A circuit for heading direction estimation in the zebrafish anterior hindbrain

To successfully navigate their environment, animals may generate an inter- nal representation of the environment that can be updated based on sensory cues or internally generated motor commands. Head-direction cells, neu- rons that fire when the animal faces a particular direction in space have been recorded in various areas of the vertebrate brain. The dynamics of heading direction circuits are well by described by ring attractor networks, where a ring attractor organizes the activity of the circuit and positions along the ring represent the heading direction. Although this model has found remarkable validation in the invertebrate central complex, the anatomical dissection of a ring attractor circuit has been elusive in the vertebrate brain. Here, I report experimental observations in the larval zebrafish that high- light a possible role of the interpenducular nucleus (IPN) and a connected area, the anterior hindbrain, in generating heading direction-related signals. The internal organization of the interpeduncular nucleus is poorly un- derstood. In the first part of this thesis, I will present anatomical recon- structions that provide crucial insights in the organization of this struc- ture in the larval zebrafish. I will show that 1) the internal circuitry of the ventral IPN is organized in a fix number of glomeruli, domains of neuropil that receive dense and segregated dendritic and axonal arborizations and exhaustively tile the ventral IPN; and that 2) neurons in the anterior hind- brain dorsal from the interpeduncular nucleus contribute many dendritic and axonal projections to the IPN neuropil. In the second part of the thesis, I will describe a population of r1π neu- rons in the anterior hindbrain that exhibit a highly constrained dynamics lying on a ring manifold in the phase space of the network. Intriguingly, clock- and counterclock-wise shifts along this manifold correspond to left and right movements of the fish, so that the network state can keep track of current heading direction. The dynamics of the network full-fills several criteria that define a head-direction network: 1) There is a sustained and unique bump of activity that translates across the network (uniqueness); 2) the activity shifts in opposite directions when the animal perform leftward and rightward movements (integration); 3) activation of the network is sta- ble over tens of seconds in the absence of motion (persistence). Finally, I will turn back to the anatomy of r1π neurons and show how they could connect with each other in the IPN according to their proximity in activity space, and I will conclude by proposing a mechanistic model for the organization of the ring network dynamics. Together, these data repre- sent the first observation of a head-direction network with an anatomical organization in the vertebrate brain

Brain Dynamics and Plastic Deformation of Self Circuitries in the Dementia Patient

Despite improved medical care that has resulted in greatly extended life expectancies, significant increases in numbers of individuals suffering age related cognitive defects is expected, making the improved understanding of normal and pathological aging an important priority. Current studies indicating that brain activity requires a dynamical architecture to preserve functional order in the face of persistent and extraneous activity suggests that cognitive impairments are likely to be closely linked to dysfunctional dynamical activity of brain systems. Cognitive impairments such as those introduced by Alzheimer’s dementia (AD), that affect fundamental operational constructs like the self, are thus likely to implicate global dynamics that oversee whole brain operation. This paper explores plastic events associated with dynamical elements used in the normal construction of the self percept and the etiology of their deconstruction in the course of AD. It is proposed that the evolution of the disease involves the increasing impairment of a global dynamical operation that is normally engaged in forming a stable and coherent self image needed to flexibly engage task related, motor plans and effectors

Neuron

Sexually dimorphic behaviors, qualitative or quantitative differences in behaviors between the sexes, result from the activity of a sexually differentiated nervous system. Sensory cues and sex hormones control the entire repertoire of sexually dimorphic behaviors, including those commonly thought to be charged with emotion such as courtship and aggression. Such overarching control mechanisms regulate distinct genes and neurons that in turn specify the display of these behaviors in a modular manner. How such modular control is transformed into cohesive internal states that correspond to sexually dimorphic behavior is poorly understood. We summarize current understanding of the neural circuit control of sexually dimorphic behaviors from several perspectives, including how neural circuits in general, and sexually dimorphic neurons in particular, can generate sexually dimorphic behaviors, and how molecular mechanisms and evolutionary constraints shape these behaviors. We propose that emergent themes such as the modular genetic and neural control of dimorphic behavior are broadly applicable to the neural control of other behaviors.DP1 MH099900/MH/NIMH NIH HHS/United StatesDP1MH099900/DP/NCCDPHP CDC HHS/United StatesR01 AA010035/AA/NIAAA NIH HHS/United StatesR01 NS049488/NS/NINDS NIH HHS/United StatesR01 NS083872/NS/NINDS NIH HHS/United StatesR01AA010035/AA/NIAAA NIH HHS/United StatesR01NS049488/NS/NINDS NIH HHS/United StatesR01NS083872/NS/NINDS NIH HHS/United States2015-04-16T00:00:00Z24742456PMC413017

Neural Computations Underpinning Anxiety in Health and Disease

University of Minnesota Ph.D. dissertation. February 2021. Major: Neuroscience. Advisor: David Redish. 1 computer file (PDF); viii, 210 pages.Motivational conflict is thought to take one of three possible forms: approach-approach conflict,avoid-approach conflict, and avoid-avoid conflict. While approach-approach conflict paradigms have primarily been used to study the neural basis of reward-based decision-making, avoid-approach conflict paradigms are typically used to model anxiety because they capture the complex, bivalent nature of most naturalistic environments. Research suggests that approach-approach conflict initiates a distinct neural algorithm: a hippocampally-mediated mental simulation of the future that is paired with evaluations of anticipated outcomes. However, it is unknown whether a similar form of episodic future thinking also occurs during avoid-approach conflict. Here I present research I have conducted to address this gap in the literature. First, I used apharmacological approach in tandem with a semi-naturalistic avoid-approach predator-inhabited foraging arena task to show that anxiety-like hesitation behaviors are attenuated by anxiolytic drugs. I then modeled these hesitation behaviors as a belief-state updating loop using a partially observable Markov decision process that involves fictive representations of potential future outcomes. Next, I explored the neural representations underpinning these anxiety-like behaviors, aiming to determine whether non-local representations occur during periods of anxious conflict. To do this, I recorded from ensembles of neurons in dorsal hippocampal layer CA1 of rats as they freely behaved in the predator-inhabited foraging arena task. I identified distinct hippocampal fictive representations that co-occurred with two anxiety-like behaviors: (1) forward sequences during choice-point hesitation that shifted from representing reward in a safe environment to representing reward and threat in a dangerous environment, and (2) discrete representations of threat during a change-of-mind behavior. Altogether, these results support the view that anxiety resulting from avoid-approach conflictinvolves representations of hypothetical scenarios and that these fictive representations are, at least in part, neurally encoded in the hippocampus. These data highlight hippocampal fictive representations as a potential target for the treatment of anxiety disorders

Curr Opin Neurobiol

The dense connectivity in the brain means that one neuron's activity can influence many others. To observe this interconnected system comprehensively, an aspiration within neuroscience is to record from as many neurons as possible at the same time. There are two useful routes toward this goal: one is to expand the spatial extent of functional imaging techniques, and the second is to use animals with small brains. Here we review recent progress toward imaging many neurons and complete populations of identified neurons in small vertebrates and invertebrates.DP1 NS082121/DP/NCCDPHP CDC HHS/United StatesDP1 NS082121/NS/NINDS NIH HHS/United StatesDP1 OD008240/OD/NIH HHS/United StatesR01DA030304/DA/NIDA NIH HHS/United StatesR24 NS086601/NS/NINDS NIH HHS/United StatesU01 NS090449/NS/NINDS NIH HHS/United StatesHoward Hughes Medical Institute/United States2016-07-21T00:00:00Z25636154PMC495559

A circuit mechanism for decision-making biases and NMDA receptor hypofunction

Decision-making biases can be features of normal behaviour, or deficits underlying neuropsychiatric symptoms. We used behavioural psychophysics, spiking-circuit modelling and pharmacological manipulations to explore decision-making biases during evidence integration. Monkeys showed a pro-variance bias (PVB): a preference to choose options with more variable evidence. The PVB was also present in a spiking circuit model, revealing a potential neural mechanism for this behaviour. To model possible effects of NMDA receptor (NMDA-R) antagonism on this behaviour, we simulated the effects of NMDA-R hypofunction onto either excitatory or inhibitory neurons in the model. These were then tested experimentally using the NMDA-R antagonist ketamine, a pharmacological model of schizophrenia. Ketamine yielded an increase in subjects' PVB, consistent with lowered cortical excitation/inhibition balance from NMDA-R hypofunction predominantly onto excitatory neurons. These results provide a circuit-level mechanism that bridges across explanatory scales, from the synaptic to the behavioural, in neuropsychiatric disorders where decision-making biases are prominent

Development of a Large-Scale Integrated Neurocognitive Architecture Part 1: Conceptual Framework

The idea of creating a general purpose machine intelligence that captures many of the features of human cognition goes back at least to the earliest days of artificial intelligence and neural computation. In spite of more than a half-century of research on this issue, there is currently no existing approach to machine intelligence that comes close to providing a powerful, general-purpose human-level intelligence. However, substantial progress made during recent years in neural computation, high performance computing, neuroscience and cognitive science suggests that a renewed effort to produce a general purpose and adaptive machine intelligence is timely, likely to yield qualitatively more powerful approaches to machine intelligence than those currently existing, and certain to lead to substantial progress in cognitive science, AI and neural computation. In this report, we outline a conceptual framework for the long-term development of a large-scale machine intelligence that is based on the modular organization, dynamics and plasticity of the human brain. Some basic design principles are presented along with a review of some of the relevant existing knowledge about the neurobiological basis of cognition. Three intermediate-scale prototypes for parts of a larger system are successfully implemented, providing support for the effectiveness of several of the principles in our framework. We conclude that a human-competitive neuromorphic system for machine intelligence is a viable long- term goal, but that for the short term, substantial integration with more standard symbolic methods as well as substantial research will be needed to make this goal achievable