Results 1  10
of
11
Vector Reconstruction from Firing Rates
, 1994
"... . In a number of systems including wind detection in the cricket, visual motion perception and coding of arm movement direction in the monkey and place cell response to position in the rat hippocampus, firing rates in a population of tuned neurons are correlated with a vector quantity. We examine an ..."
Abstract

Cited by 112 (7 self)
 Add to MetaCart
. In a number of systems including wind detection in the cricket, visual motion perception and coding of arm movement direction in the monkey and place cell response to position in the rat hippocampus, firing rates in a population of tuned neurons are correlated with a vector quantity. We examine and compare several methods that allow the coded vector to be reconstructed from measured firing rates. In cases where the neuronal tuning curves resemble cosines, linear reconstruction methods work as well as more complex statistical methods requiring more detailed information about the responses of the coding neurons. We present a new linear method, the optimal linear estimator (OLE), that on average provides the best possible linear reconstruction. This method is compared with the more familiar vector method and shown to produce more accurate reconstructions using far fewer recorded neurons. Introduction To determine how information is represented by nervous systems, we need to understand ...
Functional Significance Of LongTerm Potentiation For Sequence Learning And Prediction
 Cerebral Cortex
, 1994
"... Population coding, where neurons with broad and overlapping firing rate tuning curves collectively encode information about a stimulus, is a common feature of sensory systems.We use decoding methods and measured properties of NMDAmediated LTP induction to study the impact of longterm potentiation ..."
Abstract

Cited by 52 (9 self)
 Add to MetaCart
Population coding, where neurons with broad and overlapping firing rate tuning curves collectively encode information about a stimulus, is a common feature of sensory systems.We use decoding methods and measured properties of NMDAmediated LTP induction to study the impact of longterm potentiation of synapses between the neurons of such a coding array. We find that, due to a temporal asymmetry in the induction of NMDAmediated LTP, firing patterns in a neuronal array that initially represent the current value of a sensory input will, after training, provide an experiencedbased prediction of that input instead. We compute how this prediction arises from and depends on the training experience. We also show how the encoded prediction can be used to generate learned motor sequences, such as the movement of a limb. This involves a novel form of memory recall that is driven by the motor response so that it automatically generates new information at a rate appropriate for the task being per...
Decoding Neuronal Firing And Modeling Neural Networks
 Quart. Rev. Biophys
, 1994
"... Introduction Biological neural networks are large systems of complex elements interacting through a complex array of connections. Individual neurons express a large number of active conductances (Connors et al., 1982; Adams & Gavin, 1986; Llin'as, 1988; McCormick, 1990; Hille, 1992) and exhibit a w ..."
Abstract

Cited by 25 (4 self)
 Add to MetaCart
Introduction Biological neural networks are large systems of complex elements interacting through a complex array of connections. Individual neurons express a large number of active conductances (Connors et al., 1982; Adams & Gavin, 1986; Llin'as, 1988; McCormick, 1990; Hille, 1992) and exhibit a wide variety of dynamic behaviors on time scales ranging from milliseconds to many minutes (Llin'as, 1988; HarrisWarrick & Marder, 1991; Churchland & Sejnowski, 1992; Turrigiano et al., 1994). Neurons in cortical circuits are typically coupled to thousands of other neurons (Stevens, 1989) and very little is known about the strengths of these synapses (although see Rosenmund et al., 1993; Hessler et al., 1993; Smetters & Nelson, 1993). The complex firing patterns of large neuronal populations are difficult to describe let alone understand. There is little point in accurately modeling each membrane potential in a large neural
Statistical mechanics of neocortical interactions: Multiple scales of EEG
, 1993
"... The statistical mechanics of neocortical interactions (SMNI) approach derives a theoretical model for aggregated neuronal activity that defines the “dipole” assumed by many EEG researchers. This defines a nonlinear stochastic filter to extract EEG signals. ..."
Abstract

Cited by 19 (19 self)
 Add to MetaCart
The statistical mechanics of neocortical interactions (SMNI) approach derives a theoretical model for aggregated neuronal activity that defines the “dipole” assumed by many EEG researchers. This defines a nonlinear stochastic filter to extract EEG signals.
Statistical mechanics of neocortical interactions: Training and testing canonical momenta indicators of EEG
 Mathl. Computer Modelling
, 1998
"... Abstract—A series of papers has developed a statistical mechanics of neocortical interactions (SMNI), deriving aggregate behavior of experimentally observed columns of neurons from statistical electricalchemical properties of synaptic interactions. While not useful to yield insights at the single ne ..."
Abstract

Cited by 13 (10 self)
 Add to MetaCart
Abstract—A series of papers has developed a statistical mechanics of neocortical interactions (SMNI), deriving aggregate behavior of experimentally observed columns of neurons from statistical electricalchemical properties of synaptic interactions. While not useful to yield insights at the single neuron level, SMNI has demonstrated its capability in describing largescale properties of shortterm memory and electroencephalographic (EEG) systematics. The necessity of including nonlinear and stochastic structures in this development has been stressed. Sets of EEG and evoked potential data were fit, collected to investigate genetic predispositions to alcoholism and to extract brain “signatures ” of shortterm memory. Adaptive Simulated Annealing (ASA), a global optimization algorithm, was used to perform maximum likelihood fits of Lagrangians defined by path integrals of multivariate conditional probabilities. Canonical momenta indicators (CMI) are thereby derived for individual’s EEG data. The CMI give better signal recognition than the raw data, and can be used to advantage as correlates of behavioral states. These results give strong quantitative support for an accurate intuitive picture, portraying neocortical interactions as having common algebraic or physics mechanisms that scale across quite disparate spatial scales and functional or behavioral phenomena, i.e., describing interactions among neurons, columns of neurons, and regional masses of neurons. This paper adds to these previous investigations two important aspects, a description of how the CMI may be used in source localization, and calculations using previously ASAfitted parameters in outofsample data.
Statistical mechanics of neocortical interactions: Canonical momenta indicators of EEG
 Physical Review E
, 1997
"... Aseries of papers has developed a statistical mechanics of neocortical interactions (SMNI), deriving aggregate behavior of experimentally observed columns of neurons from statistical electricalchemical properties of synaptic interactions. While not useful to yield insights at the single neuron leve ..."
Abstract

Cited by 3 (1 self)
 Add to MetaCart
Aseries of papers has developed a statistical mechanics of neocortical interactions (SMNI), deriving aggregate behavior of experimentally observed columns of neurons from statistical electricalchemical properties of synaptic interactions. While not useful to yield insights at the single neuron level, SMNI has demonstrated its capability in describing largescale properties of shortterm memory and electroencephalographic (EEG) systematics. The necessity of including nonlinear and stochastic structures in this development has been stressed. Sets of EEG and evoked potential data were fit, collected to investigate genetic predispositions to alcoholism and to extract brain “signatures ” of shortterm memory. Adaptive Simulated Annealing (ASA), a global optimization algorithm, was used to perform maximum likelihood fits of Lagrangians defined by path integrals of multivariate conditional probabilities. Canonical momenta indicators (CMI) are thereby derived for individual’s EEG data. The CMI give better signal recognition than the raw data, and can be used to advantage as correlates of behavioral states. These results give strong quantitative support for an accurate intuitive picture, portraying neocortical interactions as having common algebraic or physics mechanisms that scale across quite disparate spatial scales and functional or behavioral phenomena, i.e., describing interactions among neurons, columns of neurons, and regional masses of neurons. PA CSNos.: 87.10.+e, 05.40.+j, 02.50.r, 02.70.cStatistical Mechanics of Neocortical...2 Lester Ingber 1.
Vector Reconstruction from Firing Rates
, 1994
"... . In a number of systems including wind detection in the cricket, visual motion perception and coding of arm movement direction in the monkey and place cell response to position in the rat hippocampus, firing rates in a population of tuned neurons are correlated with a vector quantity. We examine an ..."
Abstract
 Add to MetaCart
. In a number of systems including wind detection in the cricket, visual motion perception and coding of arm movement direction in the monkey and place cell response to position in the rat hippocampus, firing rates in a population of tuned neurons are correlated with a vector quantity. We examine and compare several methods that allow the coded vector to be reconstructed from measured firing rates. In cases where the neuronal tuning curves resemble cosines, linear reconstruction methods work as well as more complex statistical methods requiring more detailed information about the responses of the coding neurons. We present a new linear method, the optimal linear estimator (OLE), that on average provides the best possible linear reconstruction. This method is compared with the more familiar vector method and shown to produce more accurate reconstructions using far fewer recorded neurons. Introduction To determine how information is represented by nervous systems, we need to understand...
Attractor Network Models of Cortical Associative Memory
"... The theories of early brain scientists like Hebb and v. Hayek were in many ways analogous to modern attractor network models. In a series of simulations we have addressed the question whether or not a network of biological neurons could function as an attractor network. We found robust attractor dyn ..."
Abstract
 Add to MetaCart
The theories of early brain scientists like Hebb and v. Hayek were in many ways analogous to modern attractor network models. In a series of simulations we have addressed the question whether or not a network of biological neurons could function as an attractor network. We found robust attractor dynamics displayed on a timescale compatible with perceptual performance. The action at the cellular level of monoamine neuromodulators, like e.g. serotonin, could be modelled and effects on network behaviour were observed. The brain, however, is a highly complex multinetwork structure and models have to take this into account before one can get a more complete understanding of the full range of its function.
and
, 1995
"... We present highresolution pathintegral calculations of a previously developed model of shortterm memory in neocortex. These calculations, made possible with supercomputer resources, supplant similar calculations made in L. Ingber, Phys. Rev. E 49, 4652 (1994), and support coarser estimates made i ..."
Abstract
 Add to MetaCart
We present highresolution pathintegral calculations of a previously developed model of shortterm memory in neocortex. These calculations, made possible with supercomputer resources, supplant similar calculations made in L. Ingber, Phys. Rev. E 49, 4652 (1994), and support coarser estimates made in L. Ingber, Phys. Rev. A 29, 3346 (1984). We also present a current experimental context for the relevance of these calculations using the approach of statistical mechanics of neocortical interactions, especially in the context of electroencephalographic data. PA CSNos.: 87.10.+e, 05.40.+j, 02.50.r, 02.70.cStatistical Mechanics of Neocortical...2 Ingber and Nunez I.
Neuron Review Rates and Rhythms: A Synergistic View of Frequency and Temporal Coding in Neuronal Networks
"... In the CNS, activity of individual neurons has a small but quantifiable relationship to sensory representations and motor outputs. Coactivation of a few 10s to 100s of neurons can code sensory inputs and behavioral task performance within psychophysical limits. However, in a sea of sensory inputs an ..."
Abstract
 Add to MetaCart
In the CNS, activity of individual neurons has a small but quantifiable relationship to sensory representations and motor outputs. Coactivation of a few 10s to 100s of neurons can code sensory inputs and behavioral task performance within psychophysical limits. However, in a sea of sensory inputs and demand for complex motor outputs how is the activity of such small subpopulations of neurons organized? Two theories dominate in this respect: increases in spike rate (rate coding) and sharpening of the coincidence of spiking in active neurons (temporal coding). Both have computational advantages and are far from mutually exclusive. Here, we review evidence for a bias in neuronal circuits toward temporal coding and the coexistence of rate and temporal coding during population rhythm generation. The coincident expression of multiple types of gamma rhythm in sensory cortex suggests a mechanistic substrate for combining rate and temporal codes on the basis of stimulus strength. In determining how the brain codes for sensory inputs and motor outputs two types of measurement dominate the literature: the outputs (action potentials or units) of identified neurons or groups thereof and the local mean synaptic inputs (local, faror extracranial field potentials). Patterns observed in either