Tamping Ramping: Algorithmic, Implementational, and Computational Explanations of Phasic Dopamine Signals in the Accumbens.

Substantial evidence suggests that the phasic activity of dopamine neurons represents reinforcement learning's temporal difference prediction error. However, recent reports of ramp-like increases in dopamine concentration in the striatum when animals are about to act, or are about to reach rewards, appear to pose a challenge to established thinking. This is because the implied activity is persistently predictable by preceding stimuli, and so cannot arise as this sort of prediction error. Here, we explore three possible accounts of such ramping signals: (a) the resolution of uncertainty about the timing of action; (b) the direct influence of dopamine over mechanisms associated with making choices; and (c) a new model of discounted vigour. Collectively, these suggest that dopamine ramps may be explained, with only minor disturbance, by standard theoretical ideas, though urgent questions remain regarding their proximal cause. We suggest experimental approaches to disentangling which of the proposed mechanisms are responsible for dopamine ramps

Supergravity Higgs Inflation and Shift Symmetry in Electroweak Theory

We present a model of inflation in a supergravity framework in the Einstein frame where the Higgs field of the next to minimal supersymmetric standard model (NMSSM) plays the role of the inflaton. Previous attempts which assumed non-minimal coupling to gravity failed due to a tachyonic instability of the singlet field during inflation. A canonical K\"{a}hler potential with \textit{minimal coupling} to gravity can resolve the tachyonic instability but runs into the $\eta$-problem. We suggest a model which is free of the $\eta$-problem due to an additional coupling in the K\"{a}hler potential which is allowed by the Standard Model gauge group. This induces directions in the potential which we call K-flat. For a certain value of the new coupling in the (N)MSSM, the K\"{a}hler potential is special, because it can be associated with a certain shift symmetry for the Higgs doublets, a generalization of the shift symmetry for singlets in earlier models. We find that K-flat direction has $H_u^0=-H_d^{0*}.$ This shift symmetry is broken by interactions coming from the superpotential and gauge fields. This direction fails to produce successful inflation in the MSSM but produces a viable model in the NMSSM. The model is specifically interesting in the Peccei-Quinn (PQ) limit of the NMSSM. In this limit the model can be confirmed or ruled-out not just by cosmic microwave background observations but also by axion searches.Comment: matches the published version at JCA

General Analysis of Inflation in the Jordan frame Supergravity

We study various inflation models in the Jordan frame supergravity with a logarithmic Kahler potential. We find that, in a class of inflation models containing an additional singlet in the superpotential, three types of inflation can be realized: the Higgs-type inflation, power-law inflation, and chaotic inflation with/without a running kinetic term. The former two are possible if the holomorphic function dominates over the non-holomorphic one in the frame function, while the chaotic inflation occurs when both are comparable. Interestingly, the fractional-power potential can be realized by the running kinetic term. We also discuss the implication for the Higgs inflation in supergravity.Comment: 16 pages, 1 figur

A Moving Bump in a Continuous Manifold: A Comprehensive Study of the Tracking Dynamics of Continuous Attractor Neural Networks

Understanding how the dynamics of a neural network is shaped by the network structure, and consequently how the network structure facilitates the functions implemented by the neural system, is at the core of using mathematical models to elucidate brain functions. This study investigates the tracking dynamics of continuous attractor neural networks (CANNs). Due to the translational invariance of neuronal recurrent interactions, CANNs can hold a continuous family of stationary states. They form a continuous manifold in which the neural system is neutrally stable. We systematically explore how this property facilitates the tracking performance of a CANN, which is believed to have clear correspondence with brain functions. By using the wave functions of the quantum harmonic oscillator as the basis, we demonstrate how the dynamics of a CANN is decomposed into different motion modes, corresponding to distortions in the amplitude, position, width or skewness of the network state. We then develop a perturbative approach that utilizes the dominating movement of the network's stationary states in the state space. This method allows us to approximate the network dynamics up to an arbitrary accuracy depending on the order of perturbation used. We quantify the distortions of a Gaussian bump during tracking, and study their effects on the tracking performance. Results are obtained on the maximum speed for a moving stimulus to be trackable and the reaction time for the network to catch up with an abrupt change in the stimulus.Comment: 43 pages, 10 figure

Higgs Chaotic Inflation in Standard Model and NMSSM

We construct a chaotic inflation model in which the Higgs fields play the role of the inflaton in the standard model as well as in the singlet extension of the supersymmetric standard model. The key idea is to allow a non-canonical kinetic term for the Higgs field. The model is a realization of the recently proposed running kinetic inflation, in which the coefficient of the kinetic term grows as the inflaton field. The inflaton potential depends on the structure of the Higgs kinetic term. For instance, the inflaton potential is proportional to phi^2 and phi^{2/3} in the standard model and NMSSM, respectively. It is also possible to have a flatter inflaton potential.Comment: 5 pages. v2:discussion and references adde

Dynamical Synapses Enhance Neural Information Processing: Gracefulness, Accuracy and Mobility

Experimental data have revealed that neuronal connection efficacy exhibits two forms of short-term plasticity, namely, short-term depression (STD) and short-term facilitation (STF). They have time constants residing between fast neural signaling and rapid learning, and may serve as substrates for neural systems manipulating temporal information on relevant time scales. The present study investigates the impact of STD and STF on the dynamics of continuous attractor neural networks (CANNs) and their potential roles in neural information processing. We find that STD endows the network with slow-decaying plateau behaviors-the network that is initially being stimulated to an active state decays to a silent state very slowly on the time scale of STD rather than on the time scale of neural signaling. This provides a mechanism for neural systems to hold sensory memory easily and shut off persistent activities gracefully. With STF, we find that the network can hold a memory trace of external inputs in the facilitated neuronal interactions, which provides a way to stabilize the network response to noisy inputs, leading to improved accuracy in population decoding. Furthermore, we find that STD increases the mobility of the network states. The increased mobility enhances the tracking performance of the network in response to time-varying stimuli, leading to anticipative neural responses. In general, we find that STD and STP tend to have opposite effects on network dynamics and complementary computational advantages, suggesting that the brain may employ a strategy of weighting them differentially depending on the computational purpose.Comment: 40 pages, 17 figure

Morphometric and macroanatomic examination of auditory ossicles in male wolves (Canis lupus)

Background: The aim of the study was to determine morphometric and macroanatomic features of auditory ossicles and the tympanic bulla in wolf. Materials and methods: For this purpose, 7 skulls of adult male wolf were used in the study. Auditory ossicles was photographed on a dissection microscope after it was removed from the skull. A total of 14 morphometric measurements were taken among the different points of malleus, incus and stapes in Image J programme. Mean values of the measurements were obtained and statistically compared in terms of sides (right-left). Results: In male wolves, the lengths of the right and left malleus were determined as mean 9.35 ± 0.14 and 9.57 ± 0.25 mm, the lengths of the incus as mean 3.01 ± 0.32 and 2.94 ± 0.16 mm, and the lengths of the stapes as mean 2.57 ± 0.12 and 2.59 ± 0.14 mm, respectively. The differences were not statistically significant when all the morphometric parameters were compared in terms of sides (p > 0.05). Conclusions: It is considered that this study will contribute to the anatomical studies to be conducted in the Canidae family regarding auditory ossicles

Evidence for surprise minimization over value maximization in choice behavior

Classical economic models are predicated on the idea that the ultimate aim of choice is to maximize utility or reward. In contrast, an alternative perspective highlights the fact that adaptive behavior requires agents' to model their environment and minimize surprise about the states they frequent. We propose that choice behavior can be more accurately accounted for by surprise minimization compared to reward or utility maximization alone. Minimizing surprise makes a prediction at variance with expected utility models; namely, that in addition to attaining valuable states, agents attempt to maximize the entropy over outcomes and thus 'keep their options open'. We tested this prediction using a simple binary choice paradigm and show that human decision-making is better explained by surprise minimization compared to utility maximization. Furthermore, we replicated this entropy-seeking behavior in a control task with no explicit utilities. These findings highlight a limitation of purely economic motivations in explaining choice behavior and instead emphasize the importance of belief-based motivations

Neural correlates of cognitive dissonance and choice-induced preference change

According to many modern economic theories, actions simply reflect an individual's preferences, whereas a psychological phenomenon called “cognitive dissonance” claims that actions can also create preference. Cognitive dissonance theory states that after making a difficult choice between two equally preferred items, the act of rejecting a favorite item induces an uncomfortable feeling (cognitive dissonance), which in turn motivates individuals to change their preferences to match their prior decision (i.e., reducing preference for rejected items). Recently, however, Chen and Risen [Chen K, Risen J (2010) J Pers Soc Psychol 99:573–594] pointed out a serious methodological problem, which casts a doubt on the very existence of this choice-induced preference change as studied over the past 50 y. Here, using a proper control condition and two measures of preferences (self-report and brain activity), we found that the mere act of making a choice can change self-report preference as well as its neural representation (i.e., striatum activity), thus providing strong evidence for choice-induced preference change. Furthermore, our data indicate that the anterior cingulate cortex and dorsolateral prefrontal cortex tracked the degree of cognitive dissonance on a trial-by-trial basis. Our findings provide important insights into the neural basis of how actions can alter an individual's preferences

Can distributed delays perfectly stabilize dynamical networks?

Signal transmission delays tend to destabilize dynamical networks leading to oscillation, but their dispersion contributes oppositely toward stabilization. We analyze an integro-differential equation that describes the collective dynamics of a neural network with distributed signal delays. With the gamma distributed delays less dispersed than exponential distribution, the system exhibits reentrant phenomena, in which the stability is once lost but then recovered as the mean delay is increased. With delays dispersed more highly than exponential, the system never destabilizes.Comment: 4pages 5figure