Heterogeneity of heterogeneities in neuronal networks

Baroni, Fabiano; Mazzoni, Annalisa

doi:10.3389/fncom.2014.00161

“…We have demonstrated the benefits of diversity at criticality for different simple distributions of excitability (as requested in the recent literature; Baroni & Mazzoni, 2014 ). In the context of diversity-induced resonance ( Tessone et al, 2006 ), in which diversity plays a role similar to the noise in stochastic resonance, the firing rate modulation by heterogeneity causes an optimal correlation response of the network to an oscillatory external driving ( Mejias & Longtin, 2012 ; Mejias & Longtin, 2014 ) but no specific attribute has been previously identified to the network at the optimal level of diversity to justify its optimized response.…”

Section: Discussionmentioning

confidence: 93%

“…We have demonstrated the benefits of diversity at criticality for different simple distributions of excitability (as requested in the recent literature [48]). Furthermore, for the first time we provide evidence that the well-known advantages of criticality are magnified at tricriticality.…”

Section: Discussionmentioning

confidence: 94%

Diversity improves performance in excitable networks

Gollo

¹

,

Copelli

²

,

Roberts

³

2016

PeerJ

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As few real systems comprise indistinguishable units, diversity is a hallmark of nature. Diversity among interacting units shapes properties of collective behavior such as synchronization and information transmission. However, the benefits of diversity on information processing at the edge of a phase transition, ordinarily assumed to emerge from identical elements, remain largely unexplored. Analyzing a general model of excitable systems with heterogeneous excitability, we find that diversity can greatly enhance optimal performance (by two orders of magnitude) when distinguishing incoming inputs. Heterogeneous systems possess a subset of specialized elements whose capability greatly exceeds that of the nonspecialized elements. We also find that diversity can yield multiple percolation, with performance optimized at tricriticality. Our results are robust in specific and more realistic neuronal systems comprising a combination of excitatory and inhibitory units, and indicate that diversity-induced amplification can be harnessed by neuronal systems for evaluating stimulus intensities.

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“…We have demonstrated the benefits of diversity at criticality for different simple distributions of excitability (as requested in the recent literature [56]). In the context of diversityinduced resonance [8], in which diversity plays a role similar to the noise in stochastic resonance, the firing rate modulation by heterogeneity causes an optimal correlation response of the network to an oscillatory external driving [27,34] but no specific attribute has been previously identified to the network at the optimal level of diversity to justify its optimized response.…”

Section: Network With Excitatory and Inhibitory Nodesmentioning

confidence: 94%

Peer Review #2 of "Diversity improves performance in excitable networks (v0.1)"

2016

0

View full text Add to dashboard Cite

As few real systems comprise indistinguishable units, diversity is a hallmark of nature. Diversity among interacting units shapes properties of collective behavior such as synchronization and information transmission. However, the benefits of diversity on information processing at the edge of a phase transition, ordinarily assumed to emerge from identical elements, remain largely unexplored. Analyzing a general model of excitable systems with heterogeneous excitability, we find that diversity can greatly enhance optimal performance (by two orders of magnitude) when distinguishing incoming inputs. Heterogeneous systems possess a subset of specialized elements whose capability greatly exceeds that of the nonspecialized elements. We also find that diversity can yield multiple percolation, with performance optimized at tricriticality. Our results are robust in specific and more realistic neuronal systems comprising a combination of excitatory and inhibitory units, and indicate that diversity-induced amplification can be harnessed by neuronal systems for evaluating stimulus intensities.

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“…An intriguing observation is that biological brains and nanoscale electronic circuits share characteristics that can provide insights toward designing circuits and architectures that can be utilized for biologically inspired computation with potential power efficiency comparable to brains. First, the primary computational structures of biological brains, neurons and synapses, are highly heterogeneous and imprecise [14], [15], which is akin to the fact that all manufactured nanodevices have behavioral variability arising from process variation (PV), especially CMOS devices operating at subthreshold voltages [16]. Perhaps by designing circuits and architectures that can adapt to, and even utilize, such heterogeneity while trying to aggressively lower supply voltages, even greater power efficiency could be achieved compared to adhering to the strict design margins and deterministic behaviors that VLSI circuits are typically designed to realize.…”

Section: Introductionmentioning

confidence: 99%

Subthreshold Spintronic Stochastic Spiking Neural Networks With Probabilistic Hebbian Plasticity and Homeostasis

Pyle

¹

,

Zand

²

,

Sheikhfaal

³

et al. 2019

IEEE J. Explor. Solid-State Comput. Devices Circuits

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The neural sampling core (NSC) proposed herein offers a spintronic device-based circuit and learning mechanism utilizing imprecise and stochastic components, similar to biological brains, to realize ultralow-power neuromorphic computations at subthreshold voltages. Leveraging principles from neural sampling, a biologically plausible theory from computational neuroscience, a spintronic stochastic spiking neuron with digital Postsynaptic potentials is proposed in conjunction with low-precision spintronic synapses utilizing a new event-driven Probabilistic Hebbian Plasticity Rule, and a novel homeostasis mechanism that balances neural activity across multiple timescales and process variation effects. The primary computational operation, the summation of presynaptic potentials weighted by their corresponding synaptic efficacy and the neuron's homeostatic parameters, is performed in a parallel analog fashion using noisy and imprecise subthreshold components. It is demonstrated herein that the NSC is capable of learning orientation selectivity, much like the simple cells found in the visual cortex, in an unsupervised fashion at 311 nW per neuron and 1.9-7.7 nW per active synapse using a 200-mV supply voltage.

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Heterogeneity of heterogeneities in neuronal networks

Cited by 9 publications

References 13 publications

Diversity improves performance in excitable networks

Diversity improves performance in excitable networks

Peer Review #2 of "Diversity improves performance in excitable networks (v0.1)"

Subthreshold Spintronic Stochastic Spiking Neural Networks With Probabilistic Hebbian Plasticity and Homeostasis

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