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Simple Cortical and Thalamic Neuron Models for Digital Arithmetic Circuit Implementation.

ABSTRACT: Trade-off between reproducibility of neuronal activities and computational efficiency is one of crucial subjects in computational neuroscience and neuromorphic engineering. A wide variety of neuronal models have been studied from different viewpoints. The digital spiking silicon neuron (DSSN) model is a qualitative model that focuses on efficient implementation by digital arithmetic circuits. We expanded the DSSN model and found appropriate parameter sets with which it reproduces the dynamical behaviors of the ionic-conductance models of four classes of cortical and thalamic neurons. We first developed a four-variable model by reducing the number of variables in the ionic-conductance models and elucidated its mathematical structures using bifurcation analysis. Then, expanded DSSN models were constructed that reproduce these mathematical structures and capture the characteristic behavior of each neuron class. We confirmed that statistics of the neuronal spike sequences are similar in the DSSN and the ionic-conductance models. Computational cost of the DSSN model is larger than that of the recent sophisticated Integrate-and-Fire-based models, but smaller than the ionic-conductance models. This model is intended to provide another meeting point for above trade-off that satisfies the demand for large-scale neuronal network simulation with closer-to-biology models.

SUBMITTER: Nanami T

PROVIDER: S-EPMC4865656 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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Simple Cortical and Thalamic Neuron Models for Digital Arithmetic Circuit Implementation.

Nanami Takuya T Kohno Takashi T

Frontiers in neuroscience 20160513

Trade-off between reproducibility of neuronal activities and computational efficiency is one of crucial subjects in computational neuroscience and neuromorphic engineering. A wide variety of neuronal models have been studied from different viewpoints. The digital spiking silicon neuron (DSSN) model is a qualitative model that focuses on efficient implementation by digital arithmetic circuits. We expanded the DSSN model and found appropriate parameter sets with which it reproduces the dynamical b ...[more]

PMID: 27242397

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Project description:Thalamic neurons have been long assumed to fire in tonic mode during perceptive states, and in burst mode during sleep and unconsciousness. However, recent evidence suggests that bursts may also be relevant in the encoding of sensory information. Here, we explore the neural code of such thalamic bursts. In order to assess whether the burst code is generic or whether it depends on the detailed properties of each bursting neuron, we analyzed two neuron models incorporating different levels of biological detail. One of the models contained no information of the biophysical processes entailed in spike generation, and described neuron activity at a phenomenological level. The second model represented the evolution of the individual ionic conductances involved in spiking and bursting, and required a large number of parameters. We analyzed the models' input selectivity using reverse correlation methods and information theory. We found that n-spike bursts from both models transmit information by modulating their spike count in response to changes to instantaneous input features, such as slope, phase, amplitude, etc. The stimulus feature that is most efficiently encoded by bursts, however, need not coincide with one of such classical features. We therefore searched for the optimal feature among all those that could be expressed as a linear transformation of the time-dependent input current. We found that bursting neurons transmitted 6 times more information about such more general features. The relevant events in the stimulus were located in a time window spanning ~100 ms before and ~20 ms after burst onset. Most importantly, the neural code employed by the simple and the biologically realistic models was largely the same, implying that the simple thalamic neuron model contains the essential ingredients that account for the computational properties of the thalamic burst code. Thus, our results suggest the n-spike burst code is a general property of thalamic neurons.

Project description:The diversity of intrinsic dynamics observed in neurons may enhance the computations implemented in the circuit by enriching network-level emergent properties such as synchronization and phase locking. Large-scale spiking network models of entire brain regions offer a platform to test theories of neural computation and cognitive function, providing useful insights on information processing in the nervous system. However, a systematic in-depth investigation requires network simulations to capture the biological intrinsic diversity of individual neurons at a sufficient level of accuracy. The computationally efficient Izhikevich model can reproduce a wide range of neuronal behaviors qualitatively. Previous studies using optimization techniques, however, were less successful in quantitatively matching experimentally recorded voltage traces. In this article, we present an automated pipeline based on evolutionary algorithms to quantitatively reproduce features of various classes of neuronal spike patterns using the Izhikevich model. Employing experimental data from Hippocampome.org, a comprehensive knowledgebase of neuron types in the rodent hippocampus, we demonstrate that our approach reliably fit Izhikevich models to nine distinct classes of experimentally recorded spike patterns, including delayed spiking, spiking with adaptation, stuttering, and bursting. Importantly, by leveraging the parameter-exploration capabilities of evolutionary algorithms, and by representing qualitative spike pattern class definitions in the error landscape, our approach creates several suitable models for each neuron type, exhibiting appropriate feature variabilities among neurons. Moreover, we demonstrate the flexibility of our methodology by creating multi-compartment Izhikevich models for each neuron type in addition to single-point versions. Although the results presented here focus on hippocampal neuron types, the same strategy is broadly applicable to any neural systems.

Dataset Information

Simple Cortical and Thalamic Neuron Models for Digital Arithmetic Circuit Implementation.

Publications

Simple Cortical and Thalamic Neuron Models for Digital Arithmetic Circuit Implementation.

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