Stimulus-dependent maximum entropy models of neural population codes

Neural populations encode information about their stimulus in a collective fashion, by joint activity patterns of spiking and silence. A full account of this mapping from stimulus to neural activity is given by the conditional probability distribution over neural codewords given the sensory input. To be able to infer a model for this distribution from large-scale neural recordings, we introduce a stimulus-dependent maximum entropy (SDME) model---a minimal extension of the canonical linear-nonlinear model of a single neuron, to a pairwise-coupled neural population. The model is able to capture the single-cell response properties as well as the correlations in neural spiking due to shared stimulus and due to effective neuron-to-neuron connections. Here we show that in a population of 100 retinal ganglion cells in the salamander retina responding to temporal white-noise stimuli, dependencies between cells play an important encoding role. As a result, the SDME model gives a more accurate account of single cell responses and in particular outperforms uncoupled models in reproducing the distributions of codewords emitted in response to a stimulus. We show how the SDME model, in conjunction with static maximum entropy models of population vocabulary, can be used to estimate information-theoretic quantities like surprise and information transmission in a neural population.Comment: 11 pages, 7 figure

Infantile spasms followed by childhood absence epilepsy: A case series

Purpose: Infantile spasms (IS) represent a severe seizure disorder of infancy and early childhood characterized by epileptic spasms along with hypsarrhythmia often accompanied by intellectual disability. According to the current classification and terminology (3) IS can be categorized as known etiology, formerly known as "symptomatic", when an underlying cause has been observed prior to the onset of spasms, or of "unknown cause" with "unfavorable" and "favorable" outcome (previously referred as "cryptogenic" or "idiopathic", respectively). Single reports described children with "unknown cause and favorable outcome" (UC/FO) IS who later developed childhood absence epilepsy (CAE). This study aims to determine the prevalence of CAE following IS. Methods: a multicenter retrospective chart review was performed; children with UC/FO IS who subsequently developed CAE during follow-up were identified. Eight Italian pediatric epilepsy centers participated in this study. Results: seven out of 24 (29 %) children (3 males) showing a favorable outcome (UC/FO) IS received a second diagnosis of CAE during follow-up. Mean age at IS presentation was 5.8 months (SD +/- 0.9). All achieved seizure control of IS at a mean age of 8.5 months (SD +/- 1.3) (3 monotherapy, 4 polytherapy). CAE was diagnosed at a mean age of 8.0 years (SD +/- 3.0). Six children achieved sustained remission of CAE with valproic acid, whereas 1 child required dual therapy by adding ethosuximide. Conclusion: although it is not possible to determine whether the association between UC/FO IS and CAE implies a causality relationship, the later occurrence of CAE in patients with UC/FO IS might support a possible role of thalamo-cortical dysfunction