Prelude. Cycle 1. Introduction. Cycle 2. Structure defines function. Cycle 3. Diversity of cortical functions is provided by inhibition. Cycle 4. Windows on the brain. Cycle 5. A system of rhythms: from simple to complex dynamics. Cycle 6. Synchronization by oscillation. Cycle 7. The brain's default state: self-organized oscillations in rest and sleep. Cycle 8. Perturbation of the default patterns by experience. Cycle 9. The gamma buzz: gluing by oscillations in the waking brain. Cycle 10. Perceptions and actions are brain state-dependent. Cycle 11. Oscillations in the "other cortex:" navigation in real and memory space. Cycle 12. Coupling of systems by oscillations. Cycle 13. The tough problem. References.

Rhythms of the brain

Gamma frequency oscillations are thought to provide a temporal structure for information processing in the brain. They contribute to cognitive functions, such as memory formation and sensory processing, and are disturbed in some psychiatric disorders. Fast-spiking, parvalbumin-expressing, soma-inhibiting interneurons have a key role in the generation of these oscillations. Experimental analysis in the hippocampus and the neocortex reveals that synapses among these interneurons are highly specialized. Computational analysis further suggests that synaptic specialization turns interneuron networks into robust gamma frequency oscillators.

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Synaptic mechanisms of synchronized gamma oscillations in inhibitory interneuron networks

Markram describes the impressive aims of the Blue Brain Project, in which the enormous computing power of IBM's Blue Gene supercomputer is being harnessed to build biologically accurate models of the neocortical column and, ultimately, the whole brain.

The blue brain project.

Grouping of brain rhythms in corticothalamic systems

We review different aspects of the simulation of spiking neural networks. We start by reviewing the different types of simulation strategies and algorithms that are currently implemented. We next review the precision of those simulation strategies, in particular in cases where plasticity depends on the exact timing of the spikes. We overview different simulators and simulation environments presently available (restricted to those freely available, open source and documented). For each simulation tool, its advantages and pitfalls are reviewed, with an aim to allow the reader to identify which simulator is appropriate for a given task. Finally, we provide a series of benchmark simulations of different types of networks of spiking neurons, including Hodgkin-Huxley type, integrate-and-fire models, interacting with current-based or conductance-based synapses, using clock-driven or event-driven integration strategies. The same set of models are implemented on the different simulators, and the codes are made available. The ultimate goal of this review is to provide a resource to facilitate identifying the appropriate integration strategy and simulation tool to use for a given modeling problem related to spiking neural networks.

Simulation of networks of spiking neurons: A review of tools and strategies

To better understand population phenomena in thalamocortical neuronal ensembles, we have constructed a preliminary network model with 3,560 multicompartment neurons (containing soma, branching dendrites, and a portion of axon). Types of neurons included superficial pyramids (with regular spiking [RS] and fast rhythmic bursting [FRB] firing behaviors); RS spiny stellates; fast spiking (FS) interneurons, with basket-type and axoaxonic types of connectivity, and located in superficial and deep cortical layers; low threshold spiking (LTS) interneurons, which contacted principal cell dendrites; deep pyramids, which could have RS or intrinsic bursting (IB) firing behaviors, and endowed either with nontufted apical dendrites or with long tufted apical dendrites; thalamocortical relay (TCR) cells; and nucleus reticularis (nRT) cells. To the extent possible, both electrophysiology and synaptic connectivity were based on published data, although many arbitrary choices were necessary. In addition to synaptic connectivity (by AMPA/kainate, NMDA, and GABA(A) receptors), we also included electrical coupling between dendrites of interneurons, nRT cells, and TCR cells, and--in various combinations--electrical coupling between the proximal axons of certain cortical principal neurons. Our network model replicates several observed population phenomena, including 1) persistent gamma oscillations; 2) thalamocortical sleep spindles; 3) series of synchronized population bursts, resembling electrographic seizures; 4) isolated double population bursts with superimposed very fast oscillations (>100 Hz, "VFO"); 5) spike-wave, polyspike-wave, and fast runs (about 10 Hz). We show that epileptiform bursts, including double and multiple bursts, containing VFO occur in rat auditory cortex in vitro, in the presence of kainate, when both GABA(A) and GABA(B) receptors are blocked. Electrical coupling between axons appears necessary (as reported previously) for persistent gamma and additionally plays a role in the detailed shaping of epileptogenic events. The degree of recurrent synaptic excitation between spiny stellate cells, and their tendency to fire throughout multiple bursts, also appears critical in shaping epileptogenic events.

https://www.physiology.org/doi/pdf/10.1152/jn.00983.2004

Hilary Murray

Papers

Single-column thalamocortical network model exhibiting gamma oscillations, sleep spindles, and epileptogenic bursts

Analysis of Mechanisms and Functional Implications Multiple Firing Patterns in Deep Dorsal Horn Neurons of the Spinal Cord: Computational