Physiological sharp wave-ripples and interictal events in vitro: What’s the difference?

Sharp wave-ripples and interictal events are physiological and pathological forms of transient high activity in the hippocampus with similar features. Sharp wave-ripples have been shown to be essential in memory consolidation, while epileptiform (interictal) events are thought to be damaging. It is essential to grasp the difference between physiological sharp wave-ripples and pathological interictal events in order to understand the failure of control mechanisms in the latter case. We investigated the dynamics of activity generated intrinsically in the CA3 region of the mouse hippocampus in vitro, using four different types of intervention to induce epiletiform activity. As a result, sharp wave-ripples spontaneously occurring in CA3 disappeared, and following an asynchronous transitory phase, activity reorganized into a new form of pathological synchrony. During epileptiform events, all neurons increased their firing rate compared to sharp wave-ripples. Different cell types showed complementary firing: parvalbumin-positive basket cells and some axo-axonic cells stopped firing due to a depolarization block at the climax of the events in high potassium, 4-aminopyridine and zero magnesium models, but not in the gabazine model. In contrast, pyramidal cells started firing maximally at this stage. To understand the underlying mechanism we measured changes of intrinsic neuronal and transmission parameters in the high potassium model. We found that the cellular excitability increased and excitatory transmission was enhanced, whereas inhibitory transmission was compromised. We observed a strong short-term depression in parvalbumin-positive basket cell to pyramidal cell transmission. Thus, the collapse of pyramidal cell perisomatic inhibition appears to be a crucial factor in the emergence of epileptiform events

Comparison Between Supervised and Unsupervised Classifications of Neuronal Cell Types: A Case Study

In the study of neural circuits, it becomes essential to discern the different neuronal cell types that build the circuit. Traditionally, neuronal cell types have been classified using qualitative descriptors. More recently, several attempts have been made to classify neurons quantitatively, using unsupervised clustering methods. While useful, these algorithms do not take advantage of previous information known to the investigator, which could improve the classification task. For neocortical GABAergic interneurons, the problem to discern among different cell types is particularly difficult and better methods are needed to perform objective classifications. Here we explore the use of supervised classification algorithms to classify neurons based on their morphological features, using a database of 128 pyramidal cells and 199 interneurons from mouse neocortex. To evaluate the performance of different algorithms we used, as a “benchmark,” the test to automatically distinguish between pyramidal cells and interneurons, defining “ground truth” by the presence or absence of an apical dendrite. We compared hierarchical clustering with a battery of different supervised classification algorithms, finding that supervised classifications outperformed hierarchical clustering. In addition, the selection of subsets of distinguishing features enhanced the classification accuracy for both sets of algorithms. The analysis of selected variables indicates that dendritic features were most useful to distinguish pyramidal cells from interneurons when compared with somatic and axonal morphological variables. We conclude that supervised classification algorithms are better matched to the general problem of distinguishing neuronal cell types when some information on these cell groups, in our case being pyramidal or interneuron, is known a priori. As a spin-off of this methodological study, we provide several methods to automatically distinguish neocortical pyramidal cells from interneurons, based on their morphologies

In vivo characterization of hippocampal electrophysiological processes in the heterozygous Pten knockout model of autism

While cognitive deficits have been described in the heterozygous Pten (+/-) KO mouse model of autism, little work has been done to demonstrate how corresponding in vitro physiological alterations in this model may underpin these cognitive deficits in vivo. As Pten KO (+/-) is known to alter electrophysiological characteristics of neurons in vitro, this study measures the in vivo electrophysiological characteristics of CA1 interneurons, pyramidal cells, and place cells which may underlie the spatial cognitive deficits seen in the model. Four transgenic conditional heterozygous Pten+/loxPloxP;Gfap-cre mice (HetPten) and four homozygous Pten littermate control mice were used in this study. This transgene drives cre expression and excision of the Pten gene in hippocampal granule cells of the dentate gyrus, and neurons in CA2 and CA1, but not astrocytes. In vivo local field potentials and single cell recordings were made in CA1 of each mouse during an open field foraging task in two distinct arenas. HetPten mice were found to have increased interneuron and pyramidal cell firing rates. In addition, place cells demonstrated abnormal properties including increased out-of-field firing rates, an increased number of fields, and trends towards larger field sizes that were less stable in comparison to controls. HetPten mice had slower CA1 fast gamma oscillations and more variable speed/theta oscillation correlations. Behaviorally, there were weak trends towards decreased motor output compared to controls. These data suggest that the electrophysiological alterations due to Pten KO (+/-) in mouse hippocampal neurons lead to hyperactivation of CA1 interneurons, pyramidal cells, and place cells

Neuromodulatory control of localized dendritic spiking in critical period cortex.

Sensory experience in early postnatal life, during so-called critical periods, restructures neural circuitry to enhance information processing1. Why the cortex is susceptible to sensory instruction in early life and why this susceptibility wanes with age are unclear. Here we define a developmentally restricted engagement of inhibitory circuitry that shapes localized dendritic activity and is needed for vision to drive the emergence of binocular visual responses in the mouse primary visual cortex. We find that at the peak of the critical period for binocular plasticity, acetylcholine released from the basal forebrain during periods of heightened arousal directly excites somatostatin (SST)-expressing interneurons. Their inhibition of pyramidal cell dendrites and of fast-spiking, parvalbumin-expressing interneurons enhances branch-specific dendritic responses and somatic spike rates within pyramidal cells. By adulthood, this cholinergic sensitivity is lost, and compartmentalized dendritic responses are absent but can be re-instated by optogenetic activation of SST cells. Conversely, suppressing SST cell activity during the critical period prevents the normal development of binocular receptive fields by impairing the maturation of ipsilateral eye inputs. This transient cholinergic modulation of SST cells, therefore, seems to orchestrate two features of neural plasticity-somatic disinhibition and compartmentalized dendritic spiking. Loss of this modulation may contribute to critical period closure

Generation of an intense cold-atom beam from a pyramidal magneto-optical trap: experiment and simulation

An intense cold-atom beam source based on a modified pyramidal magneto-optical trap has been developed and characterized. We have produced a slow beam of cold cesium atoms with a continuous flux of 2.2× 10^9 atoms/s at a mean velocity of 15 m/s and with a divergence of 15 mrad. The corresponding radiant intensity is 1.2×10^13 atom s^−1 sr^−1. We have characterized the performance of our beam source over a range of operating conditions, and the measured values for atom flux, mean velocity, and divergence are in good agreement with results from detailed Monte Carlo numerical simulations

Why Neurons Have Thousands of Synapses, A Theory of Sequence Memory in Neocortex

Neocortical neurons have thousands of excitatory synapses. It is a mystery how neurons integrate the input from so many synapses and what kind of large-scale network behavior this enables. It has been previously proposed that non-linear properties of dendrites enable neurons to recognize multiple patterns. In this paper we extend this idea by showing that a neuron with several thousand synapses arranged along active dendrites can learn to accurately and robustly recognize hundreds of unique patterns of cellular activity, even in the presence of large amounts of noise and pattern variation. We then propose a neuron model where some of the patterns recognized by a neuron lead to action potentials and define the classic receptive field of the neuron, whereas the majority of the patterns recognized by a neuron act as predictions by slightly depolarizing the neuron without immediately generating an action potential. We then present a network model based on neurons with these properties and show that the network learns a robust model of time-based sequences. Given the similarity of excitatory neurons throughout the neocortex and the importance of sequence memory in inference and behavior, we propose that this form of sequence memory is a universal property of neocortical tissue. We further propose that cellular layers in the neocortex implement variations of the same sequence memory algorithm to achieve different aspects of inference and behavior. The neuron and network models we introduce are robust over a wide range of parameters as long as the network uses a sparse distributed code of cellular activations. The sequence capacity of the network scales linearly with the number of synapses on each neuron. Thus neurons need thousands of synapses to learn the many temporal patterns in sensory stimuli and motor sequences.Comment: Submitted for publicatio