arXiv subjects

Hugo Gabriel Eyherabide

Publications and source records attributed to Hugo Gabriel Eyherabide.

5 recordsLinked to original sources

arXiv2017

Disambiguating the role of noise correlations when decoding neural populations together

One of the most controversial problems in neural decoding is quantifying the information loss caused by ignoring noise correlations during optimal brain computations. For more than a decade, the measure here called $ ΔI^{DL} $ has been believed exact. However, we have recently shown that it can exceed the information loss $ ΔI^{B} $ caused by optimal decoders constructed ignoring noise correlations. Unfortunately, the different information notions underlying $ ΔI^{DL} $ and $ ΔI^{B} $, and the putative rigorous information-theoretical derivation of $ ΔI^{DL} $, both render unclear whether those findings indicate either flaws in $ ΔI^{DL} $ or major departures from traditional relations between information and decoding. Here we resolve this paradox and prove that, under certain conditions, observing $ ΔI^{DL} {>}ΔI^{B} $ implies that $ ΔI^{DL} $ is flawed. Motivated by this analysis, we test both measures using neural populations that transmit independent information. Our results show that $ ΔI^{DL} $ may deem noise correlations more important when decoding the populations together than when decoding them in parallel, whereas the opposite may occur for $ ΔI^{B} $. We trace these phenomena back, for $ ΔI^{B} $, to the choice of tie-breaking rules, and for $ ΔI^{DL} $, to unforeseen limitations within its information-theoretical foundations. Our study contributes with better estimates that potentially improve theoretical and experimental inferences currently drawn from $ ΔI^{DL} $ without noticing that it may constitute an upper bound. On the practical side, our results promote the design of optimal decoding algorithms and neuroprosthetics without recording noise correlations, thereby saving experimental and computational resources.

q-bio.NC↗

arXiv2017

Neural stochastic codes, encoding and decoding

Understanding brain function, constructing computational models and engineering neural prosthetics require assessing two problems, namely encoding and decoding, but their relation remains controversial. For decades, the encoding problem has been shown to provide insight into the decoding problem, for example, by upper bounding the decoded information. However, here we show that this need not be the case when studying response aspects beyond noise correlations, and trace back the actual causes of this major departure from traditional views. To that end, we reformulate the encoding and decoding problems from the observer or organism perspective. In addition, we study the role of spike-time precision and response discrimination, among other response aspects, using stochastic transformations of the neural responses, here called stochastic codes. Our results show that stochastic codes may cause different information losses when used to describe neural responses and when employed to train optimal decoders. Therefore, we conclude that response aspects beyond noise correlations may play different roles in encoding and decoding. In practice, our results show for the first time that decoders constructed low-quality descriptions of response aspects may operate optimally on high-quality descriptions and vice versa, thereby potentially yielding experimental and computational savings, as well as new opportunities for simplifying the design of computational brain models and neural prosthetics.

q-bio.NC↗

arXiv2010

Time and category information in pattern-based codes

Sensory stimuli are usually composed of different features (the what) appearing at irregular times (the when). Neural responses often use spike patterns to represent sensory information. The what is hypothesized to be encoded in the identity of the elicited patterns (the pattern categories), and the when, in the time positions of patterns (the pattern timing). However, this standard view is oversimplified. In the real world, the what and the when might not be separable concepts, for instance, if they are correlated in the stimulus. In addition, neuronal dynamics can condition the pattern timing to be correlated with the pattern categories. Hence, timing and categories of patterns may not constitute independent channels of information. In this paper, we assess the role of spike patterns in the neural code, irrespective of the nature of the patterns. We first define information-theoretical quantities that allow us to quantify the information encoded by different aspects of the neural response. We also introduce the notion of synergy/redundancy between time positions and categories of patterns. We subsequently establish the relation between the what and the when in the stimulus with the timing and the categories of patterns. To that aim, we quantify the mutual information between different aspects of the stimulus and different aspects of the response. This formal framework allows us to determine the precise conditions under which the standard view holds, as well as the departures from this simple case. Finally, we study the capability of different response aspects to represent the what and the when in the neural response.

q-bio.NC↗

arXiv2009

Bursts generate a non-reducible spike pattern code

At the single-neuron level, precisely timed spikes can either constitute firing-rate codes or spike-pattern codes that utilize the relative timing between consecutive spikes. There has been little experimental support for the hypothesis that such temporal patterns contribute substantially to information transmission. By using grasshopper auditory receptors as a model system, we show that correlations between spikes can be used to represent behaviorally relevant stimuli. The correlations reflect the inner structure of the spike train: a succession of burst-like patterns. We demonstrate that bursts with different spike counts encode different stimulus features, such that about 20% of the transmitted information corresponds to discriminating between different features, and the remaining 80% is used to allocate these features in time. In this spike-pattern code, the what and the when of the stimuli are encoded in the duration of each burst and the time of burst onset, respectively. Given the ubiquity of burst firing, we expect similar findings also for other neural systems.

q-bio.NC↗

arXiv2009

The information transmitted by spike patterns in single neurons

Spike patterns have been reported to encode sensory information in several brain areas. Here we assess the role of specific patterns in the neural code, by comparing the amount of information transmitted with different choices of the readout neural alphabet. This allows us to rank several alternative alphabets depending on the amount of information that can be extracted from them. One can thereby identify the specific patterns that constitute the most prominent ingredients of the code. We finally discuss the interplay of categorical and temporal information in the amount of synergy or redundancy in the neural code.

q-bio.QM↗