Biobit: Computational Neuroscience & Biocomputation
2.7K views | +0 today
Follow
Your new post is loading...
Your new post is loading...
Scooped by Nima Dehghani
Scoop.it!

A computational perspective of the role of Thalamus in cognition

A computational perspective of the role of Thalamus in cognition | Biobit: Computational Neuroscience & Biocomputation | Scoop.it
Nima Dehghani's insight:
See the full paper here: https://arxiv.org/pdf/1803.00997.pdf
more...
No comment yet.
Scooped by Nima Dehghani
Scoop.it!

A neural algorithm for a fundamental computing problem

A neural algorithm for a fundamental computing problem | Biobit: Computational Neuroscience & Biocomputation | Scoop.it
Flies use an algorithmic neuronal strategy to sense and categorize odors. Dasgupta et al. applied insights from the fly system to come up with a solution to a computer science problem. On the basis of the algorithm that flies use to tag an odor and categorize similar ones, the authors generated a new solution to the nearest-neighbor search problem that underlies tasks such as searching for similar images on the web.

Science , this issue p. [793][1]

[1]: /lookup/doi/10.1126/science.aam9868
more...
No comment yet.
Scooped by Nima Dehghani
Scoop.it!

Neural field model to reconcile structure with function in primary visual cortex

Neural field model to reconcile structure with function in primary visual cortex | Biobit: Computational Neuroscience & Biocomputation | Scoop.it
Author Summary Optical imaging techniques can reveal the dynamical patterns of cortical activation that encode low-level visual features like position and orientation, which are shaped by both feed-forward projections, recurrent and long-range intra-cortical connections. Anatomical studies have characterized intra-cortical connections, however, it is non-trivial to predict from this data how evoked activity might spread across cortex. Indeed, there remains an apparent conflict between the reported orientation bias of cortical connections, and imaging studies on the propagation of cortical activity. Our study reconciles structure (anatomy) with function (evoked activity) using a dynamic neural field model that predicts the dynamics of cortical activation in a setting both inspired by and parametrically matched to the available anatomical data.
more...
No comment yet.
Scooped by Nima Dehghani
Scoop.it!

Inferring oscillatory modulation in neural spike trains

Inferring oscillatory modulation in neural spike trains | Biobit: Computational Neuroscience & Biocomputation | Scoop.it
Author summary Oscillatory modulation of neural activity in the brain is widely observed under conditions associated with a variety of cognitive tasks and mental states. Within individual neurons, oscillations may be uncovered in the moment-to-moment variation in neural firing rate. This, however, is often challenging because many factors may affect fluctuations in neural firing rate and, in addition, neurons fire irregular sets of action potentials, or spike trains, due to an unknown combination of meaningful signals and extraneous noise. We have devised a statistical Latent Oscillatory Spike Train (LOST) model with accompanying model-fitting technology, that is able to detect subtle oscillations in spike trains by taking into account both spiking noise and temporal variation in the oscillation itself. The method couples two techniques developed for other purposes in the literature on Bayesian analysis. Using data simulated from theoretical neurons and real data recorded from cortical motor neurons, we demonstrate the method’s ability to track changes in the modulatory structure of the oscillation across experimental trials.
more...
No comment yet.
Scooped by Nima Dehghani
Scoop.it!

Active cortical dendrites modulate perception

Active cortical dendrites modulate perception | Biobit: Computational Neuroscience & Biocomputation | Scoop.it
What determines the detection of a sensory stimulus? To address this question, Takahashi et al. combined in vivo two-photon imaging, electrophysiology, optogenetics, and behavioral analysis in a study of mice. Calcium signals in apical dendrites of pyramidal neurons in the somatosensory cortex controlled the perceptual threshold of the mice's whiskers. Strong reduction of dendritic calcium signaling impaired the perceptual detection threshold so that an identical stimulus could no longer be noticed.

Science , this issue p. [1587][1]

[1]: /lookup/doi/10.1126/science.aah6066
more...
No comment yet.
Scooped by Nima Dehghani
Scoop.it!

Modeling the rhythmic electrical activities of the brain | American Institute of Physics

Modeling the rhythmic electrical activities of the brain | American Institute of Physics | Biobit: Computational Neuroscience & Biocomputation | Scoop.it
more...
No comment yet.
Scooped by Nima Dehghani
Scoop.it!

Local field potentials primarily reflect inhibitory neuron activity in human and monkey cortex

Local field potentials primarily reflect inhibitory neuron activity in human and monkey cortex | Biobit: Computational Neuroscience & Biocomputation | Scoop.it
The local field potential (LFP) is generated by large populations of neurons, but unitary contribution of spiking neurons to LFP is not well characterised.
more...
No comment yet.
Scooped by Nima Dehghani
Scoop.it!

Uncovering representations of sleep-associated hippocampal ensemble spike activity

Uncovering representations of sleep-associated hippocampal ensemble spike activity | Biobit: Computational Neuroscience & Biocomputation | Scoop.it
Pyramidal neurons in the rodent hippocampus exhibit spatial tuning during spatial navigation, and they are reactivated in specific temporal order during sharp-wave ripples observed in quiet wakefulness or slow wave sleep.
Nima Dehghani's insight:
more...
No comment yet.
Scooped by Nima Dehghani
Scoop.it!

High-frequency oscillations in human and monkey neocortex during the wake–sleep cycle

High-frequency oscillations in human and monkey neocortex during the wake–sleep cycle | Biobit: Computational Neuroscience & Biocomputation | Scoop.it
National Academy of Sciences
Nima Dehghani's insight:
more...
No comment yet.
Scooped by Nima Dehghani
Scoop.it!

Neural mechanisms of transient neocortical beta rhythms: Converging evidence from humans, computational modeling, monkeys, and mice

Neural mechanisms of transient neocortical beta rhythms: Converging evidence from humans, computational modeling, monkeys, and mice | Biobit: Computational Neuroscience & Biocomputation | Scoop.it
National Academy of Sciences
more...
No comment yet.
Scooped by Nima Dehghani
Scoop.it!

The Free Energy Requirements of Biological Organisms; Implications for Evolution

The Free Energy Requirements of Biological Organisms; Implications for Evolution | Biobit: Computational Neuroscience & Biocomputation | Scoop.it
Recent advances in nonequilibrium statistical physics have provided unprecedented insight into the thermodynamics of dynamic processes. The author recently used these advances to extend Landauer’s semi-formal reasoning concerning the thermodynamics of bit erasure, to derive the minimal free energy required to implement an arbitrary computation. Here, I extend this analysis, deriving the minimal free energy required by an organism to run a given (stochastic) map π from its sensor inputs to its actuator outputs. I use this result to calculate the input-output map π of an organism that optimally trades off the free energy needed to run π with the phenotypic fitness that results from implementing π. I end with a general discussion of the limits imposed on the rate of the terrestrial biosphere’s information processing by the flux of sunlight on the Earth.
more...
No comment yet.
Scooped by Nima Dehghani
Scoop.it!

Birds have primate-like numbers of neurons in the forebrain

Birds have primate-like numbers of neurons in the forebrain | Biobit: Computational Neuroscience & Biocomputation | Scoop.it
more...
No comment yet.
Scooped by Nima Dehghani
Scoop.it!

Local field potentials primarily reflect inhibitory neuron activity in human and monkey cortex

Local field potentials primarily reflect inhibitory neuron activity in human and monkey cortex | Biobit: Computational Neuroscience & Biocomputation | Scoop.it
bioRxiv - the preprint server for biology, operated by Cold Spring Harbor Laboratory, a research and educational institution
more...
No comment yet.
Scooped by Nima Dehghani
Scoop.it!

Biological Mechanisms for Learning: A Computational Model of Olfactory Learning in the Manduca sexta Moth, with Applications to Neural Nets

Biological Mechanisms for Learning: A Computational Model of Olfactory Learning in the Manduca sexta Moth, with Applications to Neural Nets | Biobit: Computational Neuroscience & Biocomputation | Scoop.it
more...
No comment yet.
Scooped by Nima Dehghani
Scoop.it!

Lattice system of functionally distinct cell types in the neocortex

Lattice system of functionally distinct cell types in the neocortex | Biobit: Computational Neuroscience & Biocomputation | Scoop.it
The fundamental organization of excitatory and inhibitory neurons in the neocortex is still poorly understood. Subcerebral projection neurons, a major excitatory cell type in neocortical layer 5, form small cell clusters called microcolumns. Maruoka et al. examined large regions of mouse brain layer 5 and observed that thousands of these microcolumns make up a hexagonal lattice with a regular gridlike spacing. The other major layer 5 excitatory cell class, cortical projection neurons, also form microcolumns that interdigitate with those of the subcerebral projection neurons. Microcolumns received common presynaptic inputs and showed synchronized activity in many cortical areas. These microcolumns developed from nonsister neurons coupled by cell type–specific gap junctions, suggesting that their development is lineage-independent but guided by local electrical transmission.

Science , this issue p. [610][1]

[1]: /lookup/doi/10.1126/science.aam6125
more...
No comment yet.
Scooped by Nima Dehghani
Scoop.it!

Pyramidal Cell-Interneuron Circuit Architecture and Dynamics in Hippocampal Networks

Pyramidal Cell-Interneuron Circuit Architecture and Dynamics in Hippocampal Networks | Biobit: Computational Neuroscience & Biocomputation | Scoop.it
English, McKenzie, et al. identify, validate, and quantify monosynaptic connections
between pyramidal cells and interneurons, using the spike timing of pre- and postsynaptic
neurons in vivo. Their large-scale method uncovers a backbone of connectivity rules
in the hippocampus CA1 circuit.
more...
No comment yet.
Scooped by Nima Dehghani
Scoop.it!

Modeling the mammalian sleep cycle

Modeling the mammalian sleep cycle | Biobit: Computational Neuroscience & Biocomputation | Scoop.it
During sleep, the mammalian brain transitions through repeated cycles of non-rapid-eye-movement (NREM) and rapid-eye-movement (REM) sleep. The physiological implementation of this slow ultradian brain rhythm is largely unknown. Two differing dynamical mechanisms have been proposed to underlie the NREM–REM cycle. The first model type relies on reciprocal interactions between inhibitory and excitatory neural populations resulting in stable limit cycle oscillations. Recent experimental findings instead favor a model, in which mutually inhibitory interactions between REM sleep-promoting (REM-on) and REM sleep-suppressing (REM-off) neural populations stabilize the brain state. Slow modulations in the neural excitability, that are hypothesized to reflect the homeostatic need for REM sleep, abruptly switch the brain in and out of REM sleep.
more...
No comment yet.
Scooped by Nima Dehghani
Scoop.it!

Stochastic Thermodynamics of Learning

Stochastic Thermodynamics of Learning | Biobit: Computational Neuroscience & Biocomputation | Scoop.it
Virtually every organism gathers information about its noisy environment and builds models from those data, mostly using neural networks. Here, we use stochastic thermodynamics to analyze the learning of a classification rule by a neural network. We show that the information acquired by the network is bounded by the thermodynamic cost of learning and introduce a learning efficiency $\ensuremath{\eta}\ensuremath{\le}1$. We discuss the conditions for optimal learning and analyze Hebbian learning in the thermodynamic limit.
more...
No comment yet.
Scooped by Nima Dehghani
Scoop.it!

Origins of Intelligence in Systems of Evolving Adapting Agents by Jim Crutchfield

Jim Crutchfield at FQXi's 5th International Conference
more...
No comment yet.
Scooped by Nima Dehghani
Scoop.it!

Theoretical principles of multiscale spatiotemporal control of neuronal networks: a complex systems perspective

bioRxiv - the preprint server for biology, operated by Cold Spring Harbor Laboratory, a research and educational institution
more...
No comment yet.
Scooped by Nima Dehghani
Scoop.it!

Imprinting and recalling cortical ensembles

Imprinting and recalling cortical ensembles | Biobit: Computational Neuroscience & Biocomputation | Scoop.it
Donald Hebb's hypothesis that coactivation of neurons leads to the formation of ensembles of neurons has inspired neuroscientists for decades. The experimental creation of such ensembles has been technically challenging. Using two-photon optogenetic stimulation with single-cell resolution, Carrillo-Reid et al. discovered that recurrent activation of a group of neurons creates an ensemble that is imprinted in the brain circuitry. Activation of a single neuron can lead to recall of the entire ensemble in a phenomenon called pattern completion. The artificial ensemble persists over days and can be reactivated at later time points without interfering with endogenous circuitry.

Science , this issue p. [691][1]

[1]: /lookup/doi/10.1126/science.aaf7560
more...
No comment yet.
Scooped by Nima Dehghani
Scoop.it!

Operation of a homeostatic sleep switch : Nature : Nature Research

Operation of a homeostatic sleep switch : Nature : Nature Research | Biobit: Computational Neuroscience & Biocomputation | Scoop.it
Sleep disconnects animals from the external world, at considerable risks and costs that must be offset by a vital benefit. Insight into this mysterious benefit will come from understanding sleep homeostasis: to monitor sleep need, an internal bookkeeper must track physiological changes that are linked to the core function of sleep. In Drosophila, a crucial component of the machinery for sleep homeostasis is a cluster of neurons innervating the dorsal fan-shaped body (dFB) of the central complex. Artificial activation of these cells induces sleep, whereas reductions in excitability cause insomnia. dFB neurons in sleep-deprived flies tend to be electrically active, with high input resistances and long membrane time constants, while neurons in rested flies tend to be electrically silent. Correlative evidence thus supports the simple view that homeostatic sleep control works by switching sleep-promoting neurons between active and quiescent states. Here we demonstrate state switching by dFB neurons, identify dopamine as a neuromodulator that operates the switch, and delineate the switching mechanism. Arousing dopamine caused transient hyperpolarization of dFB neurons within tens of milliseconds and lasting excitability suppression within minutes. Both effects were transduced by Dop1R2 receptors and mediated by potassium conductances. The switch to electrical silence involved the downregulation of voltage-gated A-type currents carried by Shaker and Shab, and the upregulation of voltage-independent leak currents through a two-pore-domain potassium channel that we term Sandman. Sandman is encoded by the CG8713 gene and translocates to the plasma membrane in response to dopamine. dFB-restricted interference with the expression of Shaker or Sandman decreased or increased sleep, respectively, by slowing the repetitive discharge of dFB neurons in the ON state or blocking their entry into the OFF state. Biophysical changes in a small population of neurons are thus linked to the control of sleep–wake state.
more...
No comment yet.
Scooped by Nima Dehghani
Scoop.it!

Energy and time determine scaling in biological and computer designs

Energy and time determine scaling in biological and computer designs | Biobit: Computational Neuroscience & Biocomputation | Scoop.it
Metabolic rate in animals and power consumption in computers are analogous quantities that scale similarly with size. We analyse vascular systems of mammals and on-chip networks of microprocessors, where natural selection and human engineering, respectively, have produced systems that minimize both energy dissipation and delivery times. Using a simple network model that simultaneously minimizes energy and time, our analysis explains empirically observed trends in the scaling of metabolic rate in mammals and power consumption and performance in microprocessors across several orders of magnitude in size. Just as the evolutionary transitions from unicellular to multicellular animals in biology are associated with shifts in metabolic scaling, our model suggests that the scaling of power and performance will change as computer designs transition to decentralized multi-core and distributed cyber-physical systems. More generally, a single energy–time minimization principle may govern the design of many complex systems that process energy, materials and information.

This article is part of the themed issue ‘The major synthetic evolutionary transitions’.
more...
No comment yet.
Scooped by Nima Dehghani
Scoop.it!

Towards an integration of deep learning and neuroscience

Towards an integration of deep learning and neuroscience | Biobit: Computational Neuroscience & Biocomputation | Scoop.it
bioRxiv - the preprint server for biology, operated by Cold Spring Harbor Laboratory, a research and educational institution
more...
No comment yet.