Mutual Inhibition with Few Inhibitory Cells via Nonlinear Inhibitory Synaptic Interaction.

In computational neural network models, neurons are usually allowed to excite some and inhibit other neurons, depending on the weight of their synaptic connections. The traditional way to transform such networks into networks that obey Dale's law (i.e., a neuron can either excite or inhibit) is to accompany each excitatory neuron with an inhibitory one through which inhibitory signals are mediated. However, this requires an equal number of excitatory and inhibitory neurons, whereas a realistic number of inhibitory neurons is much smaller. In this letter, we propose a model of nonlinear interaction of inhibitory synapses on dendritic compartments of excitatory neurons that allows the excitatory neurons to mediate inhibitory signals through a subset of the inhibitory population. With this construction, the number of required inhibitory neurons can be reduced tremendously.

On the origin of lognormal network synchrony in CA1.

The sharp wave ripple complex in rodent hippocampus is associated with a network burst in CA3 (NB) that triggers a synchronous event in the CA1 population (SE). The number of CA1 pyramidal cells participating in a SE has been observed to follow a lognormal distribution. However, the origin of this skewed and heavy-tailed distribution of population synchrony in CA1 remains unknown. Because the size of SEs is likely to originate from the size of the NBs and the underlying neural circuitry, we model the CA3-CA1 circuit to study the underlying mechanisms and their functional implications. We show analytically that if the size of a NB in CA3 is distributed according to a normal distribution, then the size of the resulting SE in CA1 follows a lognormal distribution. Our model predicts the distribution of the NB size in CA3, which remains to be tested experimentally. Moreover, we show that a putative lognormal NB size distribution leads to an extremely heavy-tailed SE size distribution in CA1, contradicting experimental evidence. In conclusion, our model provides general insight on the origin of lognormally distributed network synchrony as a consequence of synchronous synaptic transmission of normally distributed input events.

Voltage dependence of synaptic plasticity is essential for rate based learning with short stimuli.

In computational neuroscience, synaptic plasticity rules are often formulated in terms of firing rates. The predominant description of in vivo neuronal activity, however, is the instantaneous rate (or spiking probability). In this article we resolve this discrepancy by showing that fluctuations of the membrane potential carry enough information to permit a precise estimate of the instantaneous rate in balanced networks. As a consequence, we find that rate based plasticity rules are not restricted to neuronal activity that is stable for hundreds of milliseconds to seconds, but can be carried over to situations in which it changes every few milliseconds. We illustrate this, by showing that a voltage-dependent realization of the classical BCM rule achieves input selectivity, even if stimulus duration is reduced to a few milliseconds each.

A Hippocampal Model for Behavioral Time Acquisition and Fast Bidirectional Replay of Spatio-Temporal Memory Sequences.

The hippocampus is known to play a crucial role in the formation of long-term memory. For this, fast replays of previously experienced activities during sleep or after reward experiences are believed to be crucial. But how such replays are generated is still completely unclear. In this paper we propose a possible mechanism for this: we present a model that can store experienced trajectories on a behavioral timescale after a single run, and can subsequently bidirectionally replay such trajectories, thereby omitting any specifics of the previous behavior like speed, etc, but allowing repetitions of events, even with different subsequent events. Our solution builds on well-known concepts, one-shot learning and synfire chains, enhancing them by additional mechanisms using global inhibition and disinhibition. For replays our approach relies on dendritic spikes and cholinergic modulation, as supported by experimental data. We also hypothesize a functional role of disinhibition as a pacemaker during behavioral time.

Long Synfire Chains Emerge by Spike-Timing Dependent Plasticity Modulated by Population Activity.

Sequences of precisely timed neuronal activity are observed in many brain areas in various species. Synfire chains are a well-established model that can explain such sequences. However, it is unknown under which conditions synfire chains can develop in initially unstructured networks by self-organization. This work shows that with spike-timing dependent plasticity (STDP), modulated by global population activity, long synfire chains emerge in sparse random networks. The learning rule fosters neurons to participate multiple times in the chain or in multiple chains. Such reuse of neurons has been experimentally observed and is necessary for high capacity. Sparse networks prevent the chains from being short and cyclic and show that the formation of specific synapses is not essential for chain formation. Analysis of the learning rule in a simple network of binary threshold neurons reveals the asymptotically optimal length of the emerging chains. The theoretical results generalize to simulated networks of conductance-based leaky integrate-and-fire (LIF) neurons. As an application of the emerged chain, we propose a one-shot memory for sequences of precisely timed neuronal activity.

Interactions of Isonitriles with Metal-Boron Bonds: Insertions, Coupling, Ring Formation, and Liberation of Monovalent Boron.

Boryl, borylene, and base-stabilized borylene complexes of manganese and iron undergo a range of different reactions when treated with isonitriles including single, double, and partial isonitrile insertions into metal-boron bonds, ring formation, isonitrile coupling, and the liberation of new monovalent boron species. Two of the resulting cyclic species have also been found to react selectively with anhydrous HCl to form ring-opened products. The diverse isonitrile-promoted reactivity of transition-metal-boron compounds has been explored computationally.

Boron-Metallated Borirenes and Bis(Borirenes).

Heating the metalloborylene complex [{(η(5) -C5 Me5 )Fe(CO)2 }(µ-B){Cr(CO)5 }] with alkynes and diynes leads to the formation of B-metallated borirenes and a bis(B-metallated borirene) through formal transfer of the metalloborylene moiety [(η(5) -C5 Me5 )(OC)2 Fe(B:)]. By using this protocol, a range of B-metallated borirenes with electron-donating and electron-withdrawing substituents are prepared, and these are studied spectroscopically, structurally, and computationally. The yellow-orange color of the complexes is additionally explained through time-dependent density functional theory calculations.

Fundamental Differences between Group 8 Metals: Unexpected Oxidation State Preferences and Mechanisms in Ruthenium Borylene Complex Formation.

The reaction of the salts K[Ru(CO)3 (PMe3 )(SiR3 )] (R=Me, Et) with Br2 BDur or Cl2 BDur (Dur=2,3,5,6-Me4 C6 H) leads to both boryl and borylene complexes of divalent ruthenium, the former through simple salt elimination and the latter through subsequent CO loss and 1,2-halide shift. The balance of products can be altered by varying the reaction conditions; boryl complexes can be favored by the addition of CO, and borylene complexes by removal of CO under vacuum. All of these products are in competition with the corresponding (aryl)(halo)(trialkylsilyl)borane, a reductive elimination product. The Ru(II) borylene products and the mechanisms that form them are distinctly different from the analogous reactions with iron, which lead to low-valent borylene complexes, highlighting fundamental differences in oxidation state preferences between iron and ruthenium.