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A Caltech Library Repository Feedhttp://www.rssboard.org/rss-specificationpython-feedgenenTue, 16 Apr 2024 14:17:45 +0000Correlations in high dimensional or asymmetric data sets: Hebbian neuronal processing
https://resolver.caltech.edu/CaltechAUTHORS:20130816-103228638
Authors: {'items': [{'id': 'Softky-W-R', 'name': {'family': 'Softky', 'given': 'William R.'}}, {'id': 'Kammen-D-M', 'name': {'family': 'Kammen', 'given': 'Daniel M.'}}]}
Year: 1991
DOI: 10.1016/0893-6080(91)90070-L
The Hebbian neural learning algorithm that implements Principal Component Analysis (PCA) can be extended for the analysis of more realistic forms of neural data by including higher than two-channel correlations and non-Euclidean 1p metrics. Maximizing a dth rank tensor form which correlates d channels is equivalent to raising the exponential order of variance correlation from 2 to d in the algorithm that implements PCA. Simulations suggest that a generalized version of Oja's PCA neuron can detect such a dth order principal component. Arguments from biology and pattern recognition suggest that neural data in general is not symmetric about its mean; performing PCA with an implicit 1l metric rather than the Euclidean metric weights exponentially distributed vectors according to their probability, as does a highly nonlinear Hebb rule. The correlation order d and the 1p metric exponent p were each roughly constant for each of several Hebb rules simulated. High-order correlation analysis may prove increasingly useful as data from large networks of cells engaged in information processing becomes available.https://authors.library.caltech.edu/records/wmpy8-4j085Sub-millisecond coincidence detection in active dendritic trees
https://resolver.caltech.edu/CaltechAUTHORS:20130816-103228831
Authors: {'items': [{'id': 'Softky-W-R', 'name': {'family': 'Softky', 'given': 'William R.'}}]}
Year: 1994
DOI: 10.1016/0306-4522(94)90154-6
Simulations of a morphologically reconstructed cortical pyramidal cell suggest that the long, thin, distal dendrites of such a cell may be ideally suited for nonlinear coincidence-detection at time-scales much faster than the membrane time-constant. In the presence of dendritic sodium spiking conductances, such hypothetical computations might occur by two distinct mechanisms. In one mechanism, fast excitatory synaptic currents inside a thin dendrite create strong local depolarizations, whose repolarization—resulting from charge equalization—can be 100-fold faster than the membrane time-constant; two such potentials in exact coincidence might initiate a dendritic spike. In the alternate mechanism, dendritic sodium spikes which do not fire the soma nonetheless create somatic voltage pulses of millisecond width and a few millivolts amplitude. The soma may fire upon the exact coincidence of several of these dendritic spikes, while their strong delayed-rectifier currents prevent the soma from temporally summating them. The average firing rate of a compartmental simulation of this reconstructed cell can be highly sensitive to the precise (submillisecond) arrangement of its inputs; in one simulation, a subtle reorganization of the temporal and spatial distribution of synaptic events can determine whether the cell fires continuously at 200 Hz or not at all.
The two cellular properties postulated to create this behavior—fast, strong synaptic currents and spiking conductances in the distal dendrites—are at least consistent with physiological recordings of somatic potentials from single and coincident synaptic events; further measurements are proposed. The amplitudes and decays of these simulated fast EPSPs and dendritic spikes can be quantitatively predicted by approximations based on dendritic properties, intracellular resistance, and transmembrane conductance, without invoking any free parameters. These expressions both illustrate the dominant biophysical mechanisms of these very transient events and also allow extrapolation of the simulation results to nearby parameter ranges without requiring further simulation. The possibility that cortical cells perform temporally precise computations on single spikes touches many issues in cortical processing: computational speed, spiking variability, population coding, pairwise cell correlations, multiplexed information transmission, and the functional role of the dendritic tree.https://authors.library.caltech.edu/records/3tz7c-3wr32Comparison of discharge variability in vitro and in vivo in cat visual cortex neurons
https://resolver.caltech.edu/CaltechAUTHORS:20130816-103147929
Authors: {'items': [{'id': 'Holt-G-R', 'name': {'family': 'Holt', 'given': 'Gary R.'}}, {'id': 'Softky-W-R', 'name': {'family': 'Softky', 'given': 'William R.'}}, {'id': 'Koch-C', 'name': {'family': 'Koch', 'given': 'Christof'}, 'orcid': '0000-0001-6482-8067'}, {'id': 'Douglas-R-J', 'name': {'family': 'Douglas', 'given': 'Rodney J.'}}]}
Year: 1996
1. In neocortical slices, the majority of neurons fire quite regularly in response to constant current injections. But neurons in the intact animal fire irregularly in response to constant current injection as well as to visual stimuli. 2. To quantify this observation, we developed a new measure of variability, which compares only adjacent interspike intervals and is therefore less sensitive to rate variations than existing measures such as the coefficient of variation of interspike intervals. 3. We find that the variability of firing is much higher in cells of primary visual cortex in the anesthetized cat than in slice. The response to current injected from an intracellular electrode in vivo is also variable, but slightly more regular and less bursty than in response to visual stimuli. 4. Using a new technique for analyzing the variability of integrate-and-fire neurons, we prove that this behavior is consistent with a simple integrate-and-fire model receiving a large amount of synaptic background activity, but not with a noisy spiking mechanism.https://authors.library.caltech.edu/records/qpvzk-adt49