Neuroscience Information Framework

Options
Only Pubmed Central
Include Pubmed Central
Sections
Title
Abstract
Introduction
Methods
Results
Supplement
Appendix
Contributions
Background
Commentary
Funding
Limitations
Caption
FILTERS

Computational aspects of feedback in neural circuits.

Authors:
Maass W, Joshi P, Sontag ED
Affiliation:
Journal:
PLoS computational biology

Abstract

It has previously been shown that generic cortical microcircuit models can perform complex real-time computations on continuous input streams, provided that these computations can be carried out with a rapidly fading memory. We investigate the computational capability of such circuits in the more realistic case where not only readout neurons, but in addition a few neurons within the circuit, have been trained for specific tasks. This is essentially equivalent to the case where the output of trained readout neurons is fed back into the circuit. We show that this new model overcomes the limitation of a rapidly fading memory. In fact, we prove that in the idealized case without noise it can carry out any conceivable digital or analog computation on time-varying inputs. But even with noise, the resulting computational model can perform a large class of biologically relevant real-time computations that require a nonfading memory. We demonstrate these computational implications of feedback both theoretically, and through computer simulations of detailed cortical microcircuit models that are subject to noise and have complex inherent dynamics. We show that the application of simple learning procedures (such as linear regression or perceptron learning) to a few neurons enables such circuits to represent time over behaviorally relevant long time spans, to integrate evidence from incoming spike trains over longer periods of time, and to process new information contained in such spike trains in diverse ways according to the current internal state of the circuit. In particular we show that such generic cortical microcircuits with feedback provide a new model for working memory that is consistent with a large set of biological constraints. Although this article examines primarily the computational role of feedback in circuits of neurons, the mathematical principles on which its analysis is based apply to a variety of dynamical systems. Hence they may also throw new light on the computational role of feedback in other complex biological dynamical systems, such as, for example, genetic regulatory networks.

  1. Welcome

    Welcome to NIF. Explore available research resources: data, tools and materials, from across the web

  2. Community Resources

    Search for resources specially selected for NIF community

  3. More Resources

    Search across hundreds of additional biomedical databases

  4. Literature

    Search Pub Med abstracts and full text from PubMed Central

  5. Insert your Query

    Enter your search terms here and hit return. Search results for the selected tab will be returned.

  6. Join the Community

    Click here to login or register and join this community.

  7. Categories

    Narrow your search by selecting a category. For additional help in searching, view our tutorials.

  8. Query Info

    Displays the total number of search results. Provides additional information on search terms, e.g., automated query expansions, and any included categories or facets. Expansions, filters and facets can be removed by clicking on the X. Clicking on the + restores them.

  9. Search Results

    Displays individual records and a brief description. Click on the icons below each record to explore additional display options.

X