scholarly journals Neural oscillations as a signature of efficient coding in the presence of synaptic delays

2015 ◽  
Author(s):  
Matthew Chalk ◽  
Boris Gutkin ◽  
Sophie Deneve
Keyword(s):  
High Efficiency ◽  
Direct Consequence ◽  
Strongly Correlated ◽  
Cortical Networks ◽  
Efficient Coding ◽  
Gamma Rhythm ◽  
Spiking Networks ◽  
Efficient Representation ◽  
Share Information ◽  
Oscillatory Rhythm

Cortical networks exhibit "global oscillations", in which neural spike times are entrained to an underlying oscillatory rhythm, but where individual neurons fire irregularly, on only a fraction of cycles. While the network dynamics underlying global oscillations have been well characterised, their function is debated. Here, we show that such global oscillations are a direct consequence of optimal efficient coding in spiking networks with synaptic delays. To avoid firing unnecessary spikes, neurons need to share information about the network state. Ideally, membrane potentials should be strongly correlated and reflect a "prediction error" while the spikes themselves are uncorrelated and occur rarely. We show that the most efficient representation is achieved when: (i) spike times are entrained to a global Gamma rhythm (implying a consistent representation of the error); but (ii) few neurons fire on each cycle (implying high efficiency), while (iii) excitation and inhibition are tightly balanced. This suggests that cortical networks exhibiting such dynamics are tuned to achieve a maximally efficient population code.

scholarly journals Neural oscillations as a signature of efficient coding in the presence of synaptic delays

eLife ◽  
2016 ◽  
Vol 5 ◽  
Author(s):  
Matthew Chalk ◽  
Boris Gutkin ◽  
Sophie Denève
Keyword(s):  
High Efficiency ◽  
Direct Consequence ◽  
Strongly Correlated ◽  
Cortical Networks ◽  
Efficient Coding ◽  
Gamma Rhythm ◽  
Spiking Networks ◽  
Efficient Representation ◽  
Share Information ◽  
Oscillatory Rhythm

Cortical networks exhibit 'global oscillations', in which neural spike times are entrained to an underlying oscillatory rhythm, but where individual neurons fire irregularly, on only a fraction of cycles. While the network dynamics underlying global oscillations have been well characterised, their function is debated. Here, we show that such global oscillations are a direct consequence of optimal efficient coding in spiking networks with synaptic delays and noise. To avoid firing unnecessary spikes, neurons need to share information about the network state. Ideally, membrane potentials should be strongly correlated and reflect a 'prediction error' while the spikes themselves are uncorrelated and occur rarely. We show that the most efficient representation is when: (i) spike times are entrained to a global Gamma rhythm (implying a consistent representation of the error); but (ii) few neurons fire on each cycle (implying high efficiency), while (iii) excitation and inhibition are tightly balanced. This suggests that cortical networks exhibiting such dynamics are tuned to achieve a maximally efficient population code.

scholarly journals A Unified Theory Of Early Visual Representations From Retina To Cortex Through Anatomically Constrained Deep CNNs

2019 ◽  
Author(s):  
Jack Lindsey ◽  
Samuel A. Ocko ◽  
Surya Ganguli ◽  
Stephane Deny
Keyword(s):  
Visual System ◽  
Visual Processing ◽  
Visual Information ◽  
Resource Constraints ◽  
Visual Representations ◽  
Direct Consequence ◽  
Visual Scene ◽  
Natural Scenes ◽  
Unified Theory ◽  
Cortical Networks

AbstractThe vertebrate visual system is hierarchically organized to process visual information in successive stages. Neural representations vary drastically across the first stages of visual processing: at the output of the retina, ganglion cell receptive fields (RFs) exhibit a clear antagonistic center-surround structure, whereas in the primary visual cortex (V1), typical RFs are sharply tuned to a precise orientation. There is currently no unified theory explaining these differences in representations across layers. Here, using a deep convolutional neural network trained on image recognition as a model of the visual system, we show that such differences in representation can emerge as a direct consequence of different neural resource constraints on the retinal and cortical networks, and for the first time we find a single model from which both geometries spontaneously emerge at the appropriate stages of visual processing. The key constraint is a reduced number of neurons at the retinal output, consistent with the anatomy of the optic nerve as a stringent bottleneck. Second, we find that, for simple downstream cortical networks, visual representations at the retinal output emerge as nonlinear and lossy feature detectors, whereas they emerge as linear and faithful encoders of the visual scene for more complex cortical networks. This result predicts that the retinas of small vertebrates (e.g. salamander, frog) should perform sophisticated nonlinear computations, extracting features directly relevant to behavior, whereas retinas of large animals such as primates should mostly encode the visual scene linearly and respond to a much broader range of stimuli. These predictions could reconcile the two seemingly incompatible views of the retina as either performing feature extraction or efficient coding of natural scenes, by suggesting that all vertebrates lie on a spectrum between these two objectives, depending on the degree of neural resources allocated to their visual system.

scholarly journals Spectrum Degradation of Hippocampal LFP During Euthanasia

2021 ◽  
Vol 15 ◽  
Author(s):  
Yuchen Zhou ◽  
Alex Sheremet ◽  
Jack P. Kennedy ◽  
Nicholas M. DiCola ◽  
Carolina B. Maciel ◽  
...  
Keyword(s):  
Entorhinal Cortex ◽  
Fault Tolerant ◽  
Field Potential ◽  
Power Spectra ◽  
Direct Consequence ◽  
Synaptic Activity ◽  
Low Frequencies ◽  
Second Stage ◽  
Gamma Rhythm ◽  
Neural Entrainment

The hippocampal local field potential (LFP) exhibits a strong correlation with behavior. During rest, the theta rhythm is not prominent, but during active behavior, there are strong rhythms in the theta, theta harmonics, and gamma ranges. With increasing running velocity, theta, theta harmonics and gamma increase in power and in cross-frequency coupling, suggesting that neural entrainment is a direct consequence of the total excitatory input. While it is common to study the parametric range between the LFP and its complementing power spectra between deep rest and epochs of high running velocity, it is also possible to explore how the spectra degrades as the energy is completely quenched from the system. Specifically, it is unknown whether the 1/f slope is preserved as synaptic activity becomes diminished, as low frequencies are generated by large pools of neurons while higher frequencies comprise the activity of more local neuronal populations. To test this hypothesis, we examined rat LFPs recorded from the hippocampus and entorhinal cortex during barbiturate overdose euthanasia. Within the hippocampus, the initial stage entailed a quasi-stationary LFP state with a power-law feature in the power spectral density. In the second stage, there was a successive erosion of power from high- to low-frequencies in the second stage that continued until the only dominant remaining power was <20 Hz. This stage was followed by a rapid collapse of power spectrum toward the absolute electrothermal noise background. As the collapse of activity occurred later in hippocampus compared with medial entorhinal cortex, it suggests that the ability of a neural network to maintain the 1/f slope with decreasing energy is a function of general connectivity. Broadly, these data support the energy cascade theory where there is a cascade of energy from large cortical populations into smaller loops, such as those that supports the higher frequency gamma rhythm. As energy is pulled from the system, neural entrainment at gamma frequency (and higher) decline first. The larger loops, comprising a larger population, are fault-tolerant to a point capable of maintaining their activity before a final collapse.

scholarly journals Efficient phase coding in hippocampal place cells

2019 ◽  
Author(s):  
Pavithraa Seenivasan ◽  
Rishikesh Narayanan
Keyword(s):  
High Efficiency ◽  
Place Cells ◽  
Intrinsic Properties ◽  
Place Field ◽  
Phase Coding ◽  
Efficient Coding ◽  
Coding Efficiency ◽  
Synergistic Interactions ◽  
Phase Precession ◽  
Sequence Compression

ABSTRACTHippocampal place cells encode space through phase precession, whereby neuronal spike phase progressively advances during place-field traversals. What neural constraints are essential for achieving efficient transfer of information through such phase codes, while concomitantly maintaining signature neuronal excitability specific to individual cell types? We developed a conductance-based model for phase precession in CA1 pyramidal neurons within the temporal sequence compression framework, and defined phase-coding efficiency using information theory. We recruited an unbiased stochastic search strategy to build a model population that exhibited physiologically observed heterogeneities in intrinsic properties. Place-field responses elicited from these models matched signature sub- and supra-threshold place-cell characteristics, including phase precession, sub-threshold voltage ramps, increases in theta-frequency power and firing rate during place-field traversals. Employing this model population, we show that disparate parametric combinations with weak pair-wise correlations resulted in models with similar high-efficiency phase codes and similar excitability characteristics. Mechanistically, the emergence of such parametric degeneracy was dependent on the differential and variable impact of individual ion channels on phase-coding efficiency in different models, and importantly, on synergistic interactions between synaptic and intrinsic properties. Furthermore, our analyses predicted a dominant role for calcium-activated potassium channels in regulating phase precession and coding efficiency. Finally, change in afferent statistics, manifesting as input asymmetry, induced an adaptive shift in the phase code that preserved its efficiency, apart from introducing asymmetry in sub-threshold ramps and firing profiles during place-field traversals. Our study postulates degeneracy as a potential framework to attain the twin goals of efficient temporal coding and robust homeostasis.SIGNIFICANCE STATEMENTNeuronal intrinsic properties exhibit significant baseline heterogeneities, and change with activity-dependent plasticity and neuromodulation. How do hippocampal neurons encode spatial locations through the precise timings of their action potentials in the face of such heterogeneities? Here, employing a unifying synthesis of the temporal sequence compression, efficient coding and degeneracy frameworks, we show that there are several disparate routes for neurons to achieve high-efficiency spatial information transfer through such temporal codes. These disparate routes were consequent to the ability of neurons to produce precise encoding through distinct structural components, critically involving synergistic interactions between intrinsic and synaptic properties. Our results point to an explosion in the degrees of freedom available to a neuron in concomitantly achieving efficient coding and excitability homeostasis.

scholarly journals Transcription factors involved in abiotic stress responses in Maize (Zea mays L.) and their roles in enhanced productivity in the post genomics era

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e7211 ◽  
Author(s):  
Roy Njoroge Kimotho ◽  
Elamin Hafiz Baillo ◽  
Zhengbin Zhang
Keyword(s):  
Zea Mays ◽  
Transcription Factors ◽  
Abiotic Stress ◽  
Stress Responses ◽  
Abiotic Stresses ◽  
Regulatory Networks ◽  
Target Genes ◽  
High Efficiency ◽  
Animal Feed ◽  
Direct Consequence

Background Maize (Zea mays L.) is a principal cereal crop cultivated worldwide for human food, animal feed, and more recently as a source of biofuel. However, as a direct consequence of water insufficiency and climate change, frequent occurrences of both biotic and abiotic stresses have been reported in various regions around the world, and recently, this has become a constant threat in increasing global maize yields. Plants respond to abiotic stresses by utilizing the activities of transcription factors (TFs), which are families of genes coding for specific TF proteins. TF target genes form a regulon that is involved in the repression/activation of genes associated with abiotic stress responses. Therefore, it is of utmost importance to have a systematic study on each TF family, the downstream target genes they regulate, and the specific TF genes involved in multiple abiotic stress responses in maize and other staple crops. Method In this review, the main TF families, the specific TF genes and their regulons that are involved in abiotic stress regulation will be briefly discussed. Great emphasis will be given on maize abiotic stress improvement throughout this review, although other examples from different plants like rice, Arabidopsis, wheat, and barley will be used. Results We have described in detail the main TF families in maize that take part in abiotic stress responses together with their regulons. Furthermore, we have also briefly described the utilization of high-efficiency technologies in the study and characterization of TFs involved in the abiotic stress regulatory networks in plants with an emphasis on increasing maize production. Examples of these technologies include next-generation sequencing, microarray analysis, machine learning, and RNA-Seq. Conclusion In conclusion, it is expected that all the information provided in this review will in time contribute to the use of TF genes in the research, breeding, and development of new abiotic stress tolerant maize cultivars.

scholarly journals Decision by sampling implements efficient coding of psychoeconomic functions

2017 ◽  
Author(s):  
Rahul Bhui ◽  
Samuel J. Gershman
Keyword(s):  
Communication Channel ◽  
Frequency Theory ◽  
Noisy Channel ◽  
Response Distribution ◽  
Efficient Coding ◽  
Cognitive Limitations ◽  
Behavioral Observations ◽  
Subjective Value ◽  
Efficient Representation ◽  
Normative Justification

AbstractThe theory of decision by sampling (DbS) proposes that an attribute’s subjective value is its rank within a sample of attribute values retrieved from memory. This can account for instances of context dependence beyond the reach of classic theories which assume stable preferences. In this paper, we provide a normative justification for DbS that is based on the principle of efficient coding. The efficient representation of information in a noiseless communication channel is characterized by a uniform response distribution, which the rank transformation implements. However, cognitive limitations imply that decision samples are finite, introducing noise. Efficient coding in a noisy channel requires smoothing of the signal, a principle that leads to a new generalization of DbS. This generalization is closely connected to range-frequency theory, and helps descriptively account for a wider set of behavioral observations, such as how context sensitivity varies with the number of available response categories.

scholarly journals Spectrum Degradation of Hippocampal LFP During Euthanasia

2020 ◽  
Author(s):  
Y. Zhou ◽  
A. Sheremet ◽  
J. P. Kennedy ◽  
Nicholas M. DiCola ◽  
Carolina B. Maciel ◽  
...  
Keyword(s):  
Entorhinal Cortex ◽  
Fault Tolerant ◽  
Field Potential ◽  
Power Spectra ◽  
Direct Consequence ◽  
Excitatory Input ◽  
Synaptic Activity ◽  
Low Frequencies ◽  
Gamma Rhythm ◽  
Neural Entrainment

AbstractThe hippocampal local field potential (LFP) exhibits a strong correlation with behavior. During rest, the theta rhythm is not prominent, but during active behavior, there are strong rhythms in the theta, theta harmonics, and gamma ranges. With increasing running velocity, theta, theta harmonics and gamma increase in power and in cross-frequency coupling, suggesting that neural entrainment is a direct consequence of the total excitatory input. While it is common to study the parametric range between the LFP and its complementing power spectra between deep rest and epochs of high running velocity, it is also possible to explore how the spectra degrades as the energy is completely quenched from the system. Specifically, it is unknown whether the 1/f slope is preserved as synaptic activity becomes diminished, as low frequencies are generated by large pools of neurons while higher frequencies comprise the activity of more local neuronal populations. To test this hypothesis, we examined rat LFPs recorded from the hippocampus and entorhinal cortex during barbiturate overdose euthanasia. Within the hippocampus, the initial stage entailed a quasi-stationary stage when the LFP spectrum exhibited power-law feature while the frequency components over 20 Hz exhibited a power decay with a similar decay rate. This stage was followed by a rapid collapse of power spectrum towards the absolute electrothermal noise background. As the collapse of activity occurred later in hippocampus compared with medial entorhinal cortex or visual cortex, it suggests that the ability of a neural network to maintain the 1/f slope with decreasing energy is a function of general connectivity. Broadly, these data support the energy cascade theory where there is a cascade of energy from large cortical populations into smaller loops, such as those that supports the higher frequency gamma rhythm. As energy is pulled from the system, neural entrainment at gamma frequency (and higher) decline first. The larger loops, comprising a larger population, are fault-tolerant to a point capable of maintaining their activity before a final collapse.

scholarly journals Understanding the photophysics and structural organization of photosynthetic proteins using model lipid membranes assembled from natural plant thylakoids

2020 ◽  
Author(s):  
Sophie A. Meredith ◽  
Takuro Yoneda ◽  
Ashley M. Hancock ◽  
Simon D. Connell ◽  
Stephen D. Evans ◽  
...  
Keyword(s):  
Lipid Bilayers ◽  
Fluorescence Lifetime ◽  
Protein Interactions ◽  
Self Assembly ◽  
High Efficiency ◽  
Complex Mixture ◽  
Direct Consequence ◽  
Model Systems ◽  
Hybrid Membranes ◽  
Model Lipid Membranes

AbstractThe light-harvesting (LH) biomembranes from photosynthetic organisms perform solar energy absorption and transfer with high efficiency. There is great interest in the nanoscale biophysics of photosynthesis, however, natural membranes are complex and highly curved so can be challenging to study. Here we present model photosynthetic “hybrid membranes” assembled from a combination of natural LH membranes and synthetic lipids deposited into a patterned polymerized lipid template on glass. This arrangement offers many advantages over previous model systems including: a sufficiently complex mixture of natural proteins to mimic the biological processes, a modular self-assembly mechanism, and a stabilizing template promoting the formation of supported lipid bilayers from complex natural membranes with high protein content (that would not otherwise form). These hybrid membranes can be used as a platform to delineate the complex relationship between LH energy pathways and membrane organization. Atomic force microscopy and fluorescence lifetime microscopy revealed that hybrid membranes have an elongated fluorescence lifetime (∼4 ns) compared to native membranes (∼0.5 ns), a direct consequence of reduced protein density and an uncoupling of protein-protein interactions. We observed the real time self-assembly and migration of LH proteins from natural membrane extracts into the hybrid membranes and monitored the photophysical state of the membranes at each stage. Finally, experiments utilizing our hybrid membranes suggest that assays currently used in the photosynthesis community to test the electron transfer activity of Photosystem II may have non-specific interactions with other proteins, implying that new methods are needed for reliable quantification of electron transfers in photosynthesis.

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