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Repeated presentations of sensory stimuli generate transient gamma-frequency (30-80 Hz) responses in neocortex that show plasticity in a task-dependent manner. Complex relationships between individual neuronal outputs and the mean, local field potential (population activity) accompany these changes, but little is known about the underlying mechanisms responsible. Here we show that transient stimulation of input layer 4 sufficient to generate gamma oscillations induced two different, lamina-specific plastic processes that correlated with lamina-specific changes in responses to further, repeated stimulation: Unit rates and recruitment showed overall enhancement in supragranular layers and suppression in infragranular layers associated with excitatory or inhibitory synaptic potentiation onto principal cells, respectively. Both synaptic processes were critically dependent on activation of GABAB receptors and, together, appeared to temporally segregate the cortical representation. These data suggest that adaptation to repetitive sensory input dramatically alters the spatiotemporal properties of the neocortical response in a manner that may both refine and minimize cortical output simultaneously.

Very fast oscillations (VFOs, >80 Hz) are important for physiological brain processes and, in excess, with certain epilepsies. Putative mechanisms for VFO include interneuron spiking and network activity in coupled pyramidal cell axons. It is not known whether either, or both, of these apply in pathophysiological conditions. Spontaneously occurring interictal discharges occur in human tissue in vitro, resected from neocortical epileptic foci. VFO associated with these discharges was manifest in both field potential and, with phase delay, in excitatory synaptic inputs to fast spiking interneurons. Recruitment of somatic pyramidal cell and interneuron spiking was low, with no correlation between VFO power and synaptic inputs to principal cells. Reducing synaptic inhibition failed to affect VFO occurrence, but they were abolished by reduced gap junction conductance. These data suggest a lack of a causal role for interneurons, and favor a nonsynaptic pyramidal cell network origin for VFO in epileptic human neocortex.

We perform a systematic data analysis on high resolution (0.5–12 kHz) multiarray microelectrode recordings from an animal model of spontaneous limbic epilepsy, to investigate the role of high frequency oscillations and the occurrence of early precursors for seizures. Results of spectral analysis confirm the importance of very high frequency oscillations (even greater than 600 Hz) in normal (healthy) and abnormal (epileptic) hippocampus. Furthermore, we show that the measures of Recurrence Quantification Analysis (RQA) and Recurrence Time Statistics (RTS) are successful in indicating, rather uniquely, the onset of ictal state and the occurrence of some warnings/precursors during the pre-ictal state, in contrast to the linear measures investigated.

An essential requirement for the representation of functional patterns in complex neural networks, such as the mammalian cerebral cortex, is the existence of stable network activations within a limited critical range. In this range, the activity of neural populations in the network persists between the extremes of quickly dying out, or activating the whole network. The latter case of large-scale activation is visible in the transition to the epileptic state. After describing the shift to large-scale synchronization we study the role of network topology on this transition. Whereas standard explanations for balanced activity involve populations of inhibitory neurons for limiting activity, we observe how network topology limits activity spreading. A random or small-world topology results in low or high levels of activation. In contrast, a cluster hierarchy based on neuroanatomical knowledge—from cortical clusters such as the visual cortex at the highest level, to individual columns at the lowest level—enables sustained activity in neural systems and prevents large-scale activation as observed during epileptic seizures. The containment of activation critically depends on the ratio of inter-cluster connections. Such topological inhibition by means of a modular hierarchy, in addition to neuronal inhibition, might help to maintain healthy levels of neural activity.

Nervous systems are complex networks par excellence, capable of generating and integrating information from multiple external and internal sources. Within the neuroanatomical network (structural connectivity), the non-linear dynamics of neurons and neuronal populations result in patterns of statistical dependencies (functional connectivity) and causal interactions (effective connectivity), defining three major modalities of complex brain networks [61]. How does the structure of the network relate to its function and what effect do changes of edge or node properties have [37]? Since 1992 [1, 76] tools from graph theory and network analysis [18] were applied to study these questions in neural systems. After describing the properties of neural systems—fiber tracts between brain areas of the mammalian cortex and axons between individual neurons in the nematode C. elegans—we describe dynamics in structure and function. Structural dynamics concern changes in network topology by deleting or adding edges or nodes. We describe the deletion of components in terms of the removal of tissue during strokes or head injuries and the addition of components during the development and growth of neural system. The network topology is robust to random but reacts critically to targeted attacks – similar to a scale-free network. The simulations on network evolution show that spatial growth and time windows during development are sufficient for generating small-world and multi-cluster networks. Functional dynamics concern changes in the activity level of individual neurons or of cortical regions. We observe the spreading of activation first describing spreading in excitable media and then in cardiac tissue before moving to spreading of epileptic seizures in neural networks. In neural networks, the hierarchical and modular topology provides a novel mechanisms of limiting spreading in addition to the known influence of inhibitory nodes. Finally, we

This module represents the formalised opinion of the authors and the CARMEN consortium, which identifies the minimum information required to report the use of electrophysiology in a neuroscience study, for submission to the CARMEN system ("http://www.carmen.org.uk":www.carmen.org.uk).

A total of 32 microwire electrodes were implanted bilaterally into the hippocampus of Sprague-Dawley rats, which were then stimulated in the manner prescribed for the chronic limbic epilepsy model. After the initial seizure brought on by the stimulation, the animals were recorded at a high sampling rate (approximately 12 kHz) for the entire duration of the latent period. Coherence was calculated across channels in both stimulated (and later seizing) animals and non-stimulated (and thus non-seizing control) animals. Average coherence over time was greatest in intrahemispherical electrode pairs in both stimulated and non-stimulated animals. However, the 200-800 Hz band displays increased coherence interhemispherically and up to 200 Hz band displays decreased coherence interhemispherically: this occurs only in stimulated animals.

Recently a number of groups [1,2] have reported on the existence of pathological High frequency oscillations (HFO's) (oscillations in the frequency range of 80–200 Hz, termed as Ripple band and oscillations in the frequency range of 200 Hz and above, termed as Fast Ripple band) in the epileptic brain both in in-vivo and in-vitro experiments. Our goal in this study is to study the statistical modulation of HFOs during epileptogenesis in order to characterize their function in progression to seizures in the epileptic brain. In this study we define a HFO event as a subset of wave having significant high frequency component with low wave amplitude. HFO are detected from data recorded at a sampling rate of 12000 Hz for the entire duration of epileptogenesis which lasts anywhere from about 3–6 weeks. Statistical analysis on the HFO suggest that occurrence of HFO's occur primarily during the 12 hour dark cycle whereas the HFO's primarily seem to occur during the 12 hour day cycle in the control rat The video recording shows that the rat is primarily in active and exploratory state during the dark cycle. These observations suggest that HFO in epileptic rats are correlated with the state of arousal. Spatial correlation of HFOs in different regions of the brain is also investigated with cross-correlogram. Comparison of cross-correlogram of the post-stimulus HFO in the epileptic rat to the pre stimulus HFO (control) suggests modification in the circuitry in the hippocampus, evidence for which in in-vitro experiments were provided by [3].

Epilepsy is a common neurological disorder that can have damaging effects in the brain including over 50% loss of neuronal activity in the hippocampal regions of the CA1 and CA3. The pre-ictal period was studied in an animal model of limbic epilepsy using Shannon entropy and correlation analysis. The primary aim was to uncover underlying relative changes in signals between the Dentate Gyrus and CA1 areas of the bilateral hippocampus. Preliminary entropy analysis results included dynamical changes between channels in the Dentate Gyrus and channels in the CA1 region at and around the time of the seizure. Epilepsy affects 3-5% of the population worldwide. Epilepsy is a neurological disorder characterized by recurrent and unprovoked seizures. An individual loses awareness when experiencing a complex partial seizure due to the spread of the seizure through both temporal lobes and subsequently impairing memory. (1) Of all cases, approximately 60% respond favorably to anti-epileptic drugs. (2) Regardless of age, sex or race, the harmful effects of limbic Epilepsy can be caused by past infections, vascular malformations, hamartomas and gliomas. Head trauma in the form of hemorrhaging or contusion in the brain often leads to the development of limbic Epilepsy after a number of months to years. This span of time is known as a latent period when cellular and network changes are thought to occur precipitating the onset of seizures. In epileptogenesis over 50% of the neurons in the hippocampal regions of the CA1 and CA3 are lost. Neuronal loss also occurs with the granule cells in the Dentate Gyrus; accompanying these changes is a loss of inhibitory neurons, excitatory neurons and excitatory axonal sprouting. (1) The Chronic Limbic Epilepsy rat model imitates human limbic epilepsy with the initial insult to the brain quiescent period and resultant seizures later in life. The manner in which these seizures develop is thought to be a result of structural changes in the brain such as the strengthening of excitatory networks, loss of inhibitory neurons or suppression of GABA receptors. (3) Since little is known about the time period over which the changes occur, it is proposed that detectable changes occur gradually within the brain over the latent period eventually causing the later hypersynchronous seizure activity. The link between the nature of the pre-ictal period and the electrical changes manifested in the brain are not well characterized. The abnormal mode of communication, a characteristic of seizures, is demonstrated by large- amplitude wave discharges occurring over a large hemisphere of the brain. The preictal period in an animal model of limbic epilepsy is studied using Shannon entropy measurements. The goal of this research is to characterize underlying changes in signals between the Dentate Gyrus and CA1 areas of the bilateral hippocampus.