A new learning rule (Precise-Spike-Driven (PSD) Synaptic Plasticity) is proposed for processing and memorizing spatiotemporal patterns. PSD is a supervised learning rule that is analytically derived from the traditional Widrow-Hoff rule and can be used to train neurons to associate an input spatiotemporal spike pattern with a desired spike train. Synaptic adaptation is driven by the error between the desired and the actual output spikes, with positive errors causing long-term potentiation and negative errors causing long-term depression. The amount of modification is proportional to an eligibility trace that is triggered by afferent spikes. The PSD rule is both computationally efficient and biologically plausible. The properties of this learning rule are investigated extensively through experimental simulations, including its learning performance, its generality to different neuron models, its robustness against noisy conditions, its memory capacity, and the effects of its learning parameters. Experimental results show that the PSD rule is capable of spatiotemporal pattern classification, and can even outperform a well studied benchmark algorithm with the proposed relative confidence criterion. The PSD rule is further validated on a practical example of an optical character recognition problem. The results again show that it can achieve a good recognition performance with a proper encoding. Finally, a detailed discussion is provided about the PSD rule and several related algorithms including tempotron, SPAN, Chronotron and ReSuMe.
Spikes play an essential role in information transmission in central nervous system, but how neurons learn from them remains a challenging question. Most algorithms studied how to train spiking neurons to process patterns encoded with a sole assumption of either a rate or a temporal code. Is there a general learning algorithm capable of processing both codes regardless of the intense debate on them within neuroscience community? In this paper, we propose several threshold-driven plasticity algorithms to address the above question. In addition to formulating the algorithms, we also provide proofs with respect to several properties, such as robustness and convergence. The experimental results illustrate that our algorithms are simple, effective and yet efficient for training neurons to learn spike patterns. Due to their simplicity and high efficiency, our algorithms would be potentially beneficial for both software and hardware implementations. Neurons with our algorithms can also detect and recognize embedded features from a background sensory activity. With the as-proposed algorithms, a single neuron can successfully perform multicategory classifications by making decisions based on its output spike number in response to each category. Spike patterns being processed can be encoded with both spike rates and precise timings. When afferent spike timings matter, neurons will automatically extract temporal features without being explicitly instructed as to which point to fire.
Variations on DNA sequences profoundly affect how we develop diseases and respond to pathogens and drugs. Atomic force microscopy (AFM) provides a nanomechanical imaging approach for genetic analysis with nanometre resolution. However, unlike fluorescence imaging that has wavelength-specific fluorophores, the lack of shape-specific labels largely hampers widespread applications of AFM imaging. Here we report the development of a set of differentially shaped, highly hybridizable self-assembled DNA origami nanostructures serving as shape IDs for magnified nanomechanical imaging of single-nucleotide polymorphisms. Using these origami shape IDs, we directly genotype single molecules of human genomic DNA with an ultrahigh resolution of ∼10 nm and the multiplexing ability. Further, we determine three types of disease-associated, long-range haplotypes in samples from the Han Chinese population. Single-molecule analysis allows robust haplotyping even for samples with low labelling efficiency. We expect this generic shape ID-based nanomechanical approach to hold great potential in genetic analysis at the single-molecule level.
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