Continuous and lurching traveling pulses in neuronal networks with delay and spatially decaying connectivity

Propagation of discharges in cortical and thalamic systems, which is used as a probe for examining network circuitry, is studied by constructing a one-dimensional model of integrate-and-fire neurons that are coupled by excitatory synapses with delay. Each neuron fires only one spike. The velocity and stability of propagating continuous pulses are calculated analytically. Above a certain critical value of the constant delay, these pulses lose stability. Instead, lurching pulses propagate with discontinuous and periodic spatio-temporal characteristics. The parameter regime for which lurching occurs is strongly affected by the footprint (connectivity) shape; bistability may occur with a square footprint shape but not with an exponential footprint shape. For strong synaptic coupling, the velocity of both continuous and lurching pulses increases logarithmically with the synaptic coupling strength gsyn for an exponential footprint shape, and it is bounded for a step footprint shape. We conclude that the differences in velocity and shape between the front of thalamic spindle waves in vitro and cortical paroxysmal discharges stem from their different effective delay; in thalamic networks, large effective delay between inhibitory neurons arises from their effective interaction via the excitatory cells which display postinhibitory rebound.

Brain mechanisms of quantity are similar in 5-year-old children and adults

Both 5-year-old children and adults determine the quantity of a number by the use of a similar parietal lobe mechanism. Event related potentials indicate that input from Arabic digits and from dot patterns reach areas involved in determining quantity about 200 ms after input. However, voluntary key presses indicating the relation of the input to the quantity five take almost three times as long in children. The ability to trace the networks of brain areas involved in the learning of school subjects should aid in the design and testing of educational methods.

γ-Aminobutyric acid type B receptor-dependent burst-firing in thalamic neurons: A dynamic clamp study

Synchronized network responses in thalamus depend on phasic inhibition originating in the thalamic reticular nucleus (nRt) and are mediated by the neurotransmitter γ-aminobutyric acid (GABA). A suggested role for intra-nRt connectivity in inhibitory phasing remains controversial. Recently, functional GABA type B (GABAB) receptors were demonstrated on nRt cells, and the slow time course of the GABAB synaptic response seems ideally suited to deinactivate low-threshold calcium channels. This promotes burst firing, a characteristic feature of synchronized responses. Here we investigate GABAB-mediated rebound burst firing in thalamic cells. Whole-cell current-clamp recordings were obtained from nRt cells and somatosensory thalamocortical relay cells in rat brain slices. Synthetic GABAB inhibitory postsynaptic potentials, generated by a hybrid computer–neuron synapse (dynamic clamp), triggered rebound low-threshold calcium spikes in both cell types when peak inhibitory postsynaptic potential hyperpolarization was greater than −92 mV. The threshold inhibitory postsynaptic potential conductance for rebound burst generation was comparable in nRt (7 nS) and thalamocortical (5 nS) cells. However, burst onset in nRt (1 s) was considerably delayed compared with thalamocortical (0.6 s) cells. Thus, GABAB inhibitory postsynaptic potentials can elicit low-threshold calcium spikes in both relay and nRt neurons, but the resultant oscillation frequency would be faster for thalamocortical–nRt networks (3 Hz) than for nRt–nRt networks (1–2 Hz). We conclude, therefore, that fast (>2 Hz) GABAB-dependent thalamic oscillations are maintained primarily by reciprocal connections between excitatory and inhibitory cells. These findings further indicate that when oscillatory neural networks contain both recurrent and reciprocal inhibition, then distinct population frequencies may result when one or the other type of inhibition is favored.

How the brain keeps the eyes still

The brain can hold the eyes still because it stores a memory of eye position. The brain’s memory of horizontal eye position appears to be represented by persistent neural activity in a network known as the neural integrator, which is localized in the brainstem and cerebellum. Existing experimental data are reinterpreted as evidence for an “attractor hypothesis” that the persistent patterns of activity observed in this network form an attractive line of fixed points in its state space. Line attractor dynamics can be produced in linear or nonlinear neural networks by learning mechanisms that precisely tune positive feedback.

Intrinsic noise in gene regulatory networks

Cells are intrinsically noisy biochemical reactors: low reactant numbers can lead to significant statistical fluctuations in molecule numbers and reaction rates. Here we use an analytic model to investigate the emergent noise properties of genetic systems. We find for a single gene that noise is essentially determined at the translational level, and that the mean and variance of protein concentration can be independently controlled. The noise strength immediately following single gene induction is almost twice the final steady-state value. We find that fluctuations in the concentrations of a regulatory protein can propagate through a genetic cascade; translational noise control could explain the inefficient translation rates observed for genes encoding such regulatory proteins. For an autoregulatory protein, we demonstrate that negative feedback efficiently decreases system noise. The model can be used to predict the noise characteristics of networks of arbitrary connectivity. The general procedure is further illustrated for an autocatalytic protein and a bistable genetic switch. The analysis of intrinsic noise reveals biological roles of gene network structures and can lead to a deeper understanding of their evolutionary origin.

General and specific brain regions involved in encoding and retrieval of events: what, where, and when.

Remembering an event involves not only what happened, but also where and when it occurred. We measured regional cerebral blood flow by positron emission tomography during initial encoding and subsequent retrieval of item, location, and time information. Multivariate image analysis showed that left frontal brain regions were always activated during encoding, and right superior frontal regions were always activated at retrieval. Pairwise image subtraction analyses revealed information-specific activations at (i) encoding, item information in left hippocampal, location information in right parietal, and time information in left fusiform regions; and (ii) retrieval, item in right inferior frontal and temporal, location in left frontal, and time in anterior cingulate cortices. These results point to the existence of general encoding and retrieval networks of episodic memory whose operations are augmented by unique brain areas recruited for processing specific aspects of remembered events.

A complexity measure for selective matching of signals by the brain.

We have previously derived a theoretical measure of neural complexity (CN) in an attempt to characterize functional connectivity in the brain. CN measures the amount and heterogeneity of statistical correlations within a neural system in terms of the mutual information between subsets of its units. CN was initially used to characterize the functional connectivity of a neural system isolated from the environment. In the present paper, we introduce a related statistical measure, matching complexity (CM), which reflects the change in CN that occurs after a neural system receives signals from the environment. CM measures how well the ensemble of intrinsic correlations within a neural system fits the statistical structure of the sensory input. We show that CM is low when the intrinsic connectivity of a simulated cortical area is randomly organized. Conversely, CM is high when the intrinsic connectivity is modified so as to differentially amplify those intrinsic correlations that happen to be enhanced by sensory input. When the input is represented by an individual stimulus, a positive value of CM indicates that the limited mutual information between sensory sheets sampling the stimulus and the rest of the brain triggers a large increase in the mutual information between many functionally specialized subsets within the brain. In this way, a complex brain can deal with context and go "beyond the information given."