6,185 research outputs found
An associative network with spatially organized connectivity
We investigate the properties of an autoassociative network of
threshold-linear units whose synaptic connectivity is spatially structured and
asymmetric. Since the methods of equilibrium statistical mechanics cannot be
applied to such a network due to the lack of a Hamiltonian, we approach the
problem through a signal-to-noise analysis, that we adapt to spatially
organized networks. The conditions are analyzed for the appearance of stable,
spatially non-uniform profiles of activity with large overlaps with one of the
stored patterns. It is also shown, with simulations and analytic results, that
the storage capacity does not decrease much when the connectivity of the
network becomes short range. In addition, the method used here enables us to
calculate exactly the storage capacity of a randomly connected network with
arbitrary degree of dilution.Comment: 27 pages, 6 figures; Accepted for publication in JSTA
How informative are spatial CA3 representations established by the dentate gyrus?
In the mammalian hippocampus, the dentate gyrus (DG) is characterized by
sparse and powerful unidirectional projections to CA3 pyramidal cells, the
so-called mossy fibers. Mossy fiber synapses appear to duplicate, in terms of
the information they convey, what CA3 cells already receive from entorhinal
cortex layer II cells, which project both to the dentate gyrus and to CA3.
Computational models of episodic memory have hypothesized that the function of
the mossy fibers is to enforce a new, well separated pattern of activity onto
CA3 cells, to represent a new memory, prevailing over the interference produced
by the traces of older memories already stored on CA3 recurrent collateral
connections. Can this hypothesis apply also to spatial representations, as
described by recent neurophysiological recordings in rats? To address this
issue quantitatively, we estimate the amount of information DG can impart on a
new CA3 pattern of spatial activity, using both mathematical analysis and
computer simulations of a simplified model. We confirm that, also in the
spatial case, the observed sparse connectivity and level of activity are most
appropriate for driving memory storage and not to initiate retrieval.
Surprisingly, the model also indicates that even when DG codes just for space,
much of the information it passes on to CA3 acquires a non-spatial and episodic
character, akin to that of a random number generator. It is suggested that
further hippocampal processing is required to make full spatial use of DG
inputs.Comment: 19 pages, 11 figures, 1 table, submitte
Noise-enhanced computation in a model of a cortical column
Varied sensory systems use noise in order to enhance detection of weak
signals. It has been conjectured in the literature that this effect, known as
stochastic resonance, may take place in central cognitive processes such as the
memory retrieval of arithmetical multiplication. We show in a simplified model
of cortical tissue, that complex arithmetical calculations can be carried out
and are enhanced in the presence of a stochastic background. The performance is
shown to be positively correlated to the susceptibility of the network, defined
as its sensitivity to a variation of the mean of its inputs. For nontrivial
arithmetic tasks such as multiplication, stochastic resonance is an emergent
property of the microcircuitry of the model network
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