Order-Based Representation in Random Networks of Cortical Neurons

The wide range of time scales involved in neural excitability and synaptic transmission might lead to ongoing change in the temporal structure of responses to recurring stimulus presentations on a trial-to-trial basis. This is probably the most severe biophysical constraint on putative time-based primitives of stimulus representation in neuronal networks. Here we show that in spontaneously developing large-scale random networks of cortical neurons in vitro the order in which neurons are recruited following each stimulus is a naturally emerging representation primitive that is invariant to significant temporal changes in spike times. With a relatively small number of randomly sampled neurons, the information about stimulus position is fully retrievable from the recruitment order. The effective connectivity that makes order-based representation invariant to time warping is characterized by the existence of stations through which activity is required to pass in order to propagate further into the network. This study uncovers a simple invariant in a noisy biological network in vitro; its applicability under in vivo constraints remains to be seen

Dynamic excitatory and inhibitory gain modulation can produce flexible, robust and optimal decision-making

<div><p>Behavioural and neurophysiological studies in primates have increasingly shown the involvement of urgency signals during the temporal integration of sensory evidence in perceptual decision-making. Neuronal correlates of such signals have been found in the parietal cortex, and in separate studies, demonstrated attention-induced gain modulation of both excitatory and inhibitory neurons. Although previous computational models of decision-making have incorporated gain modulation, their abstract forms do not permit an understanding of the contribution of inhibitory gain modulation. Thus, the effects of co-modulating both excitatory and inhibitory neuronal gains on decision-making dynamics and behavioural performance remain unclear. In this work, we incorporate time-dependent co-modulation of the gains of both excitatory and inhibitory neurons into our previous biologically based decision circuit model. We base our computational study in the context of two classic motion-discrimination tasks performed in animals. Our model shows that by simultaneously increasing the gains of both excitatory and inhibitory neurons, a variety of the observed dynamic neuronal firing activities can be replicated. In particular, the model can exhibit winner-take-all decision-making behaviour with higher firing rates and within a significantly more robust model parameter range. It also exhibits short-tailed reaction time distributions even when operating near a dynamical bifurcation point. The model further shows that neuronal gain modulation can compensate for weaker recurrent excitation in a decision neural circuit, and support decision formation and storage. Higher neuronal gain is also suggested in the more cognitively demanding reaction time than in the fixed delay version of the task. Using the exact temporal delays from the animal experiments, fast recruitment of gain co-modulation is shown to maximize reward rate, with a timescale that is surprisingly near the experimentally fitted value. Our work provides insights into the simultaneous and rapid modulation of excitatory and inhibitory neuronal gains, which enables flexible, robust, and optimal decision-making.</p></div

The Brain's Router: A Cortical Network Model of Serial Processing in the Primate Brain

The human brain efficiently solves certain operations such as object recognition and categorization through a massively parallel network of dedicated processors. However, human cognition also relies on the ability to perform an arbitrarily large set of tasks by flexibly recombining different processors into a novel chain. This flexibility comes at the cost of a severe slowing down and a seriality of operations (100–500 ms per step). A limit on parallel processing is demonstrated in experimental setups such as the psychological refractory period (PRP) and the attentional blink (AB) in which the processing of an element either significantly delays (PRP) or impedes conscious access (AB) of a second, rapidly presented element. Here we present a spiking-neuron implementation of a cognitive architecture where a large number of local parallel processors assemble together to produce goal-driven behavior. The precise mapping of incoming sensory stimuli onto motor representations relies on a “router” network capable of flexibly interconnecting processors and rapidly changing its configuration from one task to another. Simulations show that, when presented with dual-task stimuli, the network exhibits parallel processing at peripheral sensory levels, a memory buffer capable of keeping the result of sensory processing on hold, and a slow serial performance at the router stage, resulting in a performance bottleneck. The network captures the detailed dynamics of human behavior during dual-task-performance, including both mean RTs and RT distributions, and establishes concrete predictions on neuronal dynamics during dual-task experiments in humans and non-human primates

Spatial attention deficits are causally linked to an area in macaque temporal cortex

Spatial neglect is a common clinical syndrome involving disruption of the brain’s attention-related circuitry, including the dorsocaudal temporal cortex. In macaques, the attention deficits associated with neglect can be readily modeled, but the absence of evidence for temporal cortex involvement has suggested a fundamental difference from humans. To map the neurological expression of neglect-like attention deficits in macaques, we measured attention-related fMRI activity across the cerebral cortex during experimental induction of neglect through reversible inactivation of the superior colliculus and frontal eye fields. During inactivation, monkeys exhibited hallmark attentional deficits of neglect in tasks using either motion or non-motion stimuli. The behavioral deficits were accompanied by marked reductions in fMRI attentional modulation that were strongest in a small region on the floor of the superior temporal sulcus; smaller reductions were also found in frontal eye fields and dorsal parietal cortex. Notably, direct inactivation of the mid-superior temporal sulcus (STS) cortical region identified by fMRI caused similar neglect-like spatial attention deficits. These results identify a putative macaque homolog to temporal cortex structures known to play a central role in human neglect

Midbrain activity shapes high-level visual properties in the primate temporal cortex

Recent fMRI experiments identified an attention-related region in the macaque temporal cortex, here called the floor of the superior temporal sulcus (fSTS), as the primary cortical target of superior colliculus (SC) activity. However, it remains unclear which aspects of attention are processed by fSTS neurons and how or why these might depend on SC activity. Here, we show that SC inactivation decreases attentional modulations in fSTS neurons by increasing their activity for ignored stimuli in addition to decreasing their activity for attended stimuli. Neurons in the fSTS also exhibit event-related activity during attention tasks linked to detection performance, and this link is eliminated during SC inactivation. Finally, fSTS neurons respond selectively to particular visual objects, and this selectivity is reduced markedly during SC inactivation. These diverse, high-level properties of fSTS neurons all involve visual signals that carry behavioral relevance. Their dependence on SC activity could reflect a circuit that prioritizes cortical processing of events detected subcortically