Computational neruscience를 공부할 수 있는 사이트

Computational neuroscience provided with python code example

 

https://mrgreene09.github.io/computational-neuroscience-textbook/

Computational Neuroscience

 

 

Spiking rate vs Spiking timing

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2819463/

Spike-Rate Coding and Spike-Time Coding Are Affected Oppositely by Different Adaptation Mechanisms

Neural information can be encoded in diverse ways (Perkel and Bullock, 1968). Known coding strategies used by single neurons can be divided approximately into rate codes and temporal codes, but these terms have been used inconsistently and are prone confusion (Dayan and Abbott, 2001). To distinguish our usage, we refer to spike-rate coding and spike-time coding. In spike-rate coding, the number of spikes within a time window or the reciprocal of a single interspike interval (ISI) (1/ISI = instantaneous rate) correlates with some stimulus attribute; in this case, ISIs must be shorter than the minimum time scale of the stimulus, as explained by the Nyquist theorem (Theunissen and Miller, 1995; Rieke et al., 1997). In spike-time coding, the fine temporal structure of the spike train (i.e., spike timing) sparsely encodes information about the temporal structure of the stimulus; in this case, ISIs are longer than the minimum period of the stimulus and simply reflect the interval between suprathreshold stimulus upstrokes (Oswald et al., 2007). Reliable spike timing confers good spike-time coding whereas reliable ISIs confer good spike-rate coding.