635 research outputs found
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Neuromodulatory control of localized dendritic spiking in critical period cortex.
Sensory experience in early postnatal life, during so-called critical periods, restructures neural circuitry to enhance information processing1. Why the cortex is susceptible to sensory instruction in early life and why this susceptibility wanes with age are unclear. Here we define a developmentally restricted engagement of inhibitory circuitry that shapes localized dendritic activity and is needed for vision to drive the emergence of binocular visual responses in the mouse primary visual cortex. We find that at the peak of the critical period for binocular plasticity, acetylcholine released from the basal forebrain during periods of heightened arousal directly excites somatostatin (SST)-expressing interneurons. Their inhibition of pyramidal cell dendrites and of fast-spiking, parvalbumin-expressing interneurons enhances branch-specific dendritic responses and somatic spike rates within pyramidal cells. By adulthood, this cholinergic sensitivity is lost, and compartmentalized dendritic responses are absent but can be re-instated by optogenetic activation of SST cells. Conversely, suppressing SST cell activity during the critical period prevents the normal development of binocular receptive fields by impairing the maturation of ipsilateral eye inputs. This transient cholinergic modulation of SST cells, therefore, seems to orchestrate two features of neural plasticity-somatic disinhibition and compartmentalized dendritic spiking. Loss of this modulation may contribute to critical period closure
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State-Dependent Subnetworks of Parvalbumin-Expressing Interneurons in Neocortex.
Brain state determines patterns of spiking output that underlie behavior. In neocortex, brain state is reflected in the spontaneous activity of the network, which is regulated in part by neuromodulatory input from the brain stem and by local inhibition. We find that fast-spiking, parvalbumin-expressing inhibitory neurons, which exert state-dependent control of network gain and spike patterns, cluster into two stable and functionally distinct subnetworks that are differentially engaged by ascending neuromodulation. One group is excited as a function of increased arousal state; this excitation is driven in part by the increase in cortical norepinephrine that occurs when the locus coeruleus is active. A second group is suppressed during movement when acetylcholine is released into the cortex via projections from the nucleus basalis. These data establish the presence of functionally independent subnetworks of Parvalbumin (PV) cells in the upper layers of the neocortex that are differentially engaged by the ascending reticular activating system
Feature detection using spikes: the greedy approach
A goal of low-level neural processes is to build an efficient code extracting
the relevant information from the sensory input. It is believed that this is
implemented in cortical areas by elementary inferential computations
dynamically extracting the most likely parameters corresponding to the sensory
signal. We explore here a neuro-mimetic feed-forward model of the primary
visual area (VI) solving this problem in the case where the signal may be
described by a robust linear generative model. This model uses an over-complete
dictionary of primitives which provides a distributed probabilistic
representation of input features. Relying on an efficiency criterion, we derive
an algorithm as an approximate solution which uses incremental greedy inference
processes. This algorithm is similar to 'Matching Pursuit' and mimics the
parallel architecture of neural computations. We propose here a simple
implementation using a network of spiking integrate-and-fire neurons which
communicate using lateral interactions. Numerical simulations show that this
Sparse Spike Coding strategy provides an efficient model for representing
visual data from a set of natural images. Even though it is simplistic, this
transformation of spatial data into a spatio-temporal pattern of binary events
provides an accurate description of some complex neural patterns observed in
the spiking activity of biological neural networks.Comment: This work links Matching Pursuit with bayesian inference by providing
the underlying hypotheses (linear model, uniform prior, gaussian noise
model). A parallel with the parallel and event-based nature of neural
computations is explored and we show application to modelling Primary Visual
Cortex / image processsing.
http://incm.cnrs-mrs.fr/perrinet/dynn/LaurentPerrinet/Publications/Perrinet04tau
When your eyes see more than you do
SummaryVisual information is used by the brain to construct a conscious experience of the visual world and to guide motor actions [1]. Here we report a study of how eye movements and perception relate to each other. We compared the ability of human observers to perceive image motion with the reliability of their eyes to track the motion of a target [2–4], the goal being to test whether both motor and sensory processes are based on the same set of signals and limited by a shared source of noise [2,4]. We found that the oculomotor system can detect fluctuations in the velocity of a moving target better than the observer. Surprisingly, in some conditions, eye movements reliably respond to the velocity fluctuations of a moving target that are otherwise perceptually invisible to the subjects. The implication is that visual motion signals exist in the brain that can be used to guide motor actions without evoking a perceptual outcome nor being accessible to conscious scrutiny
Theta Motion Processing in Fruit Flies
The tiny brains of insects presumably impose significant computational limitations on algorithms controlling their behavior. Nevertheless, they perform fast and sophisticated visual maneuvers. This includes tracking features composed of second-order motion, in which the feature is defined by higher-order image statistics, but not simple correlations in luminance. Flies can track the true direction of even theta motions, in which the first-order (luminance) motion is directed opposite the second-order moving feature. We exploited this paradoxical feature tracking response to dissect the particular image properties that flies use to track moving objects. We find that theta motion detection is not simply a result of steering toward any spatially restricted flicker. Rather, our results show that fly high-order feature tracking responses can be broken down into positional and velocity components – in other words, the responses can be modeled as a superposition of two independent steering efforts. We isolate these elements to show that each has differing influence on phase and amplitude of steering responses, and together they explain the time course of second-order motion tracking responses during flight. These observations are relevant to natural scenes, where moving features can be much more complex
An inhibitory pull-push circuit in frontal cortex.
Push-pull is a canonical computation of excitatory cortical circuits. By contrast, we identify a pull-push inhibitory circuit in frontal cortex that originates in vasoactive intestinal polypeptide (VIP)-expressing interneurons. During arousal, VIP cells rapidly and directly inhibit pyramidal neurons; VIP cells also indirectly excite these pyramidal neurons via parallel disinhibition. Thus, arousal exerts a feedback pull-push influence on excitatory neurons-an inversion of the canonical push-pull of feedforward input
Semantic learning in autonomously active recurrent neural networks
The human brain is autonomously active, being characterized by a
self-sustained neural activity which would be present even in the absence of
external sensory stimuli. Here we study the interrelation between the
self-sustained activity in autonomously active recurrent neural nets and
external sensory stimuli.
There is no a priori semantical relation between the influx of external
stimuli and the patterns generated internally by the autonomous and ongoing
brain dynamics. The question then arises when and how are semantic correlations
between internal and external dynamical processes learned and built up?
We study this problem within the paradigm of transient state dynamics for the
neural activity in recurrent neural nets, i.e. for an autonomous neural
activity characterized by an infinite time-series of transiently stable
attractor states. We propose that external stimuli will be relevant during the
sensitive periods, {\it viz} the transition period between one transient state
and the subsequent semi-stable attractor. A diffusive learning signal is
generated unsupervised whenever the stimulus influences the internal dynamics
qualitatively.
For testing we have presented to the model system stimuli corresponding to
the bars and stripes problem. We found that the system performs a non-linear
independent component analysis on its own, being continuously and autonomously
active. This emergent cognitive capability results here from a general
principle for the neural dynamics, the competition between neural ensembles.Comment: Journal of Algorithms in Cognition, Informatics and Logic, special
issue on `Perspectives and Challenges for Recurrent Neural Networks', in
pres
BOLD human responses to chromatic spatial features
Animal physiological and human psychophysical studies suggest that an early step in visual processing involves the detection and identification of features such as lines and edges, by neural mechanisms with even- and odd-symmetric receptive fields. Functional imaging studies also demonstrate mechanisms with even- and odd-receptive fields in early visual areas, in response to luminance-modulated stimuli. In this study we measured fMRI BOLD responses to 2-D stimuli composed of only even or only odd symmetric features, and to an amplitude-matched random noise control, modulated in red-green equiluminant colour contrast. All these stimuli had identical power but different phase spectra, either highly congruent (even or odd symmetry stimuli) or random (noise). At equiluminance, V1 BOLD activity showed no preference between congruent- and random-phase stimuli, as well as no preference between even and odd symmetric stimuli. Areas higher in the visual hierarchy, both along the dorsal pathway (caudal part of the intraparietal sulcus, dorsal LO and V3A) and the ventral pathway (V4), responded preferentially to odd symmetry over even symmetry stimuli, and to congruent over random phase stimuli. Interestingly, V1 showed an equal increase in BOLD activity at each alternation between stimuli of different symmetry, suggesting the existence of specialised mechanisms for the detection of edges and lines such as even- and odd-chromatic receptive fields. Overall the results indicate a high selectivity of colour-selective neurons to spatial phase along both the dorsal and the ventral pathways in humans
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