Statistical mechanics of neocortical interactions: Path-integral evolution of short-term memory

Abstract

Previous papers in this series of statistical mechanics of neocortical interactions (SMNI) have detailed a development from the relatively microscopic scales of neurons up to the macroscopic scales as recorded by electroencephalography (EEG), requiring an intermediate mesocolumnar scale to be developed at the scale of minicolumns (≈ 10 2 neurons) and macrocolumns (≈ 10 5 neurons). Opportunity was taken to view SMNI as sets of statistical constraints, not necessarily describing specific synaptic or neuronal mechanisms, on neuronal interactions, on some aspects of short-term memory (STM), e.g., its capacity, stability, and duration. A recently developed C-language code, PATHINT, provides a non-Monte Carlo technique for calculating the dynamic evolution of arbitrary-dimension (subject to computer resources) nonlinear Lagrangians, such as derived for the two-variable SMNI problem. Here, PATHINT is used to explicitly detail the evolution of the SMNI constraints on STM.

Statistical Mechanics of Neocortical Interactions: Testing Theories with Multiple Imaging Data

“…SMNI studies have detailed that maximal numbers of attractors lie within the physical firing space of excitatory and inhibitory minicolumnar firings, consistent with experimentally observed capacities of auditory and visual STM, when a "centering" mechanism is enforced by shifting background noise in synaptic interactions, consistent with experimental observations under conditions of selective attention (Mountcastle et al, 1981;Ingber, 1984;Ingber, 1985;Ingber, 1994;Ingber and Nunez, 1995). This leads to all attractors of the shorttime distribution lying along a diagonal line in firing space, effectively defining a narrow parabolic trough containing these most likely firing states.…”

Statistical Mechanics of Neocortical Interactions: Portfolio of Physiological Indicators

“…SMNI studies have detailed that maximal numbers of attractors lie within the physical firing space of M G , where G = {Excitatory, Inhibitory} minicolumnar firings, consistent with experimentally observed capacities of auditory and visual STM, when a "centering" mechanism is enforced by shifting background noise in synaptic interactions, consistent with experimental observations under conditions of selective attention [15,23,17,20,29]. This leads to all attractors of the short-time distribution lying along a diagonal line in M G space, effectively defining a narrow parabolic trough containing these most likely firing states.…”

“…Since 1981, a series of papers on the statistical mechanics of neocortical interactions (SMNI) has been developed to model columns and regions of neocortex, spanning mm to cm of tissue. Most of these papers have dealt explicitly with calculating properties of STM and scalp EEG in order to test the basic formulation of this approach [6,7,9,10,[15][16][17][18][19][20][21][22][23][24][25][26][27][28].…”

“…SMNI derives aggregate behavior of experimentally observed columns of neurons from statistical electrical-chemical properties of synaptic interactions. While not useful to yield insights at the single neuron level, SMNI has demonstrated its capability in describing large-scale properties of short-term memory and electroencephalographic (EEG) systematics [6,7,9,[15][16][17][18][19][20].…”

Spatiotemporal scales and links between electrical neuroimaging modalities

“…Models describing the generation of macroscopic fields as measured by the EEG, the intracerebral field potentials (LFPs), and the MEG can be encompassed into two broad classes: (1) neural mass models or network models [23,43,46,47] that aim to describe the complex connectivity of neural networks and their excitatory and inhibitory interconnections and (2) electromagnetic models (EM models) that rely on the macroscopic version of Maxwell equations and are typically used for source imaging and modeling of field propagation in neural tissue. Neural mass models emphasize the local effects of the neural circuitry on the network dynamic but often disregard the long distance effects of macroscopic fields.…”