Biomedical subjects

A Renart

Publications and source records attributed to A Renart.

3 recordsLinked to original sources

PubMed2000

A recurrent model of transformation invariance by association.

This paper describes an investigation of a recurrent artificial neural network which uses association to build transform-invariant representations. The simulation implements the analytic model of Parga and Rolls [(1998). Transform-invariant recognition by association in a recurrent network. Neural Computation 10(6), 1507-1525.] which defines multiple (e.g. "view") patterns to be within the basin of attraction of a shared (e.g. "object") representation. First, it was shown that the network could store and correctly retrieve an "object" representation from any one of the views which define that object, with capacity as predicted analytically. Second, new results extended the analysis by showing that correct object retrieval could occur where retrieval cues were distorted; where there was some association between the views of different objects; and where connectivity was diluted, even when this dilution was asymmetric. The simulations also extended the analysis by showing that the system could work well with sparse patterns; and showing how pattern sparseness interacts with the number of views of each object (as a result of the statistical properties of the pattern coding) to give predictable object retrieval performance. The results thus usefully extend a recurrent model of invariant pattern recognition.

Artificial Intelligence↗

PubMed1999

Backward projections in the cerebral cortex: implications for memory storage.

Cortical areas are characterized by forward and backward connections between adjacent cortical areas in a processing stream. Within each area there are recurrent collateral connections between the pyramidal cells. We analyze the properties of this architecture for memory storage and processing. Hebb-like synaptic modifiability in the connections and attractor states are incorporated. We show the following: (1) The number of memories that can be stored in the connected modules is of the same order of magnitude as the number that can be stored in any one module using the recurrent collateral connections, and is proportional to the number of effective connections per neuron. (2) Cooperation between modules leads to a small increase in memory capacity. (3) Cooperation can also help retrieval in a module that is cued with a noisy or incomplete pattern. (4) If the connection strength between modules is strong, then global memory states that reflect the pairs of patterns on which the modules were trained together are found. (5) If the intermodule connection strengths are weaker, then separate, local memory states can exist in each module. (6) The boundaries between the global and local retrieval states, and the nonretrieval state, are delimited. All of these properties are analyzed quantitatively with the techniques of statistical physics.

Cerebral Cortex↗

PubMed1999

Associative memory properties of multiple cortical modules.

The existence of recurrent collateral connections between pyramidal cells within a cortical area and, in addition, reciprocal connections between connected cortical areas, is well established. In this work we analyse the properties of a tri-modular architecture of this type in which two input modules have convergent connections to a third module (which in the brain might be the next module in cortical processing or a bi-modal area receiving connections from two different processing pathways). Memory retrieval is analysed in this system which has Hebb-like synaptic modifiability in the connections and attractor states. Local activity features are stored in the intra-modular connections while the associations between corresponding features in different modules present during training are stored in the inter-modular connections. The response of the network when tested with corresponding and contradictory stimuli to the two input pathways is studied in detail. The model is solved quantitatively using techniques of statistical physics. In one type of test, a sequence of stimuli is applied, with a delay between them. It is found that if the coupling between the modules is low a regime exists in which they retain the capability to retrieve any of their stored features independently of the features being retrieved by the other modules. Although independent in this sense, the modules still influence each other in this regime through persistent modulatory currents which are strong enough to initiate recall in the whole network when only a single module is stimulated, and to raise the mean firing rates of the neurons in the attractors if the features in the different modules are corresponding. Some of these mechanisms might be useful for the description of many phenomena observed in single neuron activity recorded during short term memory tasks such as delayed match-to-sample. It is also shown that with contradictory stimulation of the two input modules the model accounts for many of the phenomena observed in the McGurk effect, in which contradictory auditory and visual inputs can lead to misperception.

Action Potentials↗