Gap junctions: The missing piece of the connectome.

The central pattern generator that controls flying power in Drosophila requires desynchronized firing to drive a steady wingbeat frequency. A new study reveals how gap junctions are the key to desynchronizing the motor neurons.

Connectomic analysis of the lateral neuron clock cells reveals the synaptic basis of functional pacemaker classes.

The circadian clock orchestrates daily changes in physiology and behavior to ensure internal temporal order and optimal timing across the day. In animals, a central brain clock coordinates circadian rhythms throughout the body and is characterized by a remarkable robustness that depends on synaptic connections between constituent neurons. The clock neuron network of , which shares network motifs with clock networks in the mammalian brain yet is built of many fewer neurons, offers a powerful model for understanding the network properties of circadian timekeeping.

Nonlinear convergence boosts information coding in circuits with parallel outputs.

Neural circuits are structured with layers of converging and diverging connectivity and selectivity-inducing nonlinearities at neurons and synapses. These components have the potential to hamper an accurate encoding of the circuit inputs. Past computational studies have optimized the nonlinearities of single neurons, or connection weights in networks, to maximize encoded information, but have not grappled with the simultaneous impact of convergent circuit structure and nonlinear response functions for efficient coding.

Population adaptation in efficient balanced networks.

Adaptation is a key component of efficient coding in sensory neurons. However, it remains unclear how neurons can provide a stable representation of external stimuli given their history-dependent responses. Here we show that a stable representation is maintained if efficiency is optimized by a population of neurons rather than by neurons individually.

Complicating connectomes: Electrical coupling creates parallel pathways and degenerate circuit mechanisms.

Electrical coupling in circuits can produce non-intuitive circuit dynamics, as seen in both experimental work from the crustacean stomatogastric ganglion and in computational models inspired by the connectivity in this preparation. Ambiguities in interpreting the results of electrophysiological recordings can arise if sets of pre- or postsynaptic neurons are electrically coupled, or if the electrical coupling exhibits some specificity (e.g.

Modulation of a Single Neuron Has State-Dependent Actions on Circuit Dynamics

When does neuromodulation of a single neuron influence the output of the entire network? We constructed a five-cell circuit in which a neuron is at the center of the circuit and the remaining neurons form two distinct oscillatory subnetworks. All neurons were modeled as modified Morris-Lecar models with a hyperpolarization-activated conductance ( ) in addition to calcium ( ), potassium ( ), and leak conductances. We determined the effects of varying , , and on the frequency, amplitude, and duty cycle of a single neuron oscillator.

Rectifying electrical synapses can affect the influence of synaptic modulation on output pattern robustness.

Rectifying electrical synapses are commonplace, but surprisingly little is known about how rectification alters the dynamics of neuronal networks. In this study, we use computational models to investigate how rectifying electrical synapses change the behavior of a small neuronal network that exhibits complex rhythmic output patterns. We begin with an electrically coupled circuit of three oscillatory neurons with different starting frequencies, and subsequently add two additional neurons and inhibitory chemical synapses.

Successful reconstruction of a physiological circuit with known connectivity from spiking activity alone.

Identifying the structure and dynamics of synaptic interactions between neurons is the first step to understanding neural network dynamics. The presence of synaptic connections is traditionally inferred through the use of targeted stimulation and paired recordings or by post-hoc histology. More recently, causal network inference algorithms have been proposed to deduce connectivity directly from electrophysiological signals, such as extracellularly recorded spiking activity.

Multiple mechanisms switch an electrically coupled, synaptically inhibited neuron between competing rhythmic oscillators.

Rhythmic oscillations are common features of nervous systems. One of the fundamental questions posed by these rhythms is how individual neurons or groups of neurons are recruited into different network oscillations. We modeled competing fast and slow oscillators connected to a hub neuron with electrical and inhibitory synapses.

Functional connectivity in a rhythmic inhibitory circuit using Granger causality.

Background: Understanding circuit function would be greatly facilitated by methods that allow the simultaneous estimation of the functional strengths of all of the synapses in the network during ongoing network activity. Towards that end, we used Granger causality analysis on electrical recordings from the pyloric network of the crab Cancer borealis, a small rhythmic circuit with known connectivity, and known neuronal intrinsic properties.

Results: Granger causality analysis reported a causal relationship where there is no anatomical correlate because of the strong oscillatory behavior of the pyloric circuit.

Cancer borealis stomatogastric nervous system dissection.

The stomatogastric ganglion (STG) is an excellent model for studying cellular and network interactions because it contains a relatively small number of cells (approximately 25 in C. borealis) which are well characterized. The cells in the STG exhibit a broad range of outputs and are responsible for the motor actions of the stomach.