Cholinergic regulation of dendritic Ca spikes controls firing mode of hippocampal CA3 pyramidal neurons.

Active dendritic integrative mechanisms such as regenerative dendritic spikes enrich the information processing abilities of neurons and fundamentally contribute to behaviorally relevant computations. Dendritic Ca spikes are generally thought to produce plateau-like dendritic depolarization and somatic complex spike burst (CSB) firing, which can initiate rapid changes in spatial coding properties of hippocampal pyramidal cells (PCs). However, here we reveal that a morpho-topographically distinguishable subpopulation of rat and mouse hippocampal CA3PCs exhibits compound apical dendritic Ca spikes with unusually short duration that do not support the firing of sustained CSBs.

Parallel functional architectures within a single dendritic tree.

The input-output transformation of individual neurons is a key building block of neural circuit dynamics. While previous models of this transformation vary widely in their complexity, they all describe the underlying functional architecture as unitary, such that each synaptic input makes a single contribution to the neuronal response. Here, we show that the input-output transformation of CA1 pyramidal cells is instead best captured by two distinct functional architectures operating in parallel.

Sampling motion trajectories during hippocampal theta sequences.

Efficient planning in complex environments requires that uncertainty associated with current inferences and possible consequences of forthcoming actions is represented. Representation of uncertainty has been established in sensory systems during simple perceptual decision making tasks but it remains unclear if complex cognitive computations such as planning and navigation are also supported by probabilistic neural representations. Here, we capitalized on gradually changing uncertainty along planned motion trajectories during hippocampal theta sequences to capture signatures of uncertainty representation in population responses.

Impact of functional synapse clusters on neuronal response selectivity.

Clustering of functionally similar synapses in dendrites is thought to affect neuronal input-output transformation by triggering local nonlinearities. However, neither the in vivo impact of synaptic clusters on somatic membrane potential (sVm), nor the rules of cluster formation are elucidated. We develop a computational approach to measure the effect of functional synaptic clusters on sVm response of biophysical model CA1 and L2/3 pyramidal neurons to in vivo-like inputs.

Synaptic Plasticity Depends on the Fine-Scale Input Pattern in Thin Dendrites of CA1 Pyramidal Neurons.

Coordinated long-term plasticity of nearby excitatory synaptic inputs has been proposed to shape experience-related neuronal information processing. To elucidate the induction rules leading to spatially structured forms of synaptic potentiation in dendrites, we explored plasticity of glutamate uncaging-evoked excitatory input patterns with various spatial distributions in perisomatic dendrites of CA1 pyramidal neurons in slices from adult male rats. We show that (1) the cooperativity rules governing the induction of synaptic LTP depend on dendritic location; (2) LTP of input patterns that are subthreshold or suprathreshold to evoke local dendritic spikes (d-spikes) requires different spatial organization; and (3) input patterns evoking d-spikes can strengthen nearby, nonsynchronous synapses by local heterosynaptic plasticity crosstalk mediated by NMDAR-dependent MEK/ERK signaling.

Global and Multiplexed Dendritic Computations under In Vivo-like Conditions.

Dendrites integrate inputs nonlinearly, but it is unclear how these nonlinearities contribute to the overall input-output transformation of single neurons. We developed statistically principled methods using a hierarchical cascade of linear-nonlinear subunits (hLN) to model the dynamically evolving somatic response of neurons receiving complex, in vivo-like spatiotemporal synaptic input patterns. We used the hLN to predict the somatic membrane potential of an in vivo-validated detailed biophysical model of a L2/3 pyramidal cell.

Robust and efficient coding with grid cells.

The neuronal code arising from the coordinated activity of grid cells in the rodent entorhinal cortex can uniquely represent space across a large range of distances, but the precise conditions for optimal coding capacity are known only for environments with finite size. Here we consider a coding scheme that is suitable for unbounded environments, and present a novel, number theoretic approach to derive the grid parameters that maximise the coding range in the presence of noise. We derive an analytic upper bound on the coding range and provide examples for grid scales that achieve this bound and hence are optimal for encoding in unbounded environments.

First principles based proximity effect of superconductor-normal metal heterostructures.

In this paper we study the proximity effect in superconductor-normal metal heterostructures based on first principles calculations with treating the pairing potential as an adjustable parameter. The superconducting order parameter (anomalous density) is obtained from the Green-function by solving the Kohn-Sham-Bogoliubov-de Gennes equations with the Screened Korringa-Kohn-Rostoker method. The results are interpreted for an Au/Nb(0 0 1) system.

Location-dependent synaptic plasticity rules by dendritic spine cooperativity.

Nonlinear interactions between coactive synapses enable neurons to discriminate between spatiotemporal patterns of inputs. Using patterned postsynaptic stimulation by two-photon glutamate uncaging, here we investigate the sensitivity of synaptic Ca(2+) signalling and long-term plasticity in individual spines to coincident activity of nearby synapses. We find a proximodistally increasing gradient of nonlinear NMDA receptor (NMDAR)-mediated amplification of spine Ca(2+) signals by a few neighbouring coactive synapses along individual perisomatic dendrites.

Dendritic nonlinearities are tuned for efficient spike-based computations in cortical circuits.

Cortical neurons integrate thousands of synaptic inputs in their dendrites in highly nonlinear ways. It is unknown how these dendritic nonlinearities in individual cells contribute to computations at the level of neural circuits. Here, we show that dendritic nonlinearities are critical for the efficient integration of synaptic inputs in circuits performing analog computations with spiking neurons.

Parallel computational subunits in dentate granule cells generate multiple place fields.

A fundamental question in understanding neuronal computations is how dendritic events influence the output of the neuron. Different forms of integration of neighbouring and distributed synaptic inputs, isolated dendritic spikes and local regulation of synaptic efficacy suggest that individual dendritic branches may function as independent computational subunits. In the present paper, we study how these local computations influence the output of the neuron.

Robust path integration in the entorhinal grid cell system with hippocampal feed-back.

Animals are able to update their knowledge about their current position solely by integrating the speed and the direction of their movement, which is known as path integration. Recent discoveries suggest that grid cells in the medial entorhinal cortex might perform some of the essential underlying computations of path integration. However, a major concern over path integration is that as the measurement of speed and direction is inaccurate, the representation of the position will become increasingly unreliable.

Impaired associative learning in schizophrenia: behavioral and computational studies.

Associative learning is a central building block of human cognition and in large part depends on mechanisms of synaptic plasticity, memory capacity and fronto-hippocampal interactions. A disorder like schizophrenia is thought to be characterized by altered plasticity, and impaired frontal and hippocampal function. Understanding the expression of this dysfunction through appropriate experimental studies, and understanding the processes that may give rise to impaired behavior through biologically plausible computational models will help clarify the nature of these deficits.

Pharmacological and computational analysis of alpha-subunit preferential GABA(A) positive allosteric modulators on the rat septo-hippocampal activity.

Clinically most active anxiolytic drugs are positive allosteric modulators (PAMs) of GABA(A) receptors, represented by benzodiazepine compounds. Due to their non-selective profile, however, they potently modulate several sup-type specific GABA(A) receptors, contributing to their broad-range side effects. Based on observations in genetically altered mice, however, it has been proposed that anxiolytic action of benzodiazepines is predominantly mediated by GABA(A) alpha2/3 subunit-containing receptors.

How do glutamatergic and GABAergic cells contribute to synchronization in the medial septum?

The medial septum-diagonal band (MSDB) complex is considered as a pacemaker for the hippocampal theta rhythm. Identification of the different cell types, their electro-physiological properties and their possible function in the generation of a synchronized activity in the MSDB is a hot topic. A recent electro-physiological study showed the presence of two antiphasically firing populations of parvalbumin containing GABAergic neurons in the MSDB.