Antisaccade performance in schizophrenia: A neural model of decision making in the superior colliculus

Antisaccade performance deficits in schizophrenia are generally interpreted as an impaired top-down inhibitory signal failing to suppress the erroneous response. We recorded the antisaccade performance (error rates and latencies) of healthy and schizophrenia subjects performing the mirror antisaccade task. A neural rise-to-threshold model of antisaccade performance was developed to uncover the biophysical mechanisms giving rise to the observed deficits in schizophrenia. Schizophrenia patients displayed greater variability in the antisaccade and corrected antisaccade latency distributions, increased error rates and decreased corrected errors, relative to healthy participants. Our model showed that (1) increased variability is due to a more noisy accumulation of information by schizophrenia patients, but their confidence level required before making a decision is unaffected, and (2) competition between the correct and erroneous decision processes, and not a third top-down inhibitory signal suppressing the erroneous response, accounts for the antisaccade performance of healthy and schizophrenia subjects. Local competition further ensured that a correct antisaccade is never followed by an error prosaccade. © 2014 Cutsuridis, Kumari and Ettinger

Recall Performance Improvement in a Bio-Inspired Model of the Mammalian Hippocampus

Mammalian hippocampus is involved in short-term formation of declarative memories. We employed a bio-inspired neural model of hippocampal CA1 region consisting of a zoo of excitatory and inhibitory cells. Cells’ firing was timed to a theta oscillation paced by two distinct neuronal populations exhibiting highly regular bursting activity, one tightly coupled to the trough and the other to the peak of theta. To systematically evaluate the model’s recall performance against number of stored patterns, overlaps and ‘active cells per pattern’, its cells were driven by a non-specific excitatory input to their dendrites. This excitatory input to model excitatory cells provided context and timing information for retrieval of previously stored memory patterns. Inhibition to excitatory cells’ dendrites acted as a non-specific global threshold machine that removed spurious activity during recall. Out of the three models tested, ‘model 1’ recall quality was excellent across all conditions. ‘Model 2’ recall was the worst. The number of ‘active cells per pattern’ had a massive effect on network recall quality regardless of how many patterns were stored in it. As ‘active cells per pattern’ decreased, network’s memory capacity increased, interference effects between stored patterns decreased, and recall quality improved. Key finding was that increased firing rate of an inhibitory cell inhibiting a network of excitatory cells has a better success at removing spurious activity at the network level and improving recall quality than increasing the synaptic strength of the same inhibitory cell inhibiting the same network of excitatory cells, while keeping its firing rate fixed

Towards a comprehensive taxonomy for characterizing robots

Every day a new robot is developed with advanced characteristics and technical qualities. The increasingly rapid growth of robots and their characteristics demands bridging between the application requirements and the robot specifications. This process requires a supporting conceptual structure that can capture as many robot qualities as possible. Presenting robot characteristics through the proposed conceptual structure would enable designers to optimize robot capabilities against application requirements. It would also help application developers to select the most appropriate robot. Without a formal structure, an accurate linking between the robot domain and the application domain is not possible. This paper presents a novel theoretical representation that can capture robot features and capabilities and express them as descriptive dimensions to be used to develop a capability profile. The profile is intended to unify robot description and presentation. The proposed structure is reinforced with several layers, sections, categorizations and levels to allow a detailed explanation of robot characteristics. It is hoped that the proposed structure will influence the design, development, and testing of robots for specific applications. At the same time, it would help in highlighting the corresponding outlines in robot application requirements

Fast reverse replays of recent spatiotemporal trajectories in a robotic hippocampal model

A number of computational models have recently emerged in an attempt to understand the dynamics of hippocampal replay, but there has been little progress in testing and implementing these models in real-world robotics settings. Presented here is a bioinspired hippocampal CA3 network model, that runs in real-time to produce reverse replays of recent spatiotemporal sequences in a robotic spatial navigation task. For the sake of computational efficiency, the model is composed of continuous-rate based neurons, but incorporates two biophysical properties that have recently been hypothesised to play an important role in the generation of reverse replays: intrinsic plasticity and short-term plasticity. As this model only replays recently active trajectories, it does not directly address the functional properties of reverse replay, for instance in robotic learning tasks, but it does support further investigations into how reverse replays could contribute to functional improvements

Encoding of Spatio-Temporal Input Characteristics by a CA1 Pyramidal Neuron Model

The in vivo activity of CA1 pyramidal neurons alternates between regular spiking and bursting, but how these changes affect information processing remains unclear. Using a detailed CA1 pyramidal neuron model, we investigate how timing and spatial arrangement variations in synaptic inputs to the distal and proximal dendritic layers influence the information content of model responses. We find that the temporal delay between activation of the two layers acts as a switch between excitability modes: short delays induce bursting while long delays decrease firing. For long delays, the average firing frequency of the model response discriminates spatially clustered from diffused inputs to the distal dendritic tree. For short delays, the onset latency and inter-spike-interval succession of model responses can accurately classify input signals as temporally close or distant and spatially clustered or diffused across different stimulation protocols. These findings suggest that a CA1 pyramidal neuron may be capable of encoding and transmitting presynaptic spatiotemporal information about the activity of the entorhinal cortex-hippocampal network to higher brain regions via the selective use of either a temporal or a rate code

Selective Reduction of AMPA Currents onto Hippocampal Interneurons Impairs Network Oscillatory Activity

Reduction of excitatory currents onto GABAergic interneurons in the forebrain results in impaired spatial working memory and altered oscillatory network patterns in the hippocampus. Whether this phenotype is caused by an alteration in hippocampal interneurons is not known because most studies employed genetic manipulations affecting several brain regions. Here we performed viral injections in genetically modified mice to ablate the GluA4 subunit of the AMPA receptor in the hippocampus (GluA4HC−/− mice), thereby selectively reducing AMPA receptor-mediated currents onto a subgroup of hippocampal interneurons expressing GluA4. This regionally selective manipulation led to a strong spatial working memory deficit while leaving reference memory unaffected. Ripples (125–250 Hz) in the CA1 region of GluA4HC−/− mice had larger amplitude, slower frequency and reduced rate of occurrence. These changes were associated with an increased firing rate of pyramidal cells during ripples. The spatial selectivity of hippocampal pyramidal cells was comparable to that of controls in many respects when assessed during open field exploration and zigzag maze running. However, GluA4 ablation caused altered modulation of firing rate by theta oscillations in both interneurons and pyramidal cells. Moreover, the correlation between the theta firing phase of pyramidal cells and position was weaker in GluA4HC−/− mice. These results establish the involvement of AMPA receptor-mediated currents onto hippocampal interneurons for ripples and theta oscillations, and highlight potential cellular and network alterations that could account for the altered working memory performance