BRIAN simulator

In summary, the debate of rate vs. timing is not about the description timescale, but about the notion that neural activity and computation may be entirely and consistently defined by the time-varying rates r(t) in the network. In fact, it is interesting to cast this debate in the analysis framework proposed by David Marr. I [...]

Before I try to answer the questions I asked at the end of the previous post , I will first describe the different types of approaches in computational neuroscience. Note that this does not cover everything in theoretical and quantitative ... [More] neuroscience (see my first post). David Marr, a very important figure in computational neuroscience, proposed [...] [Less]

James Gibson defended the idea that what we perceive of the environment is affordances, that is, the possibilities of interactions they allow. For example, a knob affords twisting, or the ground affords support. The concept of affordance makes a lot of sense, but Gibson also insisted that we directly perceive these affordances. It has never [...]

To answer this question, I need to write about basic notions of epistemology (the philosophy of knowledge). Epistemology is concerned in particular with what knowledge is and how it is acquired. What is knowledge? Essentially, knowledge is statements ... [More] about the world. There are two types of statements. First there are specific statements or “observations”, for [...] [Less]

How much intrinsic noise is there in a neuron? This question would deserve a longer post, but here I will just make a few remarks. In vitro, when the membrane potential is recorded in current-clamp, little noise is seen. There could be hidden noise in the spike generating process (i.e., in the sodium channels), but [...]

What is computational neuroscience? Simply put, it is the field that is concerned with how the brain computes. The word “compute” is not necessarily an analogy with the computer, and it must be understood in a broad sense. It simply refers to the operations that must be carried out to perform cognitive functions (walking, recognizing [...]

Misconception #4: “A stochastic spike-based theory is nothing else than a rate-based theory, only at a finer timescale”. It is sometimes claimed or implied that there is no conceptual difference between the two kinds of theories, the only difference ... [More] being the timescale of the description (short timescale for spike-based theories, long timescale for rate-based theories). [...] [Less]

Misconception #3: “Neural codes can only be based on rates because neural networks are chaotic”. Whether this claim is true or not (and I will comment on it below), chaos does not imply that spike timing is irrelevant. To draw this conclusion is to commit the same category error as I discussed in the previous [...]

Misconception #2: “Neural responses are variable in vivo, therefore neural codes can only be based on rates”. Again, this is a category error. Neural variability (assuming this means randomness) is about determinism vs. stochasticity, not about rate ... [More] vs. timing. There can be stochastic or deterministic spike-based theories. I will expand on this point, because it [...] [Less]

To complement the previous post, I will comment on what firing rate means in spike-based theories. First of all, rate is important in spike-based theories. The timing of a spike can only exist if there is a spike. Therefore, the firing rate determines the rate of information in spike-based theories, but it does not determine [...]