			Home About us Contact

NEURAL COMPUTATION (neural + computation)

Distribution by Scientific Domains

Life Sciences	80%

Selected Abstracts

THE HARMONIC MIND: FROM NEURAL COMPUTATION TO OPTIMALITY-THEORETIC GRAMMAR,VOLUME 1: COGNITIVE ARCHITECTURE AND VOLUME 2: LINGUISTIC AND PHILOSOPHICAL IMPLICATIONS

ANALYTIC PHILOSOPHY, Issue 3 2009
WILLIAM RAMSEY
First page of article [source]

A learning rule for place fields in a cortical model: Theta phase precession as a network effect

HIPPOCAMPUS, Issue 7 2005
Silvia Scarpetta
Abstract We show that a model of the hippocampus introduced recently by Scarpetta et al. (2002, Neural Computation 14(10):2371,2396) explains the theta phase precession phenomena. In our model, the theta phase precession comes out as a consequence of the associative-memory-like network dynamics, i.e., the network's ability to imprint and recall oscillatory patterns, coded both by phases and amplitudes of oscillation. The learning rule used to imprint the oscillatory states is a natural generalization of that used for static patterns in the Hopfield model, and is based on the spike-time-dependent synaptic plasticity, experimentally observed. In agreement with experimental findings, the place cells' activity appears at consistently earlier phases of subsequent cycles of the ongoing theta rhythm during a pass through the place field, while the oscillation amplitude of the place cells' firing rate increases as the animal approaches the center of the place field and decreases as the animal leaves the center. The total phase precession of the place cell is lower than 360°, in agreement with experiments. As the animal enters a receptive field, the place cells' activity comes slightly less than 180° after the phase of maximal pyramidal cell population activity, in agreement with the findings of Skaggs et al. (1996, Hippocampus 6:149,172). Our model predicts that the theta phase is much better correlated with location than with time spent in the receptive field. Finally, in agreement with the recent experimental findings of Zugaro et al. (2005, Nature Neuroscience 9(1):67,71), our model predicts that theta phase precession persists after transient intrahippocampal perturbation. © 2005 Wiley-Liss, Inc. [source]

Computational significance of transient dynamics in cortical networks

EUROPEAN JOURNAL OF NEUROSCIENCE, Issue 1 2008
Daniel Durstewitz
Abstract Neural responses are most often characterized in terms of the sets of environmental or internal conditions or stimuli with which their firing rate are correlated increases or decreases. Their transient (nonstationary) temporal profiles of activity have received comparatively less attention. Similarly, the computational framework of attractor neural networks puts most emphasis on the representational or computational properties of the stable states of a neural system. Here we review a couple of neurophysiological observations and computational ideas that shift the focus to the transient dynamics of neural systems. We argue that there are many situations in which the transient neural behaviour, while hopping between different attractor states or moving along ,attractor ruins', carries most of the computational and/or behavioural significance, rather than the attractor states eventually reached. Such transients may be related to the computation of temporally precise predictions or the probabilistic transitions among choice options, accounting for Weber's law in decision-making tasks. Finally, we conclude with a more general perspective on the role of transient dynamics in the brain, promoting the view that brain activity is characterized by a high-dimensional chaotic ground state from which transient spatiotemporal patterns (metastable states) briefly emerge. Neural computation has to exploit the itinerant dynamics between these states. [source]

Action potential initiation and propagation in hippocampal mossy fibre axons

THE JOURNAL OF PHYSIOLOGY, Issue 7 2008
Christoph Schmidt-Hieber
Dentate gyrus granule cells transmit action potentials (APs) along their unmyelinated mossy fibre axons to the CA3 region. Although the initiation and propagation of APs are fundamental steps during neural computation, little is known about the site of AP initiation and the speed of propagation in mossy fibre axons. To address these questions, we performed simultaneous somatic and axonal whole-cell recordings from granule cells in acute hippocampal slices of adult mice at ,23°C. Injection of short current pulses or synaptic stimulation evoked axonal and somatic APs with similar amplitudes. By contrast, the time course was significantly different, as axonal APs had a higher maximal rate of rise (464 ± 30 V s,1 in the axon versus 297 ± 12 V s,1 in the soma, mean ±s.e.m.). Furthermore, analysis of latencies between the axonal and somatic signals showed that APs were initiated in the proximal axon at ,20,30 ,m distance from the soma, and propagated orthodromically with a velocity of 0.24 m s,1. Qualitatively similar results were obtained at a recording temperature of ,34°C. Modelling of AP propagation in detailed cable models of granule cells suggested that a ,4 times higher Na+ channel density (,1000 pS ,m,2) in the axon might account for both the higher rate of rise of axonal APs and the robust AP initiation in the proximal mossy fibre axon. This may be of critical importance to separate dendritic integration of thousands of synaptic inputs from the generation and transmission of a common AP output. [source]

Analyzing brain networks with PCA and conditional Granger causality

HUMAN BRAIN MAPPING, Issue 7 2009
Zhenyu Zhou
Abstract Identifying directional influences in anatomical and functional circuits presents one of the greatest challenges for understanding neural computations in the brain. Granger causality mapping (GCM) derived from vector autoregressive models of data has been employed for this purpose, revealing complex temporal and spatial dynamics underlying cognitive processes. However, the traditional GCM methods are computationally expensive, as signals from thousands of voxels within selected regions of interest (ROIs) are individually processed, and being based on pairwise Granger causality, they lack the ability to distinguish direct from indirect connectivity among brain regions. In this work a new algorithm called PCA based conditional GCM is proposed to overcome these problems. The algorithm implements the following two procedures: (i) dimensionality reduction in ROIs of interest with principle component analysis (PCA), and (ii) estimation of the direct causal influences in local brain networks, using conditional Granger causality. Our results show that the proposed method achieves greater accuracy in detecting network connectivity than the commonly used pairwise Granger causality method. Furthermore, the use of PCA components in conjunction with conditional GCM greatly reduces the computational cost relative to the use of individual voxel time series. Hum Brain Mapp, 2009. © 2008 Wiley-Liss, Inc. [source]