Neural Models of Normal and Abnormal Behavior: What Do Schizophrenia, Parkinsonism, Attention Deficit Disorder, and Depression Have in Common?

Defense Advanced Research Projects Agency and Office of Naval Research (N00014-95-1-0409); National Science Foundation (IRI-97-20333

Neural Dynamics of Autistic Behaviors: Cognitive, Emotional, and Timing Substrates

What brain mechanisms underlie autism and how do they give rise to autistic behavioral symptoms? This article describes a neural model, called the iSTART model, which proposes how cognitive, emotional, timing, and motor processes may interact together to create and perpetuate autistic symptoms. These model processes were originally developed to explain data concerning how the brain controls normal behaviors. The iSTART model shows how autistic behavioral symptoms may arise from prescribed breakdowns in these brain processes.Air Force Office of Scientific Research (F49620-01-1-0397); Office of Naval Research (N00014-01-1-0624

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

How Laminar Frontal Cortex and Basal Ganglia Circuits Interact to Control Planned and Reactive Saccades

The basal ganglia and frontal cortex together allow animals to learn adaptive responses that acquire rewards when prepotent reflexive responses are insufficient. Anatomical studies show a rich pattern of interactions between the basal ganglia and distinct frontal cortical layers. Analysis of the laminar circuitry of the frontal cortex, together with its interactions with the basal ganglia, motor thalamus, superior colliculus, and inferotemporal and parietal cortices, provides new insight into how these brain regions interact to learn and perform complexly conditioned behaviors. A neural model whose cortical component represents the frontal eye fields captures these interacting circuits. Simulations of the neural model illustrate how it provides a functional explanation of the dynamics of 17 physiologically identified cell types found in these areas. The model predicts how action planning or priming (in cortical layers III and VI) is dissociated from execution (in layer V), how a cue may serve either as a movement target or as a discriminative cue to move elsewhere, and how the basal ganglia help choose among competing actions. The model simulates neurophysiological, anatomical, and behavioral data about how monkeys perform saccadic eye movement tasks, including fixation; single saccade, overlap, gap, and memory-guided saccades; anti-saccades; and parallel search among distractors.Defense Advanced Research Projects Agency and the Office of Naval Research (N00014-95-l-0409, N00014-92-J-1309, N00014-95-1-0657); National Science Foundation (IRI-97-20333)

The malleable brain: plasticity of neural circuits and behavior: A review from students to students

One of the most intriguing features of the brain is its ability to be malleable, allowing it to adapt continually to changes in the environment. Specific neuronal activity patterns drive long-lasting increases or decreases in the strength of synaptic connections, referred to as long-term potentiation (LTP) and long-term depression (LTD) respectively. Such phenomena have been described in a variety of model organisms, which are used to study molecular, structural, and functional aspects of synaptic plasticity. This review originated from the first International Society for Neurochemistry (ISN) and Journal of Neurochemistry (JNC) Flagship School held in Alpbach, Austria (Sep 2016), and will use its curriculum and discussions as a framework to review some of the current knowledge in the field of synaptic plasticity. First, we describe the role of plasticity during development and the persistent changes of neural circuitry occurring when sensory input is altered during critical developmental stages. We then outline the signaling cascades resulting in the synthesis of new plasticity-related proteins, which ultimately enable sustained changes in synaptic strength. Going beyond the traditional understanding of synaptic plasticity conceptualized by LTP and LTD, we discuss system-wide modifications and recently unveiled homeostatic mechanisms, such as synaptic scaling. Finally, we describe the neural circuits and synaptic plasticity mechanisms driving associative memory and motor learning. Evidence summarized in this review provides a current view of synaptic plasticity in its various forms, offers new insights into the underlying mechanisms and behavioral relevance, and provides directions for future research in the field of synaptic plasticity.Fil: Schaefer, Natascha. University of Wuerzburg; AlemaniaFil: Rotermund, Carola. University of Tuebingen; AlemaniaFil: Blumrich, Eva Maria. Universitat Bremen; AlemaniaFil: Lourenco, Mychael V.. Universidade Federal do Rio de Janeiro; BrasilFil: Joshi, Pooja. Robert Debre Hospital; FranciaFil: Hegemann, Regina U.. University of Otago; Nueva ZelandaFil: Jamwal, Sumit. ISF College of Pharmacy; IndiaFil: Ali, Nilufar. Augusta University; Estados UnidosFil: García Romero, Ezra Michelet. Universidad Veracruzana; MéxicoFil: Sharma, Sorabh. Birla Institute of Technology and Science; IndiaFil: Ghosh, Shampa. Indian Council of Medical Research; IndiaFil: Sinha, Jitendra K.. Indian Council of Medical Research; IndiaFil: Loke, Hannah. Hudson Institute of Medical Research; AustraliaFil: Jain, Vishal. Defence Institute of Physiology and Allied Sciences; IndiaFil: Lepeta, Katarzyna. Polish Academy of Sciences; ArgentinaFil: Salamian, Ahmad. Polish Academy of Sciences; ArgentinaFil: Sharma, Mahima. Polish Academy of Sciences; ArgentinaFil: Golpich, Mojtaba. University Kebangsaan Malaysia Medical Centre; MalasiaFil: Nawrotek, Katarzyna. University Of Lodz; ArgentinaFil: Paid, Ramesh K.. Indian Institute of Chemical Biology; IndiaFil: Shahidzadeh, Sheila M.. Syracuse University; Estados UnidosFil: Piermartiri, Tetsade. Universidade Federal de Santa Catarina; BrasilFil: Amini, Elham. University Kebangsaan Malaysia Medical Centre; MalasiaFil: Pastor, Verónica. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Biología Celular y Neurociencia ; ArgentinaFil: Wilson, Yvette. University of Melbourne; AustraliaFil: Adeniyi, Philip A.. Afe Babalola University; NigeriaFil: Datusalia, Ashok K.. National Brain Research Centre; IndiaFil: Vafadari, Benham. Polish Academy of Sciences; ArgentinaFil: Saini, Vedangana. University of Nebraska; Estados UnidosFil: Suárez Pozos, Edna. Instituto Politécnico Nacional; MéxicoFil: Kushwah, Neetu. Defence Institute of Physiology and Allied Sciences; IndiaFil: Fontanet, Paula. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Biología Celular y Neurociencia ; ArgentinaFil: Turner, Anthony J.. University of Leeds; Reino Unid

Motor Learning Mechanism on the Neuron Scale

Based on existing data, we wish to put forward a biological model of motor system on the neuron scale. Then we indicate its implications in statistics and learning. Specifically, neuron firing frequency and synaptic strength are probability estimates in essence. And the lateral inhibition also has statistical implications. From the standpoint of learning, dendritic competition through retrograde messengers is the foundation of conditional reflex and grandmother cell coding. And they are the kernel mechanisms of motor learning and sensory motor integration respectively. Finally, we compare motor system with sensory system. In short, we would like to bridge the gap between molecule evidences and computational models.Comment: 8 pages, 4 figure

Dynamical principles in neuroscience

Dynamical modeling of neural systems and brain functions has a history of success over the last half century. This includes, for example, the explanation and prediction of some features of neural rhythmic behaviors. Many interesting dynamical models of learning and memory based on physiological experiments have been suggested over the last two decades. Dynamical models even of consciousness now exist. Usually these models and results are based on traditional approaches and paradigms of nonlinear dynamics including dynamical chaos. Neural systems are, however, an unusual subject for nonlinear dynamics for several reasons: (i) Even the simplest neural network, with only a few neurons and synaptic connections, has an enormous number of variables and control parameters. These make neural systems adaptive and flexible, and are critical to their biological function. (ii) In contrast to traditional physical systems described by well-known basic principles, first principles governing the dynamics of neural systems are unknown. (iii) Many different neural systems exhibit similar dynamics despite having different architectures and different levels of complexity. (iv) The network architecture and connection strengths are usually not known in detail and therefore the dynamical analysis must, in some sense, be probabilistic. (v) Since nervous systems are able to organize behavior based on sensory inputs, the dynamical modeling of these systems has to explain the transformation of temporal information into combinatorial or combinatorial-temporal codes, and vice versa, for memory and recognition. In this review these problems are discussed in the context of addressing the stimulating questions: What can neuroscience learn from nonlinear dynamics, and what can nonlinear dynamics learn from neuroscience?This work was supported by NSF Grant No. NSF/EIA-0130708, and Grant No. PHY 0414174; NIH Grant No. 1 R01 NS50945 and Grant No. NS40110; MEC BFI2003-07276, and Fundación BBVA

Dopaminergic and Non-Dopaminergic Value Systems in Conditioning and Outcome-Specific Revaluation

Animals are motivated to choose environmental options that can best satisfy current needs. To explain such choices, this paper introduces the MOTIVATOR (Matching Objects To Internal Values Triggers Option Revaluations) neural model. MOTIVATOR describes cognitiveemotional interactions between higher-order sensory cortices and an evaluative neuraxis composed of the hypothalamus, amygdala, and orbitofrontal cortex. Given a conditioned stimulus (CS), the model amygdala and lateral hypothalamus interact to calculate the expected current value of the subjective outcome that the CS predicts, constrained by the current state of deprivation or satiation. The amygdala relays the expected value information to orbitofrontal cells that receive inputs from anterior inferotemporal cells, and medial orbitofrontal cells that receive inputs from rhinal cortex. The activations of these orbitofrontal cells code the subjective values of objects. These values guide behavioral choices. The model basal ganglia detect errors in CS-specific predictions of the value and timing of rewards. Excitatory inputs from the pedunculopontine nucleus interact with timed inhibitory inputs from model striosomes in the ventral striatum to regulate dopamine burst and dip responses from cells in the substantia nigra pars compacta and ventral tegmental area. Learning in cortical and striatal regions is strongly modulated by dopamine. The model is used to address tasks that examine food-specific satiety, Pavlovian conditioning, reinforcer devaluation, and simultaneous visual discrimination. Model simulations successfully reproduce discharge dynamics of known cell types, including signals that predict saccadic reaction times and CS-dependent changes in systolic blood pressure.Defense Advanced Research Projects Agency and the Office of Naval Research (N00014-95-1-0409); National Institutes of Health (R29-DC02952, R01-DC007683); National Science Foundation (IIS-97-20333, SBE-0354378); Office of Naval Research (N00014-01-1-0624

Spatially structured oscillations in a two-dimensional excitatory neuronal network with synaptic depression

We study the spatiotemporal dynamics of a two-dimensional excitatory neuronal network with synaptic depression. Coupling between populations of neurons is taken to be nonlocal, while depression is taken to be local and presynaptic. We show that the network supports a wide range of spatially structured oscillations, which are suggestive of phenomena seen in cortical slice experiments and in vivo. The particular form of the oscillations depends on initial conditions and the level of background noise. Given an initial, spatially localized stimulus, activity evolves to a spatially localized oscillating core that periodically emits target waves. Low levels of noise can spontaneously generate several pockets of oscillatory activity that interact via their target patterns. Periodic activity in space can also organize into spiral waves, provided that there is some source of rotational symmetry breaking due to external stimuli or noise. In the high gain limit, no oscillatory behavior exists, but a transient stimulus can lead to a single, outward propagating target wave

A roadmap to integrate astrocytes into Systems Neuroscience.

Systems neuroscience is still mainly a neuronal field, despite the plethora of evidence supporting the fact that astrocytes modulate local neural circuits, networks, and complex behaviors. In this article, we sought to identify which types of studies are necessary to establish whether astrocytes, beyond their well-documented homeostatic and metabolic functions, perform computations implementing mathematical algorithms that sub-serve coding and higher-brain functions. First, we reviewed Systems-like studies that include astrocytes in order to identify computational operations that these cells may perform, using Ca2+ transients as their encoding language. The analysis suggests that astrocytes may carry out canonical computations in a time scale of subseconds to seconds in sensory processing, neuromodulation, brain state, memory formation, fear, and complex homeostatic reflexes. Next, we propose a list of actions to gain insight into the outstanding question of which variables are encoded by such computations. The application of statistical analyses based on machine learning, such as dimensionality reduction and decoding in the context of complex behaviors, combined with connectomics of astrocyte-neuronal circuits, is, in our view, fundamental undertakings. We also discuss technical and analytical approaches to study neuronal and astrocytic populations simultaneously, and the inclusion of astrocytes in advanced modeling of neural circuits, as well as in theories currently under exploration such as predictive coding and energy-efficient coding. Clarifying the relationship between astrocytic Ca2+ and brain coding may represent a leap forward toward novel approaches in the study of astrocytes in health and disease