend critically both on the spatial arrangement of LGN synaptic contacts across the complex cell dendritic tree, established by a Hebbian developmental principle, and on the physiological efficacy of excitatory voltage-dependent dendritic ion channels. We conclude that unoriented LGN inputs to a complex cell could contribute in a significant way to its orientation tuning, acting in concert with oriented inputs to the same cell provided by simple cells or other complex cells. As such, our model provides a novel, experimentally testable hypothesis regarding the basis of orientation tuning in the complex cell population, and more generally underscores the potential importance of nonlinear intradendritic subunit processing in cortical neurophysiology.

Action Potentials↗
PubMed1997

SEEMORE: combining color, shape, and texture histogramming in a neurally inspired approach to visual object recognition.

Severe architectural and timing constraints within the primate visual system support the conjecture that the early phase of object recognition in the brain is based on a feedforward feature-extraction hierarchy. To assess the plausibility of this conjecture in an engineering context, a difficult three-dimensional object recognition domain was developed to challenge a pure feedforward, receptive-field-based recognition model called SEEMORE. SEEMORE is based on 102 viewpoint-invariant nonlinear filters that as a group are sensitive to contour, texture, and color cues. The visual domains consists of 100 real objects of many different types, including rigid (shovel), nonrigid (telephone cord), and statistical (maple leaf cluster) objects and photographs of complex scenes. Objects were individually presented in color video images under normal room lighting conditions. Based on 12 to 36 training views, SEEMORE was required to recognize unnormalized test views of objects that could vary in position, orientation in the image plane and in depth, and scale (factor of 2); for nonrigid objects, recognition was also tested under gross shape deformations. Correct classification performance on a test set consisting of 600 novel object views was 97 percent (chance was 1 percent) and was comparable for the subset of 15 nonrigid objects. Performance was also measured under a variety of image degradation conditions, including partial occlusion, limited clutter, color shift, and additive noise. Generalization behavior and classification errors illustrated the emergence of several striking natural shape categories that are not explicitly encoded in the dimensions of the feature space. It is concluded that in the light of the vast hardware resources available in the ventral stream of the primate visual system relative to those exercised here, the appealingly simple feature-space conjecture remains worthy of serious consideration as a neurobiological model.

Artificial Intelligence↗
PubMed1993

Synaptic integration in an excitable dendritic tree.

1. Compartmental modeling experiments were carried out in an anatomically characterized neocortical pyramidal cell to study the integrative behavior of a complex dendritic tree containing active membrane mechanisms. Building on a previously presented hypothesis, this work provides further support for a novel principle of dendritic information processing that could underlie a capacity for nonlinear pattern discrimination and/or sensory processing within the dendritic trees of individual nerve cells. 2. It was previously demonstrated that when excitatory synaptic input to a pyramidal cell is dominated by voltage-dependent N-methyl-D-aspartate (NMDA)-type channels, the cell responds more strongly when synaptic drive is concentrated within several dendritic regions than when it is delivered diffusely across the dendritic arbor. This effect, called dendritic "cluster sensitivity," persisted under wide-ranging parameter variations and directly implicated the spatial ordering of afferent synaptic connections onto the dendritic tree as an important determinant of neuronal response selectivity. 3. In this work, the sensitivity of neocortical dendrites to spatially clustered synaptic drive has been further studied with fast sodium and slow calcium spiking mechanisms present in the dendritic membrane. Several spatial distributions of the dendritic spiking mechanisms were tested with and without NMDA synapses. Results of numerous simulations reveal that dendritic cluster sensitivity is a highly robust phenomenon in dendrites containing a sufficiency of excitatory membrane mechanisms and is only weakly dependent on their detailed spatial distribution, peak conductances, or kinetics. Factors that either work against or make irrelevant the dendritic cluster sensitivity effect include 1) very high-resistance spine necks, 2) very large synaptic conductances, 3) very high baseline levels of synaptic activity, and 4) large fluctuations in level of synaptic activity on short time scales. 4. The functional significance of dendritic cluster sensitivity has been previously discussed in the context of associative learning and memory. Here it is demonstrated that the dendritic tree of a cluster-sensitive neuron implements an approximative spatial correlation, or sum of products operation, such as that which could underlie nonlinear disparity tuning in binocular visual neurons.

Animals↗