Neuronal Dynamics

Gerstner, Wulfram; Kistler, Werner M.; Naud, Richard; Paninski, Liam

doi:10.1017/cbo9781107447615

Cited by 1,158 publications

(533 citation statements)

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“…17 of Ref. [Gerstner et al, 2014]). In addition, while the highly structured input used to train the network may at first appear highly artificial, we point out that similar sparse sequential activity patterns have been observed in motor cortex, which is a main input to striatum, in rodents performing a learned lever-press task (Peters et al, 2014; Dhawale et al, 2015).…”

Section: Resultsmentioning

confidence: 99%

Learning multiple variable-speed sequences in striatum via cortical tutoring

Murray

Escola

2017

eLife

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Sparse, sequential patterns of neural activity have been observed in numerous brain areas during timekeeping and motor sequence tasks. Inspired by such observations, we construct a model of the striatum, an all-inhibitory circuit where sequential activity patterns are prominent, addressing the following key challenges: (i) obtaining control over temporal rescaling of the sequence speed, with the ability to generalize to new speeds; (ii) facilitating flexible expression of distinct sequences via selective activation, concatenation, and recycling of specific subsequences; and (iii) enabling the biologically plausible learning of sequences, consistent with the decoupling of learning and execution suggested by lesion studies showing that cortical circuits are necessary for learning, but that subcortical circuits are sufficient to drive learned behaviors. The same mechanisms that we describe can also be applied to circuits with both excitatory and inhibitory populations, and hence may underlie general features of sequential neural activity pattern generation in the brain.DOI: http://dx.doi.org/10.7554/eLife.26084.001

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Section: Resultsmentioning

confidence: 99%

Learning multiple variable-speed sequences in striatum via cortical tutoring

Murray

Escola

2017

eLife

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“…First, brain networks consist of billions of neurons (Kandel et al, 2000). Even if each neuron is described as a relatively simple dynamic processing unit (e.g., an adaptive integrate-and-fire neuron with two or three update equations per neuron Izhikevich, 2003; Brette and Gerstner, 2005; Gerstner et al, 2014), the sheer number of units suggests that faster than real-time simulation of these equations will be hard to achieve on a single core. Hence parallelization of computation is desirable.…”

Section: Introductionmentioning

confidence: 99%

Limits to high-speed simulations of spiking neural networks using general-purpose computers

Zenke

Gerstner

2014

Front. Neuroinform.

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To understand how the central nervous system performs computations using recurrent neuronal circuitry, simulations have become an indispensable tool for theoretical neuroscience. To study neuronal circuits and their ability to self-organize, increasing attention has been directed toward synaptic plasticity. In particular spike-timing-dependent plasticity (STDP) creates specific demands for simulations of spiking neural networks. On the one hand a high temporal resolution is required to capture the millisecond timescale of typical STDP windows. On the other hand network simulations have to evolve over hours up to days, to capture the timescale of long-term plasticity. To do this efficiently, fast simulation speed is the crucial ingredient rather than large neuron numbers. Using different medium-sized network models consisting of several thousands of neurons and off-the-shelf hardware, we compare the simulation speed of the simulators: Brian, NEST and Neuron as well as our own simulator Auryn. Our results show that real-time simulations of different plastic network models are possible in parallel simulations in which numerical precision is not a primary concern. Even so, the speed-up margin of parallelism is limited and boosting simulation speeds beyond one tenth of real-time is difficult. By profiling simulation code we show that the run times of typical plastic network simulations encounter a hard boundary. This limit is partly due to latencies in the inter-process communications and thus cannot be overcome by increased parallelism. Overall, these results show that to study plasticity in medium-sized spiking neural networks, adequate simulation tools are readily available which run efficiently on small clusters. However, to run simulations substantially faster than real-time, special hardware is a prerequisite.

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“…Even in cases where ample neural data may be available for both subjects, neural dynamics learned from the easier subject might transfer some useful knowledge that improves the decoding performance of the harder subject. However, existing BCI decoders usually fail to generalize to different sessions 22 or subjects, because they fail to capture the underlying neural dynamics 23 . We believe this is because current approaches lack a principled representation of neural dynamics, obtained through exploration of possible interactions between encoding and decoding problems.…”

Section: Introductionmentioning

confidence: 99%

Interactions of encoding and decoding problems to understand motor control

Wen

Yin

Zheng

et al. 2019

Preprint

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Learning a map from movement to neural data (Encoding Problem) and vice versa (Decoding Problem) are crucial to understanding motor control. A principled encoding model that understands underlying neural dynamics can help better solve the decoding problem. Here, we develop a new generative encoding model leveraging deep learning that autonomously captures neural dynamics. After training, the model can synthesize spike trains given any observed kinematics, under the guidance of the learned neural dynamics. When neural data from other sessions or subjects are limited, synthesized spike trains can improve cross-session and cross-subject decoding performance of a Brain Computer Interface decoder. For cross-subject, even with ample data for both subjects, neural dynamics learned from a previous subject can transfer useful knowledge that improves the best achievable decoding performance for the new subject. The approach is general and fully data-driven, and hence could apply to neuroscience encoding and decoding problems beyond motor control.1 Current deep generative models can only generate samples from the distribution they have been trained on. For example, in machine vision, a generative model can only generate images of dogs and cats if it has only been trained on images of cats and dogs. We do not expect it can generalize to images of birds. Here, because each distinct kinematics is a new category such as birds, we do not expect our model to generalize well to novel kinematics in terms of neural dynamics. However, we show that, after fine-tuning with a small amount of neural data from another session or subject, our encoding model can generalize to novel situations improving cross-session and cross-subject decoding performance.

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Neuronal Dynamics

Cited by 1,158 publications

References 0 publications

Learning multiple variable-speed sequences in striatum via cortical tutoring

Learning multiple variable-speed sequences in striatum via cortical tutoring

Limits to high-speed simulations of spiking neural networks using general-purpose computers

Interactions of encoding and decoding problems to understand motor control

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