Biomedical subjects

O Hendin

Publications and source records attributed to O Hendin.

4 recordsLinked to original sources

PubMed1998

Associative memory and segmentation in an oscillatory neural model of the olfactory bulb.

We discuss the first few stages of olfactory processing in the framework of a layered neural network. Its central component is an oscillatory associative memory, describing the external plexiform layer, that consists of inhibitory and excitatory neurons with dendrodendritic interactions. We explore the computational properties of this neural network and point out its possible functional role in the olfactory bulb. When receiving a complex input that is composed of several odors, the network segments it into its components. This is done in two stages. First, multiple odor input is preprocessed in the glomerular layer via a decorrelation mechanism that relies on temporal independence of odor sources. Second, as the recall process of a pattern consists of associative convergence to an oscillatory attractor, multiple inputs are identified by alternate dominance of memory patterns during different sniff cycles. This could explain how quick analysis of mixed odors is subserved by the rapid sniffing behavior of highly olfactory animals. When one of the odors is much stronger than the rest, the network converges onto it, thus displaying odor masking.

Animals↗

PubMed1998

Medical volume rendering over the WWW using VRML and JAVA.

The rapid growth of the World Wide Web (WWW) enables access to huge amounts of data and applications. The diversity of data-structures and applications has led to the concept of network computing where the data is encapsulated within the application. The end-user does not have to worry about tools for data manipulation as they are bundled together with the data itself. However, the user usually has to pay a price in the form of degraded performance. While JAVA is gradually taking its place as the network cross platform programming language, it is clear that it currently does not support high-performance visualization. The purpose of this paper is to demonstrate that high performance volume rendering, traditionally reserved for high-end visual computing, can now be made widely available in a cross-platform fashion using VRML and JAVA.

Algorithms↗

PubMed1997

The role of inhibition in an associative memory model of the olfactory bulb.

The external plexiform layer is where the interactions between the mitral (excitatory) and granule (inhibitory) cells of the olfactory bulb (OB) take place. Two outstanding features of these interactions are that they are dendrodendritic and that there seem to be none between excitatory cells. The latter are usually credited with the role of forming Hebbian cell assemblies. Hence, it would seem that this structure lacks the necessary ingredients for an associative memory system. In this article we show that in spite of these two properties this system can serve as an associative memory. Our model incorporates the essential anatomical characteristics of the OB. The memories in our system, defined by Hebbian mitral assemblies, are activated via the interactions with the inhibitory granule cells. The nonlinearity is introduced in our model via a sigmoid function that describes neurotransmitter release in reciprocal dendrodendritic synapses. The capacity (maximal number of odors that can be memorized) depends on the sparseness of coding that is being used. For very low memory activities, the capacity grows as a fractional power of the number of neurons. We validate the theoretical results by numerical simulations. An interesting result of our model is that its capacity increases as a function of the ratio of inhibitory to excitatory populations. This may provide an explanation for the dominance of inhibitory cells in the olfactory bulb.

Memory↗

PubMed1994

Decomposition of a mixture of signals in a model of the olfactory bulb.

We describe models for the olfactory bulb which perform separation and decomposition of mixed odor inputs from different sources. The odors are unknown to the system; hence this is an analog and extension of the engineering problem of blind separation of signals. The separation process makes use of the different temporal fluctuations of the input odors which occur under natural conditions. We discuss two possibilities, one relying on a specific architecture connecting modules with the same sensory inputs and the other assuming that the modules (e.g., glomeruli) have different receptive fields in odor space. We compare the implications of these models for the testing of mixed odors from a single source.

Animals↗