Emergent neural dynamics and geometry for generalization in a transitive inference task.

Relational cognition-the ability to infer relationships that generalize to novel combinations of objects-is fundamental to human and animal intelligence. Despite this importance, it remains unclear how relational cognition is implemented in the brain due in part to a lack of hypotheses and predictions at the levels of collective neural activity and behavior. Here we discovered, analyzed, and experimentally tested neural networks (NNs) that perform transitive inference (TI), a classic relational task (if A > B and B > C, then A > C). We found NNs that (i) generalized perfectly, despite lacking overt transitive structure prior to training, (ii) generalized when the task required working memory (WM), a capacity thought to be essential to inference in the brain, (iii) emergently expressed behaviors long observed in living subjects, in addition to a novel order-dependent behavior, and (iv) expressed different task solutions yielding alternative behavioral and neural predictions. Further, in a large-scale experiment, we found that human subjects performing WM-based TI showed behavior inconsistent with a class of NNs that characteristically expressed an intuitive task solution. These findings provide neural insights into a classical relational ability, with wider implications for how the brain realizes relational cognition.

Input correlations impede suppression of chaos and learning in balanced firing-rate networks.

Neural circuits exhibit complex activity patterns, both spontaneously and evoked by external stimuli. Information encoding and learning in neural circuits depend on how well time-varying stimuli can control spontaneous network activity. We show that in firing-rate networks in the balanced state, external control of recurrent dynamics, i.e., the suppression of internally-generated chaotic variability, strongly depends on correlations in the input. A distinctive feature of balanced networks is that, because common external input is dynamically canceled by recurrent feedback, it is far more difficult to suppress chaos with common input into each neuron than through independent input. To study this phenomenon, we develop a non-stationary dynamic mean-field theory for driven networks. The theory explains how the activity statistics and the largest Lyapunov exponent depend on the frequency and amplitude of the input, recurrent coupling strength, and network size, for both common and independent input. We further show that uncorrelated inputs facilitate learning in balanced networks.

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.

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.

Tuning charge and correlation effects for a single molecule on a graphene device.

The ability to understand and control the electronic properties of individual molecules in a device environment is crucial for developing future technologies at the nanometre scale and below. Achieving this, however, requires the creation of three-terminal devices that allow single molecules to be both gated and imaged at the atomic scale. We have accomplished this by integrating a graphene field effect transistor with a scanning tunnelling microscope, thus allowing gate-controlled charging and spectroscopic interrogation of individual tetrafluoro-tetracyanoquinodimethane molecules. We observe a non-rigid shift in the molecule's lowest unoccupied molecular orbital energy (relative to the Dirac point) as a function of gate voltage due to graphene polarization effects. Our results show that electron-electron interactions play an important role in how molecular energy levels align to the graphene Dirac point, and may significantly influence charge transport through individual molecules incorporated in graphene-based nanodevices.