An official website of the United States government
The spintronic stochastic spiking neuron (S3N) developed herein realizes biologically mimetic stochastic spiking characteristics observed within in vivo cortical neurons, while operating several orders of magnitude more rapidly and exhibiting a favorable energy profile. This work leverages a novel probabilistic spintronic switching element device that provides thermally-driven and current-controlled tunable stochasticity in a compact, low-energy, and high-speed package. Simulation program with integrated circuit emphasis (SPICE) simulation results indicate that the equivalent of 1 second of in vivo neuronal spiking characteristics can be generated on the order of nanoseconds, enabling the feasibility of extremely rapid emulation of in vivo neuronal behaviors for future statistical models of cortical information processing. Their results also indicate that the S3N can generate spikes on the order of ten picoseconds while dissipating only 0.6–9.6 μW, depending on the spiking rate. Additionally, they demonstrate that an S3N can implement perceptron functionality, such as AND-gate- and OR-gate-based logic processing, and provide future extensions of the work to more advanced stochastic neuromorphic architectures.
more »
« less
Network resonance can be generated independently at distinct levels of neuronal organization
Resonance is defined as maximal response of a system to periodic inputs in a limited frequency band. Resonance may serve to optimize inter-neuronal communication, and has been observed at multiple levels of neuronal organization. However, it is unknown how neuronal resonance observed at the network level is generated and how network resonance depends on the properties of the network building blocks. Here, we first develop a metric for quantifying spike timing resonance in the presence of background noise, extending the notion of spiking resonance for in vivo experiments. Using conductance-based models, we find that network resonance can be inherited from resonances at other levels of organization, or be intrinsically generated by combining mechanisms across distinct levels. Resonance of membrane potential fluctuations, postsynaptic potentials, and single neuron spiking can each be generated independently of resonance at any other level and be propagated to the network level. At all levels of organization, interactions between processes that give rise to low- and high-pass filters generate the observed resonance. Intrinsic network resonance can be generated by the combination of filters belonging to different levels of organization. Inhibition-induced network resonance can emerge by inheritance from resonance of membrane potential fluctuations, and be sharpened by presynaptic high-pass filtering. Our results demonstrate a multiplicity of qualitatively different mechanisms that can generate resonance in neuronal systems, and provide analysis tools and a conceptual framework for the mechanistic investigation of network resonance in terms of circuit components, across levels of neuronal organization.
more »
« less
- PAR ID:
- 10342326
- Date Published:
- Journal Name:
- PLoS computational biology
- Volume:
- 18
- ISSN:
- 1553-734X
- Page Range / eLocation ID:
- e1010364
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Analog neuromorphic computing systems emulate the parallelism and connectivity of the human brain, promising greater expressivity and energy efficiency compared to those of digital systems. Though many devices have emerged as candidates for artificial neurons and artificial synapses, there have been few device candidates for artificial dendrites. In this work, we report on biocompatible graphene-based artificial dendrites (GrADs) that can implement dendritic processing. By using a dual side-gate configuration, current applied through a Nafion membrane can be used to control device conductance across a trilayer graphene channel, showing spatiotemporal responses of leaky recurrent, alpha, and Gaussian dendritic potentials. The devices can be variably connected to enable higher-order neuronal responses, and we show through data-driven spiking neural network simulations that spiking activity is reduced by ≤15% without accuracy loss while low-frequency operation is stabilized. This positions the GrADs as strong candidates for energy efficient bio-interfaced spiking neural networks.more » « less
-
Abstract Small-conductance calcium-activated potassium (SK) channels show a ubiquitous distribution on neurons, in both somatodendritic and axonal regions. SK channels are associated with neuronal activity regulating action potential frequency, dendritic excitability, and synaptic plasticity. Although the physiology of SK channels and the mechanisms that control their surface expression levels have been investigated extensively, little is known about what controls SK channel diffusion in the neuronal plasma membrane. This aspect is important, as the diffusion of SK channels at the surface may control their localization and proximity to calcium channels, hence increasing the likelihood of SK channel activation by calcium. In this study, we successfully investigated the diffusion of SK channels labeled with quantum dots on human embryonic kidney cells and dissociated hippocampal neurons by combining a single-particle tracking method with total internal reflection fluorescence microscopy. We observed that actin filaments interfere with SK mobility, decreasing their diffusion coefficient. We also found that during neuronal maturation, SK channel diffusion was gradually inhibited in somatodendritic compartments. Importantly, we observed that axon barriers formed at approximately days in vitro 6 and restricted the diffusion of SK channels on the axon initial segment (AIS). However, after neuron maturation, SK channels on the AIS were strongly immobilized, even after disruption of the actin network, suggesting that crowding may cause this effect. Altogether, our work provides insight into how SK channels diffuse on the neuronal plasma membrane and how actin and membrane crowding impacts SK channel diffusion.more » « less
-
Neurons differentiate mechanical stimuli force and rate to elicit unique functional responses, driving the need for further tools to generate various mechanical stimuli. Here, cell-internal nanomagnetic forces (iNMF) are introduced by manipulating internalized magnetic nanoparticles with an external magnetic field across cortical neuron networks in vitro. Under iNMF, cortical neurons exhibit calcium (Ca2+) influx, leading to modulation of activity observed through Ca2+ event rates. Inhibiting particle uptake or altering nanoparticle exposure time reduced the neuronal response to nanomagnetic forces, exposing the requirement of nanoparticle uptake to induce the Ca2+ response. In highly active cortical networks, iNMF robustly modulates synchronous network activity, which is lasting and repeatable. Using pharmacological blockers, it is shown that iNMF activates mechanosensitive ion channels to induce the Ca2+ influx. Then, in contrast to transient mechanically evoked neuronal activity, iNMF activates Ca2+-activated potassium (KCa) channels to stabilize the neuronal membrane potential and induce network activity shifts. The findings reveal the potential of magnetic nanoparticle-mediated mechanical stimulation to modulate neuronal circuit dynamics, providing insights into the biophysics of neuronal computation.more » « less
-
Big data often has emergent structure that exists at multiple levels of abstraction, which are useful for characterizing complex interactions and dynamics of the observations. Here, we consider multiple levels of abstraction via a multiresolution geometry of data points at different granularities. To construct this geometry we define a time-inhomogemeous diffusion process that effectively condenses data points together to uncover nested groupings at larger and larger granularities. This inhomogeneous process creates a deep cascade of intrinsic low pass filters on the data affinity graph that are applied in sequence to gradually eliminate local variability while adjusting the learned data geometry to increasingly coarser resolutions. We provide visualizations to exhibit our method as a “continuously-hierarchical” clustering with directions of eliminated variation highlighted at each step. The utility of our algorithm is demonstrated via neuronal data condensation, where the constructed multiresolution data geometry uncovers the organization, grouping, and connectivity between neurons.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.13039/100006221
