Emergent Orientation Selectivity from Random Networks in Mouse Visual Cortex

The connectivity principles underlying the emergence of orientation selectivity in primary visual cortex (V1) of mammals lacking an orientation map (such as rodents and lagomorphs) are poorly understood. We present a computational model in which random connectivity gives rise to orientation selectivity that matches experimental observations. The model predicts that mouse V1 neurons should exhibit intricate receptive fields in the two-dimensional frequency domain, causing a shift in orientation preferences with spatial frequency. We find evidence for these features in mouse V1 using calcium imaging and intracellular whole-cell recordings. Pattadkal et al. show that orientation selectivity can emerge from random connectivity, and offer a distinct perspective for how computations occur in the neocortex. They propose that a random convergence of inputs can provide signals for orientation preference in contrast with the dominant model that requires a precise arrangement.Fil: Pattadkal, Jagruti J.. University of Texas at Austin; Estados UnidosFil: Mato, German. Comisión Nacional de Energía Atómica. Gerencia del Área de Energía Nuclear. Instituto Balseiro; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: van Vreeswijk, Carl. Centre National de la Recherche Scientifique; FranciaFil: Priebe, Nicholas J.. University of Texas at Austin; Estados UnidosFil: Hansel, David. Centre National de la Recherche Scientifique; Franci

Exact analysis of the subthreshold variability for conductance-based neuronal models with synchronous synaptic inputs

The spiking activity of neocortical neurons exhibits a striking level of variability, even when these networks are driven by identical stimuli. The approximately Poisson firing of neurons has led to the hypothesis that these neural networks operate in the asynchronous state. In the asynchronous state neurons fire independently from one another, so that the probability that a neuron experience synchronous synaptic inputs is exceedingly low. While the models of asynchronous neurons lead to observed spiking variability, it is not clear whether the asynchronous state can also account for the level of subthreshold membrane potential variability. We propose a new analytical framework to rigorously quantify the subthreshold variability of a single conductance-based neuron in response to synaptic inputs with prescribed degrees of synchrony. Technically we leverage the theory of exchangeability to model input synchrony via jump-process-based synaptic drives; we then perform a moment analysis of the stationary response of a neuronal model with all-or-none conductances that neglects post-spiking reset. As a result, we produce exact, interpretable closed forms for the first two stationary moments of the membrane voltage, with explicit dependence on the input synaptic numbers, strengths, and synchrony. For biophysically relevant parameters, we find that the asynchronous regime only yields realistic subthreshold variability (voltage variance $\simeq 4-9\mathrm{mV^2}$) when driven by a restricted number of large synapses, compatible with strong thalamic drive. By contrast, we find that achieving realistic subthreshold variability with dense cortico-cortical inputs requires including weak but nonzero input synchrony, consistent with measured pairwise spiking correlations

A High-Resolution View of Genome-Wide Pneumococcal Transformation

Transformation is an important mechanism of microbial evolution through which bacteria have been observed to rapidly adapt in response to clinical interventions; examples include facilitating vaccine evasion and the development of penicillin resistance in the major respiratory pathogen Streptococcus pneumoniae. To characterise the process in detail, the genomes of 124 S. pneumoniae isolates produced through in vitro transformation were sequenced and recombination events detected. Those recombinations importing the selected marker were independent of unselected events elsewhere in the genome, the positions of which were not significantly affected by local sequence similarity between donor and recipient or mismatch repair processes. However, both types of recombinations were sometimes mosaic, with multiple non-contiguous segments originating from the same molecule of donor DNA. The lengths of the unselected events were exponentially distributed with a mean of 2.3 kb, implying that recombinations are stochastically resolved with a fixed per base probability of 4.4×10−4 bp−1. This distribution of recombination sizes, coupled with an observed under representation of large insertions within transferred sequence, suggests transformation has the potential to reduce the size of bacterial genomes, and is unlikely to act as an efficient mechanism for the uptake of accessory genomic loci

Encoding of Temporal Information by Timing, Rate, and Place in Cat Auditory Cortex

A central goal in auditory neuroscience is to understand the neural coding of species-specific communication and human speech sounds. Low-rate repetitive sounds are elemental features of communication sounds, and core auditory cortical regions have been implicated in processing these information-bearing elements. Repetitive sounds could be encoded by at least three neural response properties: 1) the event-locked spike-timing precision, 2) the mean firing rate, and 3) the interspike interval (ISI). To determine how well these response aspects capture information about the repetition rate stimulus, we measured local group responses of cortical neurons in cat anterior auditory field (AAF) to click trains and calculated their mutual information based on these different codes. ISIs of the multiunit responses carried substantially higher information about low repetition rates than either spike-timing precision or firing rate. Combining firing rate and ISI codes was synergistic and captured modestly more repetition information. Spatial distribution analyses showed distinct local clustering properties for each encoding scheme for repetition information indicative of a place code. Diversity in local processing emphasis and distribution of different repetition rate codes across AAF may give rise to concurrent feed-forward processing streams that contribute differently to higher-order sound analysis

A New Mechanism for Neuronal Gain Control (or How the Gain in Brains Has Mainly Been Explained)

AbstractOne of the more prosaic but necessary features of almost any information processing system is gain control. All such systems must have some way to adjust the relationship between input, which can vary dramatically depending on changes in the environment, and output, which is almost always required to remain within a limited range of amplitudes. While the volume control on a radio or the brightness control on a computer monitor are not the most exciting or highly touted features, imagine such devices without these forms of gain control. Many an engineer can attest to the large effort required to design automatic gain controls in telephones, cameras, and radio transmitters