Possible dendritic contribution to unimodal numerosity tuning and weber-fechner law-dependent numerical cognition

Response to change in the number of visual stimuli in zebrafish:A behavioural and molecular study

Evidence has shown that a variety of vertebrates, including fish, can discriminate collections of visual items on the basis of their numerousness using an evolutionarily conserved system for approximating numerical magnitude (the so-called Approximate Number System, ANS). Here we combine a habituation/dishabituation behavioural task with molecular biology assays to start investigating the neural bases of the ANS in zebrafish. Separate groups of zebrafish underwent a habituation phase with a set of 3 or 9 small red dots, associated with a food reward. The dots changed in size, position and density from trial to trial but maintained their numerousness, and the overall areas of the stimuli was kept constant. During the subsequent dishabituation test, zebrafish faced a change (i) in number (from 3 to 9 or vice versa with the same overall surface), or (ii) in shape (with the same overall surface and number), or (iii) in size (with the same shape and number). A control group of zebrafish was shown the same stimuli as during the habituation. RT-qPCR revealed that the telencephalon and thalamus were characterized by the most consistent modulation of the expression of the immediate early genes c-fos and egr-1 upon change in numerousness; in contrast, the retina and optic tectum responded mainly to changes in stimulus size.

Enumeration strategy differences revealed by saccade-terminated eye tracking

Brain regions involved in saccadic eye movements partially overlap with a frontoparietal network implicated in encoding numerosities. Eye movement patterns may plausibly reflect strategic scanning behaviours to resolve the open-ended task of efficiently enumerating visual arrays. If so, these patterns may help explain individual differences in enumeration acuity in terms of well-understood visual attention mechanisms. Most enumeration eye-tracking paradigms, however, do not allow for direct manipulation of eye movement behaviours to test these claims. In the current study we terminated trials after a specified number of saccades to systematically probe the time course of enumeration strategies. Fifteen adults (11 naïve, 4 informed) enumerated random dot arrays under three conditions: (1) a novel saccade-terminated design where arrays were visible until one, two or four saccades had occurred; (2) a duration-terminated design where arrays were shown for 250, 500 or 1000 ms; and (3) a response-terminated design where arrays were visible until a response. Participants gave more accurate responses when enumerating saccade-terminated trials despite taking a similar time as in the duration-terminated trials. When participants were informed how trials would terminate, their saccade onset latencies shifted to match task demands. Rotating saccade vectors to align with salient image locations accounted for variability in the orientation of saccade trajectories. These findings (1) show a combination of stimulus-derived visual processing and task-based strategic demands account for enumeration eye movements patterns, (2) validate a novel saccade-contingent trial termination procedure for studying sequences of enumeration eye movements, and (3) highlight the need to include analyses of spatial and temporal eye movement patterns into models of visual enumeration strategies.

Statistical physics approach to dendritic computation: the excitable-wave mean-field approximation

We analytically study the input-output properties of a neuron whose active dendritic tree, modeled as a Cayley tree of excitable elements, is subjected to Poisson stimulus. Both single-site and two-site mean-field approximations incorrectly predict a nonequilibrium phase transition which is not allowed in the model. We propose an excitable-wave mean-field approximation which shows good agreement with previously published simulation results [Gollo et al., PLoS Comput. Biol. 5, e1000402 (2009)] and accounts for finite-size effects. We also discuss the relevance of our results to experiments in neuroscience, emphasizing the role of active dendrites in the enhancement of dynamic range and in gain control modulation.

A low-order model of biological neural networks

A biologically plausible low-order model (LOM) of biological neural networks is proposed. LOM is a recurrent hierarchical network of models of dendritic nodes and trees; spiking and nonspiking neurons; unsupervised, supervised covariance and accumulative learning mechanisms; feedback connections; and a scheme for maximal generalization. These component models are motivated and necessitated by making LOM learn and retrieve easily without differentiation, optimization, or iteration, and cluster, detect, and recognize multiple and hierarchical corrupted, distorted, and occluded temporal and spatial patterns. Four models of dendritic nodes are given that are all described as a hyperbolic polynomial that acts like an exclusive-OR logic gate when the model dendritic nodes input two binary digits. A model dendritic encoder that is a network of model dendritic nodes encodes its inputs such that the resultant codes have an orthogonality property. Such codes are stored in synapses by unsupervised covariance learning, supervised covariance learning, or unsupervised accumulative learning, depending on the type of postsynaptic neuron. A masking matrix for a dendritic tree, whose upper part comprises model dendritic encoders, enables maximal generalization on corrupted, distorted, and occluded data. It is a mathematical organization and idealization of dendritic trees with overlapped and nested input vectors. A model nonspiking neuron transmits inhibitory graded signals to modulate its neighboring model spiking neurons. Model spiking neurons evaluate the subjective probability distribution (SPD) of the labels of the inputs to model dendritic encoders and generate spike trains with such SPDs as firing rates. Feedback connections from the same or higher layers with different numbers of unit-delay devices reflect different signal traveling times, enabling LOM to fully utilize temporally and spatially associated information. Biological plausibility of the component models is discussed. Numerical examples are given to demonstrate how LOM operates in retrieving, generalizing, and unsupervised and supervised learning.