Energetics of BCM type synaptic plasticity and storing of accurate information

Excitatory synaptic signaling in cortical circuits is thought to be metabolically expensive. Two fundamental brain functions, learning and memory, are associated with long-term synaptic plasticity, but we know very little about energetics of these slow biophysical processes. This study investigates the interplay between stochastic BCM type synaptic plasticity, its metabolic requirements, and the accuracy and retention of stored information in synaptic weights, within the frameworks of stochastic dynamical systems and nonequilibrium thermodynamics. The dynamic mean-field is derived for the synaptic weights, and it is found that the energy used by plastic synapses, related to their information content, is primarily caused by fluctuations in the synaptic weights and in presynaptic firing activity. Such information-related plasticity energy rate, together with the accuracy of stored information depend nonlinearly on key neurophysiological parameters, which is due to bistability in the system: synapses plus their postsynaptic neuron. At the onset of bistability, the memory lifetime, its accuracy, and plasticity energy rate are all positively correlated and exhibit sharp peaks. However, in the bistable regime, the accuracy of encoded information and plasticity energetics are generally anticorrelated, which suggests that a precise storing of synaptic information neither has to be metabolically expensive nor it is limited by energy consumption. Interestingly, such a limit on synaptic coding accuracy is imposed instead by a derivative of the plasticity energy rate with respect to the presynaptic firing, and this relationship has a general character that is independent of the plasticity type. An estimate for primate neocortex reveals that a relative metabolic cost of BCM type synaptic plasticity, as a fraction of the overall neuronal cost, can vary from negligible to substantial, depending on a synaptic working regime and presynaptic firing.

UP-DOWN cortical dynamics reflect state transitions in a bistable network

In the idling brain, neuronal circuits transition between periods of sustained firing (UP state) and quiescence (DOWN state), a pattern the mechanisms of which remain unclear. Here we analyzed spontaneous cortical population activity from anesthetized rats and found that UP and DOWN durations were highly variable and that population rates showed no significant decay during UP periods. We built a network rate model with excitatory (E) and inhibitory (I) populations exhibiting a novel bistable regime between a quiescent and an inhibition-stabilized state of arbitrarily low rate. Fluctuations triggered state transitions, while adaptation in E cells paradoxically caused a marginal decay of E-rate but a marked decay of I-rate in UP periods, a prediction that we validated experimentally. A spiking network implementation further predicted that DOWN-to-UP transitions must be caused by synchronous high-amplitude events. Our findings provide evidence of bistable cortical networks that exhibit non-rhythmic state transitions when the brain rests.

Numerical Study of Flow Patterns in a Tandem Array Cylinder System

The spectral element method, a direct numerical technique, is used to study the behavior of flow past two cylinders in tandem array. A control cylinder is employed in front of the main cylinder to study the drag reduction performance and flow patterns. Three major flow patterns are found, including single cylinder vortex shedding, two cylinders vortex shedding and suppression. The flow patterns are affected by the distance between two cylinders, Reynolds number and the diameter ratios of cylinders. In a bistable regime, when there is a critical distance between cylinders, drag is reduced dramatically.

Piezoelectric 2D materials for bistable NEMS energy harvesters

ABSTRACTThe dynamics of one atom thick h-BN suspended nanoribbons have been obtained by first performing ab-initio calculations of the deformation potential energy and then solving numerically a Langevine type equation to explore their use as energy harvesting devices. Similarly to our previous proposal for a graphene-based harvester1, an applied compressive strain is used to drive the clamped-clamped nanoribbon structure into a bistable regime, where quasi-harmonic vibrations are combined with low frequency swings between the minima of a double-well potential. h-BN, graphene and MoS2 similar structures have been compared in terms of the static response to a compressive strain and of the dynamic evolution induced by an external noisy vibration. Due to its intrinsic piezoelectric response, the mechanical harvester naturally provides an electrical power that is readily available or can be stored by simply contacting the monolayer at its ends. Engineering the induced non-linearity, the proposed device is predicted to harvest an electrical root mean square (rms) power of more than 180 fW when it is excited by a noisy external force characterized by a white Gaussian frequency distribution with an intensity in the order of Frms=5pN.