Spatio-temporal Representations of Uncertainty in Spiking Neural Networks
Authors: Cristina Savin, Sophie Denève
NeurIPS 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Here we present a novel circuit model for representing multidimensional real-valued distributions using a spike based spatio-temporal code. Our model combines the computational advantages of the currently competing models for probabilistic codes and exhibits realistic neural responses along a variety of classic measures. To investigate the experimental implications of our coding scheme, we assumed the posterior distribution is centred around a stimulus-specific mean (...) This allows us quantify the general properties of distributed sampling in terms of classic measures (tuning curves, Fano factors, FF, cross-correlogram, CCG, and spike count correlations, rsc) and how these change with uncertainty. |
| Researcher Affiliation | Academia | Cristina Savin IST Austria Klosterneuburg, A-3400, Austria csavin@ist.ac.at Sophie Deneve Group for Neural Theory, ENS Paris Rue d Ulm, 29, Paris, France sophie.deneve@ens.fr |
| Pseudocode | No | The paper describes formal equations for network dynamics but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access information (e.g., specific repository link, explicit code release statement, or mention of code in supplementary materials) for the methodology described. |
| Open Datasets | No | The paper describes synthetic data generated for the experiments ('assumed the posterior distribution is centred around a stimulus-specific mean (a set of S = 12 values, equidistantly distributed on a circle of radius 1 around the origin, see black dots in Fig. 3a)') but does not provide concrete access information (specific link, DOI, repository name, or formal citation with authors/year) for a publicly available or open dataset. |
| Dataset Splits | No | The paper describes running simulations for '50 trials, each 1s long' but does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) needed to reproduce data partitioning. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | Formally, the network dynamics minimise the squared reconstruction error, (y ˆy)2, under certain constraints on mean firing rate which ensure the representation is distributed (see Suppl. Info.). The resulting network consists of spiking neurons with simple leaky-integrate-and-fire dynamics, V = 1 τv V Wo + I, where V denotes the temporal derivative of V, the binary vector o denotes the spikes, oi(t) = δ iff Vi(t) > Θi, τv is the membrane time constant (same as that of the decoder), the neural threshold is Θi = P j Γ2 ij + λ and the recurrent connections, W = ΓTΓ + λ I, can be learned by STDP [8], where λ is a free parameter controlling neural sparseness. The phase of the decoding weights was sampled uniformly around the circle, with an amplitude drawn uniformly from the interval [0.005; 0.025]. We measured the mean firing rate of the neurons for each stimulus (averaged across 50 trials, each 1s long). |