Multiscale motion and deformation of bumps in stochastic neural fields with dynamic connectivity

The distinct timescales of synaptic plasticity and neural activity dynamics play an important role in the brain's learning and memory systems. Activity-dependent plasticity reshapes neural circuit architecture, determining spontaneous and stimulus-encoding spatiotemporal patterns of neural activity. Neural activity bumps maintain short term memories of continuous parameter values, emerging in spatially-organized models with short term excitation and long-range inhibition. Previously, we demonstrated nonlinear Langevin equations derived using an interface method accurately describe the dynamics of bumps in continuum neural fields with separate excitatory/inhibitory populations. Here we extend this analysis to incorporate effects of slow short term plasticity that modifies connectivity described by an integral kernel. Linear stability analysis adapted to these piecewise smooth models with Heaviside firing rates further indicate how plasticity shapes bumps' local dynamics. Facilitation (depression), which strengthens (weakens) synaptic connectivity originating from active neurons, tends to increase (decrease) stability of bumps when acting on excitatory synapses. The relationship is inverted when plasticity acts on inhibitory synapses. Multiscale approximations of the stochastic dynamics of bumps perturbed by weak noise reveal the plasticity variables evolve to slowly diffusing and blurred versions of that arising in the stationary solution. Nonlinear Langevin equations associated with bump positions or interfaces coupled to slowly evolving projections of plasticity variables accurately describe the wandering of bumps underpinned by these smoothed synaptic efficacy profiles.Comment: 19 pages, 11 figure

Neural network mechanisms of working memory interference

[eng] Our ability to memorize is at the core of our cognitive abilities. How could we effectively make decisions without considering memories of previous experiences? Broadly, our memories can be divided in two categories: long-term and short-term memories. Sometimes, short-term memory is also called working memory and throughout this thesis I will use both terms interchangeably. As the names suggest, long-term memory is the memory you use when you remember concepts for a long time, such as your name or age, while short-term memory is the system you engage while choosing between different wines at the liquor store. As your attention jumps from one bottle to another, you need to hold in memory characteristics of previous ones to pick your favourite. By the time you pick your favourite bottle, you might remember the prices or grape types of the other bottles, but you are likely to forget all of those details an hour later at home, opening the wine in front of your guests. The overall goal of this thesis is to study the neural mechanisms that underlie working memory interference, as reflected in quantitative, systematic behavioral biases. Ultimately, the goal of each chapter, even when focused exclusively on behavioral experiments, is to nail down plausible neural mechanisms that can produce specific behavioral and neurophysiological findings. To this end, we use the bump-attractor model as our working hypothesis, with which we often contrast the synaptic working memory model. The work performed during this thesis is described here in 3 main chapters, encapsulation 5 broad goals: In Chapter 4.1, we aim at testing behavioral predictions of a bump-attractor (1) network when used to store multiple items. Moreover, we connected two of such networks aiming to model feature-binding through selectivity synchronization (2). In Chapter 4.2, we aim to clarify the mechanisms of working memory interference from previous memories (3), the so-called serial biases. These biases provide an excellent opportunity to contrast activity-based and activity-silent mechanisms because both mechanisms have been proposed to be the underlying cause of those biases. In Chapter 4.3, armed with the same techniques used to seek evidence for activity-silent mechanisms, we test a prediction of the bump-attractor model with short-term plasticity (4). Finally, in light of the results from aim 4 and simple computer simulations, we reinterpret previous studies claiming evidence for activity-silent mechanisms (5)

An Analysis of Down Syndrome Children and the Importance of Their Cognitive and Communicative Development

This paper will look at the issues of cognitive and communication development in children with Down Syndrome. With recent advances in neuroscience, educators need to understand brain development in order to utilize methods in dealing with education problems that occur with this population. Therefore this paper will look at the Down Syndrome child in both a physiological and educational context, to better understand appropriate methods of intervention. Moreover, brain development and learning involves not only genetic content specific to each individual, but also the many paths laid out by the family, environment, social and educational needs. As they move through the first months of life, which is the time when neurons grow and connect with each other under the impulse of stimuli comes streaming in through the sensory organs. Their brain grows less, there are fewer neurons in some parts, and neurons establish less synaptic connections because of the fewer alterations in dendrite spines and axonal extensions that take longer (Bullock, Bennett, Johnston, Josephson, Marder, Fields, 2005). Although their neurons may have problems to develop and establish their connections, they may need to be surrounded by different stimuli, although we see that their development is slower. Early intervention in cognitive development provides strong stimuli, consistent, and appropriate to the needs of each child and is rich in content, well thought out and directed, because it involves the different stimuli and as well as the family to help to increase these children\u27s\u27 skill to succeed in life

THE ROLE OF PLASTICITY IN COGNITION: A TMS-EEG STUDY

This item is only available electronically.Past studies have implicated a relationship between the Dorsolateral Prefrontal Cortex (DLPFC), and working memory and cognitive flexibility performance as measured via the N back and Trail Making tasks. It stands to reason that inducing plastic change to increase excitability of the DLPFC should result in improved performance on these tasks. This study used a 2 x 2 within groups single-blinded design with fourteen healthy participants (19 to 29 years old) attending two sessions, receiving iTBS in one, and sham in the other, investigating whether intermittent theta burst stimulation (iTBS) increased excitability of the DLPFC, and improved task performance. Cortical excitability was measured with TMS-evoked potentials (TEPs). Wilcoxon tests were used to determine the effect of iTBS on TEPs and psychometric performance, and the relationships between dependent variables were investigated using correlational analyses. Results show nonsignificant mild increases in 2-Back and Trail Making A tasks following iTBS relative to sham, and moderate correlations between changes in task performance and iTBS induced TEP changes. These findings go against previous research that support the iTBS to modulate TEP amplitudes, but are consistent with literature only finding mild effects of rTMS on improving working memory and cognitive flexibility.Thesis (B.PsychSc(Hons)) -- University of Adelaide, School of Psychology, 201

A robust sound perception model suitable for neuromorphic implementation

Coath M, Sheik S, Chicca E, Indiveri G, Denham S, Wennekers T. A robust sound perception model suitable for neuromorphic implementation. Neuromorphic Engineering. 2014;7(278):1-10.We have recently demonstrated the emergence of dynamic feature sensitivity through exposure to formative stimuli in a real-time neuromorphic system implementing a hybrid analog/digital network of spiking neurons. This network, inspired by models of auditory processing in mammals, includes several mutually connected layers with distance-dependent transmission delays and learning in the form of spike timing dependent plasticity, which effects stimulus-driven changes in the network connectivity. Here we present results that demonstrate that the network is robust to a range of variations in the stimulus pattern, such as are found in naturalistic stimuli and neural responses. This robustness is a property critical to the development of realistic, electronic neuromorphic systems. We analyze the variability of the response of the network to “noisy” stimuli which allows us to characterize the acuity in information-theoretic terms. This provides an objective basis for the quantitative comparison of networks, their connectivity patterns, and learning strategies, which can inform future design decisions. We also show, using stimuli derived from speech samples, that the principles are robust to other challenges, such as variable presentation rate, that would have to be met by systems deployed in the real world. Finally we demonstrate the potential applicability of the approach to real sounds