Biology subjects

Katsanevaki, C.

Publications and source records attributed to Katsanevaki, C..

3 recordsLinked to original sources

bioRxiv2024

Stimulus-repetition effects on macaque V1 and V4 microcircuits explain gamma-synchronization increase

Under natural conditions, animals repeatedly encounter the same visual scenes, objects or patterns repeatedly. These repetitions constitute statistical regularities, which the brain captures in an internal model through learning. A signature of such learning in primate visual areas V1 and V4 is the gradual strengthening of gamma synchronization. We used a V1-V4 Dynamic Causal Model (DCM) to explain visually induced responses in early and late epochs from a sequence of several hundred grating presentations. The DCM reproduced the empirical increase in local and inter-areal gamma synchronization, revealing specific intrinsic connectivity effects that could explain the phenomenon. In a sensitivity analysis, the isolated modulation of several connection strengths induced increased gamma. Comparison of alternative models showed that empirical gamma increases are better explained by (1) repetition effects in both V1 and V4 intrinsic connectivity (alone or together with extrinsic) than in extrinsic connectivity alone, and (2) repetition effects on V1 and V4 population input rather than output gain. The best input gain model included effects in V1 granular and superficial excitatory populations and in V4 granular and deep excitatory populations. Our findings are consistent with gamma reflecting bottom-up signal precision, which increases with repetition and, therefore, with predictability and learning. HighlightsO_LIWe model learning effects in macaque visual cortex using Dynamic Causal Modeling. C_LIO_LIMicrocircuit-level changes explain the repetition-induced gamma increases. C_LIO_LIThe best models include changes 1) within V1 and V4 and 2) in neuronal input gain. C_LIO_LIGamma may reflect bottom-up signal precision. C_LI

neuroscience↗

bioRxiv2024

Would you agree if N is three?On statistical inference for small N.

Non-human primate studies traditionally use two or three animals. We previously used standard statistics to argue for using either one animal, for an inference about that sample, or five or more animals, for a useful inference about the population. A recently proposed framework argued for testing three animals and accepting the outcome found in the majority as the outcome that is most representative for the population. The proposal tests this framework under various assumptions about the true probability of the representative outcome in the population, i.e. its typicality. On this basis, it argues that the framework is valid across a wide range of typicalities. Here, we show (1) that the error rate of the framework depends strongly on the typicality of the representative outcome, (2) that an acceptable error rate requires this typicality to be very high (87% for a single type of outlier), which actually renders empirical testing beyond a single animal obsolete, (3) that moving from one to three animals decreases error rates mainly for typicality values of 70-90%, and much less for both lower and higher values. Furthermore, we use conjunction analysis to demonstrate that two out of three animals with a given outcome only allow to infer a lower bound to typicality of 9%, which is of limited value. Thus, the use of two or three animals does not allow a useful inference about the population, and if this option is nevertheless chosen, the inferred lower bound of typicality should be reported.

neuroscience↗

bioRxiv2022

Selective V1-to-V4 communication of attended stimuli mediated by attentional effects in V1

Selective attention implements preferential routing of attended stimuli, likely through increasing the influence of the respective synaptic inputs on higher-area neurons. As the inputs of competing stimuli converge onto postsynaptic neurons, presynaptic circuits might offer the best target for attentional top-down influences. If those influences enabled presynaptic circuits to selectively entrain postsynaptic neurons, this might explain selective routing. Indeed, when two visual stimuli induce two gamma rhythms in V1, only the gamma induced by the attended stimulus entrains gamma in V4. Here, we modeled induced responses with a Dynamic Causal Model for Cross-Spectral Densities and found that selective entrainment can be explained by attentional modulation of intrinsic V1 connections. Specifically, local inhibition was decreased in the granular input layer and increased in the supragranular output layer of the V1 circuit that processed the attended stimulus. Thus, presynaptic attentional influences and ensuing entrainment were sufficient to mediate selective routing. HIGHLIGHTSO_LIWe model selective visual attention in macaques using Dynamic Causal Modeling. C_LIO_LIIntrinsic V1 modulation can explain attention effects in V1-V4 communication. C_LIO_LIModulation of superficial and granular inhibition is key to induce the effects. C_LIO_LIThose modulations increase V1-V4 communication in a feedforward manner. C_LI GRAPHICAL ABSTRACT O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=97 SRC="FIGDIR/small/484223v2_ufig1.gif" ALT="Figure 1"> View larger version (32K): org.highwire.dtl.DTLVardef@1cc79a7org.highwire.dtl.DTLVardef@146e6bdorg.highwire.dtl.DTLVardef@1d6bd6borg.highwire.dtl.DTLVardef@12dd663_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗