Computational NeuroScience (CNS) conference is held annually alternating in America and Europe. This year it was held in Paris, next year is Québec City, Canada. There are more theoretical and simulation based studies, compared to experimental studies. Among the experimental studies, there were a lot of oscillation and synchrony related subjects.
Disclaimer: I was occupied with several things, so I was not 100% attending the conference, so my selection is heavily biased. These notes are primarily for my future reference.
Simon Laughlin. The influence of metabolic energy on neural computation (keynote)
There are three main categories of energy cost in the brain: (1) maintenance, (2) spike generation, and (3) synapse. Assuming a finite energy budget for the brain, the optimal efficient coding strategy can vary from small number of neurons with high rate to large population with sparse coding [see Fig 3, Laughlin 2001]. Variation of cost ratios across animals may be associated with different coding strategies to optimize for energy/bits. He illustrated the balance through various laws of diminishing return plots. He emphasized reverse engineering the brain, and concluded with the 10 principles of neural design (transcribed from the slides thanks to the photo by @neuroflips):
(1) save on wire, (2) make components irreducibly small, (3) send only what is needed, (4) send at the lowest rate, (5) sparsify, (6) compute directly with analogue primitives, (7) mix analogue and digital, (8) adapt, match and learn, (9) complexify (elaborate to specialize), (10) compute with chemistry??????. (question marks are from the original slide)
Sophie Denev. Rescuing the spike (keynote)
She proposed that the observation of high trial-to-trial variability in spike trains from single neurons is due to degeneracy in the population encoding. There are many ways the presynaptic population can evoke similar membrane potential fluctuations of a linear readout neuron, and hence she claims that through precisely controlled lateral inhibition, the neural code is precise in the population level, but seems variable if we only observe a single neuron. She briefly mentioned how a linear dynamical system might be implemented in such a coding system, but it seemed limited as to what kind of computations can be achieved.
There were several noise correlation (joint variability in the population activity) related talks:
Joel Zylberberg et al. Consistency requirements determine optimal noise correlations in neural populations
The “sign rule” says that if the signal correlation is opposite of the noise correlation, linear Fisher information (and OLE performance) is improved (see Fig 1, Averbeck, Latham, Pouget 2006). They showed a theorem confirming the sign rule in general setup, and furthermore showed the optimal noise correlation does NOT necessarily obey the sign rule (see Hu, Zylberberg, Shea-Brown 2013). Experiments from the retina does not obey the sign rule; noise correlation is positive even for cells tuned to the same direction, however, it is still near optimal according to their theory.
Federico Carnevale et al. The role of neural correlations in a decision-making task
During a vibration detection task, cross-correlations among neurons in the premotor cortex (in a 250 ms window) were shown to be dependent on behavior (see Carnevale et al. 2012). Federico told me that there were no sharp peaks in the cross-correlation. He further extrapolated the choice probability to the network level based on multivariate Gaussian approximation, and a simplification to categorize neurons into two classes (transient or sustained response).
Alex Pouget and Peter Latham each gave talks in the Functional role of correlations workshop.
Both were on Fisher information and effect of noise correlations. Pouget’s talk was focused on “differential correlation” which is the noise in the direction of the manifold that tuning curves encode information (noise that looks like signal). Peter talked about why there are so many neurons in the brain with linear Fisher information and additive noise (but I forgot the details!)
On the first day of the workshop, I participated in the New approaches to spike train analysis and neuronal coding workshop organized by Conor Houghton and Thomas Kreuz.
Florian Mormann. Measuring spike-field coherence and spike train synchrony
He emphasized on using nonparametric statistics for testing circular variable of interest: the phase of LFP oscillation conditioned on spike timings. In the second part, he talked about spike-distance (see Kreuz 2012) which is a smooth, time scale invariant measure of instantaneous synchrony among spike trains.
Rodrigo Quian Quiroga. Extracting information in time patterns and correlations with wavelets
Using Haar wavelet time bins as the feature space, he proposed scale free linear analysis of spike trains. In addition, he proposed discovering relevant temporal structure through a feature selection using mutual information. The method doesn’t seem to be able to find higher order interactions between time bins.
Ralph Andrzejak. Detecting directional couplings between spiking signals and time-continuous signals
Using distance based directional coupling analysis (see Chicharro, Andrzejak 2009; Andrzejak, Kreuz 2011), he showed that it is possible to find unidirectional coupling between continuous signals and spike trains via spike train distances. He mentioned the possibility of using spectral Granger causality for a similar purpose.
Adrià Tauste Campo. Estimation of directed information between simultaneous spike trains in decision making
Bayesian conditional information estimation through the use of context-tree weighting was used to infer directional information (analogous to Granger causality, but with mutual information). A compact Markovian structure is learned for binary time series.
I presented a poster on Bayesian entropy estimation in the main meeting, and gave a talk about nonparametric (kernel) methods for spike trains in the workshop.