George P. Moore

Bio: George P. Moore is an academic researcher from Imperial College London. The author has contributed to research in topics: Excitatory postsynaptic potential & Inhibitory postsynaptic potential. The author has an hindex of 11, co-authored 15 publications receiving 4107 citations. Previous affiliations of George P. Moore include RAND Corporation & University of California, Los Angeles.

Pacemaker Neurons: Effects of Regularly Spaced Synaptic Input

Abstract: The consequences of inhibitory or excitatory synaptic input between pacemaker neurons were predicted mathematically and through digital-computer simulations, and the predicted behavior was found to occur in abdominal ganglia of Aplysia and in stretch receptors of Procambarus. Discharge patterns under conditions that do not involve interneuronal feedback are characteristic and self-stabilizing. Paradoxically, increased arrival rates of inhibitory input can increase firing rates, and increased excitatory input rates can decrease firing rates.

Input-output relations in computer-simulated nerve cells. Influence of the statistical properties, strength, number and inter-dependence of excitatory pre-synaptic terminals.

Abstract: This communication examines, in digital computer simulated networks, the input-output relation established at synaptic level. It is restricted to excitatory junctions and analyzes the changes in post-synaptic discharge which occur when the number of pre-synaptic terminals increases while the EPSP size decreases, when the statistical structure or “form” (as measured by the interspike interval mean, standard deviation, histogram and by the autocorrelogram) of the spike train in each pre-synaptic fiber changes, and/or when the interdependence between pre-synaptic fibers varies from complete independence to strong dependence. 1Independent Pre-synaptic Terminals. When the number of pre-synaptic terminals increases and the EPSP size decreases proportionally (while the input form remains constant), the post-synaptic interspike interval mean increases slightly, the standard deviation decreases markedly, the histogram becomes sharp and narrow and the autocorrelogram becomes periodic. When, on the other hand, the pre-synaptic form varies (while the number of terminals and the EPSP size remain constant), the effect upon the post-synaptio output depends upon the given number of terminals and EPSP size. If terminals are few and EPSP's large, the output varies with the pre-synaptic form. The post-synaptic coefficient of variation is linearly related to the pre-synaptic coefficient of variation, the slope decreasing as the number of inputs increases. If terminals are numerous and weak, the pre-synaptic form ceases to be influential and the post-synaptic cell generates the same output regardless of the detailed structure of the corresponding input. The output common to any combination of independent weak input forms is a very regular train of evenly spaced spikes. (This conclusion is valid unless pre-synaptic terminals fire at extremely low rates.) Such results are mathematically predictable in a simple and realistic model of membrane potential and threshold dynamics (see Appendix). As the EPSP size increases, all other variables being equal, the post-synaptic interval mean decreases monotonically. The decrease is smooth or in steps depending on whether the pre-synaptic form is Poisson or pacemaker, respectively. Post-synaptic spikes are effectively blocked by relatively small numbers of inhibitory terminals. 2Dependent Pre-synaptic Terminals. When there is a physiological amount of interdependence between the presynaptic terminals that impinge upon the post-synaptic cell, the activity of the latter is a function of the statistical form of the input channels, even when the latter are numerous and weak. This happens when interdependence involves only a proportion of all terminals or only the terminals within separate and independent groups. In order to understand the transactions that take place in the nervous system, it is necessary to identify the presynaptic statistics that influence the corresponding post-synaptic discharge. When pre-synaptic terminals produce large PSP's their influence is dominant and exerted by way of the precise statistical form of the discharge. When terminals produce small PSP's their influence is contingent on their degree of interdependence. If they are uncorrelated, they act exclusively by way of their mean rates and provide a smooth adjustment of the post-synaptic membrane potential and firing rate. If terminals are correlated, they act by way of several statistical features and assume a dominant role that determines a precise relation between pre-synaptic timing and post-synaptic firing. The degree of inter-terminal correlation is thus a functionally significant variable.

The brainweb: phase synchronization and large-scale integration.

Abstract: The emergence of a unified cognitive moment relies on the coordination of scattered mosaics of functionally specialized brain regions. Here we review the mechanisms of large-scale integration that counterbalance the distributed anatomical and functional organization of brain activity to enable the emergence of coherent behaviour and cognition. Although the mechanisms involved in large-scale integration are still largely unknown, we argue that the most plausible candidate is the formation of dynamic links mediated by synchrony over multiple frequency bands.

Oscillatory responses in cat visual cortex exhibit inter-columnar synchronization which reflects global stimulus properties.

Abstract: A FUNDAMENTAL step in visual pattern recognition is the establishment of relations between spatially separate features. Recently, we have shown that neurons in the cat visual cortex have oscillatory responses in the range 40–60 Hz (refs 1,2) which occur in synchrony for cells in a functional column and are tightly correlated with a local oscillatory field potential. This led us to hypothesize that the synchronization of oscillatory responses of spatially distributed, feature selective cells might be a way to establish relations between features in different parts of the visual field2,3. In support of this hypothesis, we demonstrate here that neurons in spatially separate columns can synchronize their oscillatory responses. The synchronization has, on average, no phase difference, depends on the spatial separation and the orientation preference of the cells and is influenced by global stimulus properties.

The synaptic organization of the brain

Abstract: Introduction to synaptic circuits, Gordon M.Shepherd and Christof Koch membrane properties and neurotransmitter actions, David A.McCormick peripheral ganglia, Paul R.Adams and Christof Koch spinal cord - ventral horn, Robert E.Burke olfactory bulb, Gordon M.Shepherd, and Charles A.Greer retina, Peter Sterling cerebellum, Rodolfo R.Llinas and Kerry D.Walton thalamus, S.Murray Sherman and Christof Koch basal ganglia, Charles J.Wilson olfactory cortex, Lewis B.Haberly hippocampus, Thomas H.Brown and Anthony M.Zador neocortex, Rodney J.Douglas and Kevan A.C.Martin Gordon M.Shepherd. Appendix: Dendretic electrotonus and synaptic integration.

Visual feature integration and the temporal correlation hypothesis

Abstract: The mammalian visual system is endowed with a nearly infinite capacity for the recognition of patterns and objects. To have acquired this capability the visual system must have solved what is a fundamentally combinatorial prob­ lem. Any given image consists of a collection of features, consisting of local contrast borders of luminance and wavelength, distributed across the visual field. For one to detect and recognize an object within a scene, the features comprising the object must be identified and segregated from those comprising other objects. This problem is inherently difficult to solve because of the combinatorial nature of visual images. To appreciate this point, consider a simple local feature such as a small vertically oriented line segment placed within a fixed location of the visual field. When combined with other line segments, this feature can form a nearly infinite number of geometrical objects. Any one of these objects may coexist with an equally large number of other

Spiking Neuron Models: Single Neurons, Populations, Plasticity

Abstract: Neurons in the brain communicate by short electrical pulses, the so-called action potentials or spikes. How can we understand the process of spike generation? How can we understand information transmission by neurons? What happens if thousands of neurons are coupled together in a seemingly random network? How does the network connectivity determine the activity patterns? And, vice versa, how does the spike activity influence the connectivity pattern? These questions are addressed in this 2002 introduction to spiking neurons aimed at those taking courses in computational neuroscience, theoretical biology, biophysics, or neural networks. The approach will suit students of physics, mathematics, or computer science; it will also be useful for biologists who are interested in mathematical modelling. The text is enhanced by many worked examples and illustrations. There are no mathematical prerequisites beyond what the audience would meet as undergraduates: more advanced techniques are introduced in an elementary, concrete fashion when needed.