5,663 research outputs found
Cell assembly dynamics of sparsely-connected inhibitory networks: a simple model for the collective activity of striatal projection neurons
Striatal projection neurons form a sparsely-connected inhibitory network, and
this arrangement may be essential for the appropriate temporal organization of
behavior. Here we show that a simplified, sparse inhibitory network of
Leaky-Integrate-and-Fire neurons can reproduce some key features of striatal
population activity, as observed in brain slices [Carrillo-Reid et al., J.
Neurophysiology 99 (2008) 1435{1450]. In particular we develop a new metric to
determine the conditions under which sparse inhibitory networks form
anti-correlated cell assemblies with time-varying activity of individual cells.
We found that under these conditions the network displays an input-specific
sequence of cell assembly switching, that effectively discriminates similar
inputs. Our results support the proposal [Ponzi and Wickens, PLoS Comp Biol 9
(2013) e1002954] that GABAergic connections between striatal projection neurons
allow stimulus-selective, temporally-extended sequential activation of cell
assemblies. Furthermore, we help to show how altered intrastriatal GABAergic
signaling may produce aberrant network-level information processing in
disorders such as Parkinson's and Huntington's diseases.Comment: 22 pages, 9 figure
Relating macroscopic measures of brain activity to fast dynamic neuronal interactions
The aim of this thesis was to find a systematic relationship between neuronal synchrony and firing rates, that would enable us to make inferences about one given knowledge of the other. Functional neuroimaging techniques, such as functional magnetic resonance imaging (fMRI), are sensitive to changes in overall population synaptic activity, that can be interpreted in terms of rate coding for a particular stimulus or task. Characterising the relationship between synchrony and firing rates would facilitate inferences about fast neuronal interactions on the basis of macroscopic measures such as those obtained by fMRI. In this thesis, we used computer simulations of neuronal networks and fMRI in humans to investigate the relationship between mean synaptic activity and fast synchronous neuronal interactions. We found that the extent to which different neurons engage in fast dynamic interactions is largely dependent on the neuronal population firing rates and vice versa, i.e. as one metric changes (either activity or synchrony), so does the other. Additionally, as a result of the strong coupling between overall activity and neuronal synchrony, there is also a robust relationship between background activity and stimulus-evoked activity: Increased background activity increases the gain of the neurons, by decreasing effective membrane time constants, and enhancing stimulus-evoked population activity through the selection of fast synchronous dynamics. In concluding this thesis, we tested and confirmed, with fMRI in humans, that this mechanism may account for attentional modulation, i.e. the change in baseline neuronal firing rates associated with attention, in cell assemblies selectively responding to an attended sensory attribute, enhances responses elicited by presentation of that attribute
Common and Distinct Functional Brain Networks for Intuitive and Deliberate Decision Making
Reinforcement learning studies in rodents and primates demonstrate that goal-directed and habitual choice behaviors are mediated through different fronto-striatal systems, but the evidence is less clear in humans. In this study, functional magnetic resonance imaging (fMRI) data were collected whilst participants ( n = 20) performed a conditional associative learning task in which blocks of novel conditional stimuli (CS) required a deliberate choice, and blocks of familiar CS required an intuitive choice. Using standard subtraction analysis for fMRI event-related designs, activation shifted from the dorso-fronto-parietal network, which involves dorsolateral prefrontal cortex (DLPFC) for deliberate choice of novel CS, to ventro-medial frontal (VMPFC) and anterior cingulate cortex for intuitive choice of familiar CS. Supporting this finding, psycho-physiological interaction (PPI) analysis, using the peak active areas within the PFC for novel and familiar CS as seed regions, showed functional coupling between caudate and DLPFC when processing novel CS and VMPFC when processing familiar CS. These findings demonstrate separable systems for deliberate and intuitive processing, which is in keeping with rodent and primate reinforcement learning studies, although in humans they operate in a dynamic, possibly synergistic, manner particularly at the level of the striatum.Peer reviewedFinal Published versio
Fractals in the Nervous System: conceptual Implications for Theoretical Neuroscience
This essay is presented with two principal objectives in mind: first, to
document the prevalence of fractals at all levels of the nervous system, giving
credence to the notion of their functional relevance; and second, to draw
attention to the as yet still unresolved issues of the detailed relationships
among power law scaling, self-similarity, and self-organized criticality. As
regards criticality, I will document that it has become a pivotal reference
point in Neurodynamics. Furthermore, I will emphasize the not yet fully
appreciated significance of allometric control processes. For dynamic fractals,
I will assemble reasons for attributing to them the capacity to adapt task
execution to contextual changes across a range of scales. The final Section
consists of general reflections on the implications of the reviewed data, and
identifies what appear to be issues of fundamental importance for future
research in the rapidly evolving topic of this review
Information flow through a model of the C. elegans klinotaxis circuit
Understanding how information about external stimuli is transformed into
behavior is one of the central goals of neuroscience. Here we characterize the
information flow through a complete sensorimotor circuit: from stimulus, to
sensory neurons, to interneurons, to motor neurons, to muscles, to motion.
Specifically, we apply a recently developed framework for quantifying
information flow to a previously published ensemble of models of salt
klinotaxis in the nematode worm C. elegans. The models are grounded in the
neuroanatomy and currently known neurophysiology of the worm. The unknown model
parameters were optimized to reproduce the worm's behavior. Information flow
analysis reveals several key principles underlying how the models operate: (1)
Interneuron class AIY is responsible for integrating information about positive
and negative changes in concentration, and exhibits a strong left/right
information asymmetry. (2) Gap junctions play a crucial role in the transfer of
information responsible for the information symmetry observed in interneuron
class AIZ. (3) Neck motor neuron class SMB implements an information gating
mechanism that underlies the circuit's state-dependent response. (4) The neck
carries non-uniform distribution about changes in concentration. Thus, not all
directions of movement are equally informative. Each of these findings
corresponds to an experimental prediction that could be tested in the worm to
greatly refine our understanding of the neural circuit underlying klinotaxis.
Information flow analysis also allows us to explore how information flow
relates to underlying electrophysiology. Despite large variations in the neural
parameters of individual circuits, the overall information flow architecture
circuit is remarkably consistent across the ensemble, suggesting that
information flow analysis captures general principles of operation for the
klinotaxis circuit
Adaptive networks for robotics and the emergence of reward anticipatory circuits
Currently the central challenge facing evolutionary robotics is to determine
how best to extend the range and complexity of behaviour supported by evolved
neural systems. Implicit in the work described in this thesis is the idea that this
might best be achieved through devising neural circuits (tractable to evolutionary
exploration) that exhibit complementary functional characteristics. We concentrate
on two problem domains; locomotion and sequence learning. For locomotion
we compare the use of GasNets and other adaptive networks. For sequence learning
we introduce a novel connectionist model inspired by the role of dopamine
in the basal ganglia (commonly interpreted as a form of reinforcement learning).
This connectionist approach relies upon a new neuron model inspired by notions
of energy efficient signalling. Two reward adaptive circuit variants were investigated.
These were applied respectively to two learning problems; where action
sequences are required to take place in a strict order, and secondly, where action
sequences are robust to intermediate arbitrary states. We conclude the thesis
by proposing a formal model of functional integration, encompassing locomotion
and sequence learning, extending ideas proposed by W. Ross Ashby.
A general model of the adaptive replicator is presented, incoporating subsystems
that are tuned to continuous variation and discrete or conditional events.
Comparisons are made with Ross W. Ashby's model of ultrastability and his
ideas on adaptive behaviour. This model is intended to support our assertion
that, GasNets (and similar networks) and reward adaptive circuits of the type
presented here, are intrinsically complementary. In conclusion we present some
ideas on how the co-evolution of GasNet and reward adaptive circuits might lead
us to significant improvements in the synthesis of agents capable of exhibiting
complex adaptive behaviour
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