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Tsien, J. Z.

Publications and source records attributed to Tsien, J. Z..

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Spike-Timing Patterns Conform to Gamma Distribution with Regional and Cell Type-Specific Characteristics

Spike-timing patterns - crucial for synaptic plasticity and neural computation - are often modeled as Poisson-like random processes, log-normal distribution or gamma-distribution patterns, each with different underlying assumptions that may or may not be biologically true. However, it is not entirely clear whether (and how well) these different models would or would not capture spike-timing statistical patterns across different neurons, regions, animal species and cognitive states. Here, we examine statistical patterns of spike-timing irregularity in 13 different cortical and subcortical regions from mouse, hamster, cat and monkey brains. In contrast to the widely-assumed Poisson or log-normal distribution patterns, we show that spike-timing patterns of various projection neurons-including cortical excitatory principal cells, hippocampal pyramidal cells, inhibitory striatal medium spiny neurons and dopaminergic neurons, as well as fast-spiking interneurons - all invariantly conform to the gamma-distribution model. While higher regularity in spike-timing patterns are observed in a few cases, such as mouse DA neurons and monkey motor cortical neurons, there is no clear tendency in increased firing regularity from the sensory and subcortical neurons to prefrontal or motor cortices, as previously entertained. Moreover, gamma shapes of spike-timing patterns remain robust over various natural cognitive states, such as sleep, awake periods, or during fearful episodic experiences. Interestingly, ketamine-induced general anesthesia or unconsciousness is associated with the breakdown of forebrain spike patterns from a singular gamma distribution into two distinct subtypes of gamma distributions, suggesting the importance of this spike-timing pattern in supporting natural cognitive states. These results suggest that gamma-distribution patterns of spike timing reflect not only a fundamental property conserved across different neurons, regions and animal species, but also an operation crucial for supporting natural cognitive states. Such gamma-distribution-based spike-timing patterns can also have important implications for real-time neural coding and realistic neuromorphic computing.

neuroscience

On Neural Code: The Self-Information Processor Disguised As Neuronal Variability?

One important goal of BRAIN projects is to crack the neural code -- to understand how information is represented in patterns of electrical activity generated by ensembles of neurons. Yet the major stumbling block in the understanding of neural code is neuronal variability - neurons in the brain discharge their spikes with tremendous variability in both the control resting states and across trials within the same experiments. Such on-going spike variability imposes a great conceptual challenge to the classic rate code and/or synchrony-based temporal code. In practice, spike variability is typically removed via over-the-trial averaging methods such as peri-event spike histogram. In contrast to view neuronal variability as a noise problem, here we hypothesize that neuronal variability should be viewed as the self-information processor. Under this conceptual framework, neurons transmit their information by conforming to the basic logic of the statistical Self-Information Theory: spikes with higher-probability inter-spike-intervals (ISI) contain less information, whereas spikes with lower-probability ISIs convey more information, termed as surprisal spikes. In other words, real-time information is encoded not by changes in firing frequency per se, but rather by spikes variability probability. When these surprisal spikes occur as positive surprisals or negative surprisals in a temporally coordinated manner across populations of cells, they generate cell-assembly neural code to convey discrete quanta of information in real-time. Importantly, such surprisal code can afford not only robust resilience to interference, but also biochemical coupling to energy metabolism, protein synthesis and gene expression at both synaptic sites and cell soma. We describe how this neural self-information theory might be used as a general decoding strategy to uncover the brains various cell assemblies in an unbiased manner.

neuroscience