Another neural code?

K J Friston

doi:10.1006/nimg.1997.0260

Home/Archive/PubMed

PubMed · 9345550

Another neural code?

K J Friston

View original source ↗Export metadata ↓

Abstract

This paper presents the conjecture that functional integration may be mediated by the mutual induction and maintenance of stereotyped spatiotemporal patterns of activity (i.e., transients) in different neuronal populations. In contradistinction to temporal and rate coding models of neuronal interactions, transient coding considers that transactions among neuronal systems use transient dynamics that are distributed in a structured way over both space and time. In contrast to synchronization models, transient coding does not depend on interactions at the same frequencies, in different parts of the brain, but involves covariations among different frequencies and can therefore be considered a more general form of coding. Using an analysis of the correlations among the spectral density of neuromagnetic signals, measured at different cortical regions, this hypothesis was confirmed. For example high (gamma)-frequency oscillations in the prefrontal cortex are associated with low (20 Hz)-frequency oscillations in the parietal cortex. The results are consistent with transient coding and suggest that transient dynamics endure for at least 40-200 ms. Transient coding means that correlations (rate coding) and coherence (synchrony) are neither complete nor sufficient characterizations of neuronal interactions. Although temporal coding, rate coding, and synchrony are important aspects of neuronal interactions, the results speak to further integrative neuronal mechanisms of a more general nature.

Explore related subjects

Models, Neurological Neurons Normal Distribution Time Factors Journal Article Research Support, Non-U.S. Gov't Review

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

K J Friston. 1997. Another neural code?. https://doi.org/10.1006/nimg.1997.0260

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

PubMed2025

Balanced state of networks of winner-take-all units.

Irregularly timed action potentials, or spikes, are pervasively observed in the brain activity of awake mammals. However, the role of this temporal irregularity in neural computation is still not well understood. In canonical network models irregular spiking emerges via balanced, fluctuating input currents, leading to collective responses that track inputs linearly. How networks characterized by irregular spiking could support flexible nonlinear dynamics needed for general-purpose computation remains under ongoing debate. Here we characterize the dynamics of networks whose elementary unit is not a single neuron but a small group of neurons, with distinct tunings, that compete at each timestep via a winner-take-all (WTA) interaction. While WTA has long been proposed as an elementary functional motif in the brain and represents a powerful computational primitive, how large networks of such units behave has received less investigation. We show that these networks, like classic excitatory-inhibitory balanced networks, exhibit a chaotic fluctuation-driven regime characterized by sustained irregular activity resembling realistic cortical spiking, which we interpret as a multidimensional balance spread over several competing neural populations with different tunings. We develop a mean-field theory for the network, which shows how irregular spiking sustained by time-varying input fluctuations can support flexible nonlinear collective dynamics. Using the theory we predict and verify network regimes in which input fluctuations alone yield multistability, stable sequence generation, or complex heterogeneous firing rate dynamics-three core dynamical primitives thought to underlie memory-dependent neural computation-via consistent Poisson-like spiking produced through chaos. Thus, networks of WTA units support a chaotic fluctuation-driven regime characterized by irregular spiking that can power complex nonlinear collective dynamics. This represents a new model of brain activity capable of simultaneously reproducing realistic spike trains and diverse nonlinear firing rate patterns well posed for flexible computation, and which can be trained or fit to data.

Models, Neurological↗

PubMed1999

Computing with self-excitatory cliques: A model and an application to hyperacuity-scale computation in visual cortex.

We present a model of visual computation based on tightly inter-connected cliques of pyramidal cells. It leads to a formal theory of cell assemblies, a specific relationship between correlated firing patterns and abstract functionality, and a direct calculation relating estimates of cortical cell counts to orientation hyperacuity. Our network architecture is unique in that (1) it supports a mode of computation that is both reliable and efficient; (2) the current-spike relations are modeled as an analog dynamical system in which the requisite computations can take place on the time scale required for an early stage of visual processing; and (3) the dynamics are triggered by the spatiotemporal response of cortical cells. This final point could explain why moving stimuli improve vernier sensitivity.

Models, Neurological↗

PubMed1998

Neural feature abstraction from judgments of similarity.

The common neural network modeling practice of representing the elements of a task domain in terms of a set of features lacks justification if the features are derived through some form of ad hoc preabstraction. By examining a featural similarity model related to established multidimensional scaling techniques, a neural network is developed that generates features from similarity data and attaches weights to these features. The network performs a constrained search of a continuous solution space to determine the features and uses a previously developed regularization technique to minimize the number of features it derives. The network is demonstrated on artificial data, from which it recovers known features and weights, and on two real data sets involving the similarity of a set of geometric shapes and the abstract conceptual similarities of the 10 Arabic numerals. On the basis of these results, the relationship between the multidimensional scaling approach adopted by the network and an alternative additive clustering approach to feature extraction is discussed.

Models, Neurological↗