Sparse balance: Excitatory-inhibitory networks with small bias currents and broadly distributed synaptic weights

Cortical circuits generate excitatory currents that must be cancelled by strong inhibition to assure stability. The resulting excitatory-inhibitory (E-I) balance can generate spontaneous irregular activity but, in standard balanced E-I models, this requires that an extremely strong feedforward bias current be included along with the recurrent excitation and inhibition. The absence of experimental evidence for such large bias currents inspired us to examine an alternative regime that exhibits asynchronous activity without requiring unrealistically large feedforward input. In these networks, irregular spontaneous activity is supported by a continually changing sparse set of neurons. To support this activity, synaptic strengths must be drawn from high-variance distributions. Unlike standard balanced networks, these sparse balance networks exhibit robust nonlinear responses to uniform inputs and non-Gaussian input statistics. Interestingly, the speed, not the size, of synaptic fluctuations dictates the degree of sparsity in the model. In addition to simulations, we provide a mean-field analysis to illustrate the properties of these networks.

A class of models in computational neuroscience that have been successful at describing a variety of effects in the neocortex involve a tight balance between excitatory, inhibitory and unrealistically large external input, without which the model cannot produce robust patterns of activity. In this work, we explore what happens when these inputs are smaller in size, and we provide an alternative solution for recovering robust network activity. This solution relies on broadly distributed synaptic strengths and, interestingly, gives rise to sparse subsets of neurons firing at any given time. Unlike the conventional models, the networks exhibit nonlinear responses to uniform external input.

Neural Trajectories in the Supplementary Motor Area and Motor Cortex Exhibit Distinct Geometries, Compatible with Different Classes of Computation

The supplementary motor area (SMA) is believed to contribute to higher order aspects of motor control. We considered a key higher order role: tracking progress throughout an action. We propose that doing so requires population activity to display low "trajectory divergence": situations with different future motor outputs should be distinct, even when present motor output is identical. We examined neural activity in SMA and primary motor cortex (M1) as monkeys cycled various distances through a virtual environment. SMA exhibited multiple response features that were absent in M1. At the single-neuron level, these included ramping firing rates and cycle-specific responses. At the population level, they included a helical population-trajectory geometry with shifts in the occupied subspace as movement unfolded. These diverse features all served to reduce trajectory divergence, which was much lower in SMA versus M1. Analogous population-trajectory geometry, also with low divergence, naturally arose in networks trained to internally guide multi-cycle movement.

Preventing Thin Film Dewetting via Graphene Capping

A monolayer 2D capping layer with high Young's modulus is shown to be able to effectively suppress the dewetting of underlying thin films of small organic semiconductor molecule, polymer, and polycrystalline metal, respectively. To verify the universality of this capping layer approach, the dewetting experiments are performed for single-layer graphene transferred onto polystyrene (PS), semiconducting thienoazacoronene (EH-TAC), gold, and also MoS₂ on PS. Thermodynamic modeling indicates that the exceptionally high Young's modulus and surface conformity of 2D capping layers such as graphene and MoS₂ substantially suppress surface fluctuations and thus dewetting. As long as the uncovered area is smaller than the fluctuation wavelength of the thin film in a dewetting process via spinodal decomposition, the dewetting should be suppressed. The 2D monolayer-capping approach opens up exciting new possibilities to enhance the thermal stability and expands the processing parameters for thin film materials without significantly altering their physical properties.