Brian2Loihi: An emulator for the neuromorphic chip Loihi using the spiking neural network simulator Brian

Michaelis, Carlo; Lehr, Andrew B.; Oed, Winfried; Tetzlaff, Christian

doi:10.3389/fninf.2022.1015624

Cited by 9 publications

(4 citation statements)

References 37 publications

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“…Another advantage of our implementation is its compatibility with all popular operating systems running on CPUs and GPUs 72,73 . Finally, our approach allows testing new algorithms compatible with neuromorphic hardware [88][89][90] , which has seen impressive resource-saving benefits by including dendrites 91 . We expect Dendrify to be a valuable tool for anyone interested in developing SNNs with a high degree of bioinspiration to study how singlecell properties can influence network-level functions.…”

Section: Discussionmentioning

confidence: 99%

Introducing the Dendrify framework for incorporating dendrites to spiking neural networks

2023

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Computational modeling has been indispensable for understanding how subcellular neuronal features influence circuit processing. However, the role of dendritic computations in network-level operations remains largely unexplored. This is partly because existing tools do not allow the development of realistic and efficient network models that account for dendrites. Current spiking neural networks, although efficient, are usually quite simplistic, overlooking essential dendritic properties. Conversely, circuit models with morphologically detailed neuron models are computationally costly, thus impractical for large-network simulations. To bridge the gap between these two extremes and facilitate the adoption of dendritic features in spiking neural networks, we introduce Dendrify, an open-source Python package based on Brian 2. Dendrify, through simple commands, automatically generates reduced compartmental neuron models with simplified yet biologically relevant dendritic and synaptic integrative properties. Such models strike a good balance between flexibility, performance, and biological accuracy, allowing us to explore dendritic contributions to network-level functions while paving the way for developing more powerful neuromorphic systems.

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Section: Discussionmentioning

confidence: 99%

Introducing the Dendrify framework for incorporating dendrites to spiking neural networks

2023

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“…Another advantage of our implementation is its compatibility with all popular operating systems running on CPUs and GPUs 72,73 . Finally, our approach allows testing new algorithms compatible with neuromorphic hardware [88][89][90] , which has seen impressive resource-saving benefits by including dendrites 91 . We expect Dendrify to be a valuable tool for anyone interested in developing SNNs with a high degree of bioinspiration to study how single-cell properties can influence networklevel functions.…”

Section: Discussionmentioning

confidence: 99%

Introducing the Dendrify framework for incorporating dendrites to spiking neural networks

Pagkalos

Chavlis

Poirazi

2022

Preprint

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Computational modeling has been indispensable for understanding how subcellular neuronal features influence circuit processing. However, the role of dendritic computations in network-level operations remains largely unexplored. This is partly because existing tools do not allow the development of realistic and efficient network models that account for dendrites. Current spiking neural networks, although efficient, are usually quite simplistic, overlooking essential dendritic properties. Conversely, circuit models with morphologically detailed neuron models are computationally costly, thus impractical for large-network simulations. To bridge the gap between these two extremes, we introduce Dendrify, an open-source Python package compatible with Brian2, designed to facilitate the development of bioinspired spiking neural networks. Dendrify, through simple commands, automatically generates reduced compartmental neuron models with simplified yet biologically relevant dendritic and synaptic integrative properties. Such models strike a good balance between flexibility, performance, and biological accuracy, allowing us to explore dendritic contributions to network-level functions while paving the way for developing more powerful neuromorphic systems.

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“…These limits can be overcome by a combination of software and hardware techniques, as in the work of [ 57 ] which makes use of code-generation for SNN [ 58 ], targeting GPUs; here, further software optimizations may be explored to accelerate SNN simulations on traditional hardware. Additionally, related work by [ 59 ] compiles models to be run on an emulated Loihi chip [ 1 ], allowing researchers to errorlessly simulate how their model would perform on the neuromorphic chip without actual access to one. This is possible because the Loihi chip can be shown to be equivalent to a leaky integrate-and-fire neuron with Euler time stepping integration [ 59 ].…”

Section: Neuromorphic Computing: Hardware Vs Softwarementioning

confidence: 99%

“…Additionally, related work by [ 59 ] compiles models to be run on an emulated Loihi chip [ 1 ], allowing researchers to errorlessly simulate how their model would perform on the neuromorphic chip without actual access to one. This is possible because the Loihi chip can be shown to be equivalent to a leaky integrate-and-fire neuron with Euler time stepping integration [ 59 ]. Different neuromorphic chips implement different neuronal models by varying the design and functionality of their circuitry.…”

Section: Neuromorphic Computing: Hardware Vs Softwarementioning

confidence: 99%

From Brain Models to Robotic Embodied Cognition: How Does Biological Plausibility Inform Neuromorphic Systems?

Pham,

D’Angiulli,

Dehnavi

et al. 2023

Brain Sciences

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We examine the challenging “marriage” between computational efficiency and biological plausibility—A crucial node in the domain of spiking neural networks at the intersection of neuroscience, artificial intelligence, and robotics. Through a transdisciplinary review, we retrace the historical and most recent constraining influences that these parallel fields have exerted on descriptive analysis of the brain, construction of predictive brain models, and ultimately, the embodiment of neural networks in an enacted robotic agent. We study models of Spiking Neural Networks (SNN) as the central means enabling autonomous and intelligent behaviors in biological systems. We then provide a critical comparison of the available hardware and software to emulate SNNs for investigating biological entities and their application on artificial systems. Neuromorphics is identified as a promising tool to embody SNNs in real physical systems and different neuromorphic chips are compared. The concepts required for describing SNNs are dissected and contextualized in the new no man’s land between cognitive neuroscience and artificial intelligence. Although there are recent reviews on the application of neuromorphic computing in various modules of the guidance, navigation, and control of robotic systems, the focus of this paper is more on closing the cognition loop in SNN-embodied robotics. We argue that biologically viable spiking neuronal models used for electroencephalogram signals are excellent candidates for furthering our knowledge of the explainability of SNNs. We complete our survey by reviewing different robotic modules that can benefit from neuromorphic hardware, e.g., perception (with a focus on vision), localization, and cognition. We conclude that the tradeoff between symbolic computational power and biological plausibility of hardware can be best addressed by neuromorphics, whose presence in neurorobotics provides an accountable empirical testbench for investigating synthetic and natural embodied cognition. We argue this is where both theoretical and empirical future work should converge in multidisciplinary efforts involving neuroscience, artificial intelligence, and robotics.

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Brian2Loihi: An emulator for the neuromorphic chip Loihi using the spiking neural network simulator Brian

Cited by 9 publications

References 37 publications

Introducing the Dendrify framework for incorporating dendrites to spiking neural networks

Introducing the Dendrify framework for incorporating dendrites to spiking neural networks

Introducing the Dendrify framework for incorporating dendrites to spiking neural networks

From Brain Models to Robotic Embodied Cognition: How Does Biological Plausibility Inform Neuromorphic Systems?

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