Search

Acting at postsynaptic alpha 1- and beta 1-receptors, norepinephrine (NE) exerts a complex action in rat hippocampus. It is currently believed that beta 1-receptor activation enhances excitability of recorded neurons, whereas alpha 1 activation suppresses reactivity to afferent stimulation. These reported effects of alpha-agonists are not consistent with alpha 1 effects found elsewhere in the brain. We have conducted experiments in the anesthetized rat and found that an amphetamine-induced increase in the dentate gyrus population spike can be blocked by a beta-antagonist but also by an alpha 1-antagonist. We have conducted experiments in the brain slide preparation and found that an alpha-agonist, phenylephrine (PHE), selectively enhances responses to N-methyl-D-aspartate (NMDA) but not to quisqualate. We propose that the product of activation of both alpha- and beta-receptor types will enhance reactivity of hippocampal cells to afferent stimulation.

Pyramidal neurons are the principal neurons of the neocortex and their excitatory impact on other pyramidal neurons and interneurons is central to neocortical dynamics. A fundamental principal that has emerged which governs pyramidal neuron excitation of other neurons in the local circuitry of neocortical columns is differential anatomical and physiological properties of the synaptic innervation via the same axon depending on the type of neuron targeted. In this study we derive anatomical principles for divergent innervation of pyramidal neurons of the same type within the local microcircuit. We also review data providing circumstantial and direct evidence for differential synaptic transmission via the same axon from neocortical pyramidal neurons and derive some principles for differential synaptic innervation of pyramidal neurons of the same type, of pyramidal neurons and interneurons and of different types of interneurons. We conclude that differential anatomical and physiological differentiation is a fundamental property of glutamatergic axons of pyramidal neurons in the neocortex.

Changing the reliability of neurotransmitter release results in a change in the efficacy of low frequency synaptic transmission and in the rate of high frequency synaptic depression thus it can not cause an uniform change in strength of synapses and instead results in a change in the dynamics of synaptic transmission referred to as 'redistribution of synaptic efficacy' (RSE). Since the change in synaptic transmission associated with RSE depends on the history of action potential activity it is concluded that RSE serves as a mechanism to generate a potentially infinite diversity of synaptic input.

Tufted layer 5 (TL5) pyramidal neurons are important projection neurons from the cerebral cortex to subcortical areas. Recent and ongoing experiments aimed at understanding the computational analysis performed by a network of synaptically connected TL5 neurons are reviewed here. The experiments employed dual and triple whole-cell patch clamp recordings from visually identified and preselected neurons in brain slices of somatosensory cortex of young (14- to 16-day-old) rats. These studies suggest that a local network of TL5 neurons within a cortical module of diameter 300 microns consists of a few hundred neurons that are extensively inter-connected with reciprocal feedback from at least first-, second- and third-order target neurons. A statistical analysis of synaptic innervation suggests that this recurrent network is not randomly arranged and hence each neuron could be functionally unique. Synaptic transmission between these neurons is characterized by use-dependent synaptic depression which confers novel properties to this recurrent network of neurons. First, a range of rates of depression for different synaptic connections enable each TL5 neuron to receive a unique mixture of information about the average firing rates and the temporally correlated action potential (AP) activity in the population of presynaptic TL5 neurons. Second, each AP generated by any neuron in the network induces a change (defined as an iteration step) in the functional coupling of the neurons in the network (defined as network configuration). It is proposed that the network configuration is iterated during a stimulus to achieve an optimally orchestrated network response. Hebbian, anti-Hebbian and neuromodulatory-induced modifications of neurotransmitter release probability change the rates of synaptic depression and thereby alter the iteration step size. These data may be important to understand the dynamics of electrical activity within the network.

A single intra-peritoneal injection of valproic acid (VPA) on embryonic day (ED) 11.5 to pregnant rats has been shown to produce severe autistic-like symptoms in the offspring. Previous studies showed that the microcircuitry is hyperreactive due to hyperconnectivity of glutamatergic synapses and hyperplastic due to over-expression of NMDA receptors. These changes were restricted to the dimensions of a minicolumn (<50 μm). In the present study, we explored whether Long Term Microcircuit Plasticity (LTMP) was altered in this animal model. We performed multineuron patch-clamp recordings on clusters of layer 5 pyramidal cells in somatosensory cortex brain slices (PN 12-15), mapped the connectivity and characterized the synaptic properties for connected neurons. Pipettes were then withdrawn and the slice was perfused with 100 μM sodium glutamate in artificial cerebrospinal fluid in the recording chamber for 12 h. When we repatched the same cluster of neurons, we found enhanced LTMP only at inter-somatic distances beyond minicolumnar dimensions. These data suggest that hyperconnectivity is already near its peak within the dimensions of the minicolumn in the treated animals and that LTMP, which is normally restricted to within a minicolumn, spills over to drive hyperconnectivity across the dimensions of a minicolumn. This study provides further evidence to support the notion that the neocortex is highly plastic in response to new experiences in this animal model of autism.

Temporal integration of information and prediction of future sensory inputs are assumed to be important computational tasks of generic cortical microcircuits. It has remained open how cortical microcircuits could possibly achieve this, especially since they consist--in contrast to most neural network models--of neurons and synapses with heterogeneous dynamic responses. It turns out, however, that the diversity of computational units increases the capability of microcircuit models for temporal integration. Furthermore the prediction of future input may be rather easy for such circuits since it suffices to train the readouts from such microcircuits. In this article we show that very simple readouts from a generic recurrently connected circuit of integrate-and-fire neurons with diverse dynamic synapses can be trained in an unsupervised manner to predict movements of different objects, that move within an unlimited number of combinations of speed, angle, and offset over a simulated sensory field. The autonomously trained microcircuit model is also able to compute the direction of motion, which is a computationally difficult problem ('aperture problem') since it requires disambiguation of local sensory readings through the context of other sensory readings at the current and preceding moments. Furthermore the same circuit can be trained simultaneously in a supervised manner to also report the shape and velocity of the moving object. Finally it is shown that the trained neural circuit supports novelty detection and the generation of 'imagined movements'. Altogether the results of this article suggest that it is not necessary to construct specific and biologically unrealistic neural circuit models for specific sensory processing tasks, since 'found' generic cortical microcircuit models in combination with very simple perceptron-like readouts can easily be trained to solve such computational tasks

IBM's Blue Gene supercomputer allows a quantum leap in the level of detail at which the brain can be modelled. I argue that the time is right to begin assimilating the wealth of data that has been accumulated over the past century and start building biologically accurate models of the brain from first principles to aid our understanding of brain function and dysfunction

To understand the interface between global brain function and molecular neuroscience--that is, the microcircuit level--a major challenge. Such understanding is prerequisite if we are to account for neural function in cellular terms. Very few vertebrate microcircuits are yet understood because their analysis is demanding technically. In this review of the TINS Microcircuits Special Feature, we attempt to shed light on the problem by comparing the operation of four types of microcircuit, to identify common molecular and cellular components. Central pattern generator (CPG) networks underlying rhythmic movements and hippocampal microcircuits that generate gamma and theta rhythms are compared with the neocortical microcircuits used in cognitive tasks and a cerebellar network. The long-term goal is to identify the components of a molecular and synaptic tool kit for the design of different microcircuits

The functions performed by different neural microcircuits depend on the anatomical and physiological properties of the various synaptic pathways connecting neurons. Neural microcircuits across various species and brain regions are similar in terms of their repertoire of neurotransmitters, their synaptic kinetics, their short-term and long-term plasticity, and the target-specificity of their synaptic connections. However, microcircuits can be fundamentally different in terms of the precise recurrent design used to achieve a specific functionality. In this review, which is part of the TINS Microcircuits Special Feature, we compare the connectivity designs in spinal, hippocampal, neocortical and cerebellar microcircuits, and discuss the different computational challenges that each microcircuit faces

Structural and functional diversity of GABAergic interneurons has become increasingly central in our understanding of the elemental steps of information processing in the brain. The use of different molecular, electrophysiological and anatomical techniques has provided a wealth of new information regarding GABAergic interneurons over the past decade but it has also led to confusion regarding the number of subtypes of GABAergic interneurons. Combinatorial approaches that also consider multiple parameters seem now to offer renewed hope for finally clarifying the structural diversity of GABAergic interneurons. New molecular techniques have become a powerful tool for exposing the functional diversity of GABAergic neurons at the cellular, microcircuit and systems levels. This article reviews literature regarding molecular tools that have been used, or that appear promising for future attempts, to classify GABAergic interneurons. Some important limitations will also be indicated

Mammals adapt to a rapidly changing world because of the sophisticated cognitive functions that are supported by the neocortex. The neocortex, which forms almost 80% of the human brain, seems to have arisen from repeated duplication of a stereotypical microcircuit template with subtle specializations for different brain regions and species. The quest to unravel the blueprint of this template started more than a century ago and has revealed an immensely intricate design. The largest obstacle is the daunting variety of inhibitory interneurons that are found in the circuit. This review focuses on the organizing principles that govern the diversity of inhibitory interneurons and their circuits

A central debate regarding neocortical function concerns the degree to which the underlying microcircuitry is stereotypically organized. Stereotypy reflects invariance in structure and function, as a result of common genetic templates and environmental conditions, whereas uniqueness can be caused by genetic variations, differences in environmental conditions as well as random processes. Stereotypy is an appealing concept because it provides strong support for determinism in the formation of neuronal microcircuits and in the relationship between their specific structure and function