Bridging the Functional and Wiring Properties of V1 Neurons Through Sparse Coding

2021 ◽  
pp. 1-34
Author(s):  
Xiaolin Hu ◽  
Zhigang Zeng
Keyword(s):  
Sparse Coding ◽  
Hebbian Learning ◽  
Inhibitory Neuron ◽  
Learning Rule ◽  
Orientation Selectivity ◽  
Inhibitory Neurons ◽  
Natural Scenes ◽  
Firing Rates ◽  
V1 Neurons ◽  
Excitatory Neurons

Abstract The functional properties of neurons in the primary visual cortex (V1) are thought to be closely related to the structural properties of this network, but the specific relationships remain unclear. Previous theoretical studies have suggested that sparse coding, an energy-efficient coding method, might underlie the orientation selectivity of V1 neurons. We thus aimed to delineate how the neurons are wired to produce this feature. We constructed a model and endowed it with a simple Hebbian learning rule to encode images of natural scenes. The excitatory neurons fired sparsely in response to images and developed strong orientation selectivity. After learning, the connectivity between excitatory neuron pairs, inhibitory neuron pairs, and excitatory-inhibitory neuron pairs depended on firing pattern and receptive field similarity between the neurons. The receptive fields (RFs) of excitatory neurons and inhibitory neurons were well predicted by the RFs of presynaptic excitatory neurons and inhibitory neurons, respectively. The excitatory neurons formed a small-world network, in which certain local connection patterns were significantly overrepresented. Bidirectionally manipulating the firing rates of inhibitory neurons caused linear transformations of the firing rates of excitatory neurons, and vice versa. These wiring properties and modulatory effects were congruent with a wide variety of data measured in V1, suggesting that the sparse coding principle might underlie both the functional and wiring properties of V1 neurons.

scholarly journals Mechanisms underlying contrast-dependent orientation selectivity in mouse V1

2018 ◽  
Vol 115 (45) ◽  
pp. 11619-11624 ◽  
Author(s):  
Wei P. Dai ◽  
Douglas Zhou ◽  
David W. McLaughlin ◽  
David Cai
Keyword(s):  
Large Scale ◽  
Feedback Inhibition ◽  
Orientation Selectivity ◽  
Inhibitory Neurons ◽  
Orientation Tuning ◽  
Response Properties ◽  
Firing Rates ◽  
Excitatory Neurons ◽  
Cortical Excitation ◽  
Contrast Invariance

Recent experiments have shown that mouse primary visual cortex (V1) is very different from that of cat or monkey, including response properties—one of which is that contrast invariance in the orientation selectivity (OS) of the neurons’ firing rates is replaced in mouse with contrast-dependent sharpening (broadening) of OS in excitatory (inhibitory) neurons. These differences indicate a different circuit design for mouse V1 than that of cat or monkey. Here we develop a large-scale computational model of an effective input layer of mouse V1. Constrained by experiment data, the model successfully reproduces experimentally observed response properties—for example, distributions of firing rates, orientation tuning widths, and response modulations of simple and complex neurons, including the contrast dependence of orientation tuning curves. Analysis of the model shows that strong feedback inhibition and strong orientation-preferential cortical excitation to the excitatory population are the predominant mechanisms underlying the contrast-sharpening of OS in excitatory neurons, while the contrast-broadening of OS in inhibitory neurons results from a strong but nonpreferential cortical excitation to these inhibitory neurons, with the resulting contrast-broadened inhibition producing a secondary enhancement on the contrast-sharpened OS of excitatory neurons. Finally, based on these mechanisms, we show that adjusting the detailed balances between the predominant mechanisms can lead to contrast invariance—providing insights for future studies on contrast dependence (invariance).

scholarly journals Cell type specific information transfer for sparse coding

2020 ◽  
Author(s):  
Fleur Zeldenrust ◽  
Niccolò Calcini ◽  
Xuan Yan ◽  
Ate Bijlsma ◽  
Tansu Celikel
Keyword(s):  
Sparse Coding ◽  
Information Transfer ◽  
Spike Trains ◽  
Inhibitory Neurons ◽  
Specific Information ◽  
Cell Type ◽  
Sensory Stimuli ◽  
Firing Rates ◽  
Excitatory Neurons ◽  
Cell Type Specific

AbstractSensory neurons reconstruct the world from action potentials (spikes) impinging on them. Recent work argues that the formation of sensory representations are cell-type specific, as excitatory and inhibitory neurons use complementary information available in spike trains to represent sensory stimuli. Here, by measuring the mutual information between synaptic input and spike trains, we show that inhibitory and excitatory neurons in the barrel cortex transfer information differently: excitatory neurons show strong threshold adaptation and a reduction of intracellular information transfer with increasing firing rates. Inhibitory neurons, on the other hand, show threshold behaviour that facilitates broadband information transfer. We propose that cell-type specific intracellular information transfer is the rate-limiting step for neuronal communication across synaptically coupled networks. Ultimately, at high firing rates, the reduction of information transfer by excitatory neurons and its facilitation by inhibitory neurons together provides a mechanism for sparse coding and information compression in cortical networks.

scholarly journals Sparse Coding with a Somato-Dendritic Rule

2018 ◽  
Author(s):  
Damien Drix ◽  
Verena V. Hafner ◽  
Michael Schmuker
Keyword(s):  
Sparse Coding ◽  
Cortical Neurons ◽  
Hebbian Learning ◽  
Learning Rule ◽  
Homeostatic Plasticity ◽  
Hybrid Network ◽  
Neuron Models ◽  
Functional Interpretation ◽  
Inhibitory Plasticity ◽  
Somatic Inhibition

AbstractCortical neurons are silent most of the time. This sparse activity is energy efficient, and the resulting neural code has favourable properties for associative learning. Most neural models of sparse coding use some form of homeostasis to ensure that each neuron fires infrequently. But homeostatic plasticity acting on a fast timescale may not be biologically plausible, and could lead to catastrophic forgetting in embodied agents that learn continuously. We set out to explore whether inhibitory plasticity could play that role instead, regulating both the population sparseness and the average firing rates. We put the idea to the test in a hybrid network where rate-based dendritic compartments integrate the feedforward input, while spiking somas compete through recurrent inhibition. A somato-dendritic learning rule allows somatic inhibition to modulate nonlinear Hebbian learning in the dendrites. Trained on MNIST digits and natural images, the network discovers independent components that form a sparse encoding of the input and support linear decoding. These findings con-firm that intrinsic plasticity is not strictly required for regulating sparseness: inhibitory plasticity can have the same effect, although that mechanism comes with its own stability-plasticity dilemma. Going beyond point neuron models, the network illustrates how a learning rule can make use of dendrites and compartmentalised inputs; it also suggests a functional interpretation for clustered somatic inhibition in cortical neurons.

Epileptiform activity in microcultures containing one excitatory hippocampal neuron

1991 ◽  
Vol 65 (4) ◽  
pp. 761-770 ◽  
Author(s):  
M. M. Segal
Keyword(s):  
Action Potentials ◽  
Epileptiform Activity ◽  
Inhibitory Neuron ◽  
Excitatory Neuron ◽  
Inhibitory Neurons ◽  
Gamma Aminobutyric Acid ◽  
Intracellular Recordings ◽  
Voltage Fluctuations ◽  
Excitatory Neurons ◽  
Small Voltage

1. Paroxysmal depolarizing shifts (PDSs) occur during interictal epileptiform activity. Sustained depolarizations are characteristic of ictal activity, and events resembling PDSs also occur during the sustained depolarizations. To study these elements of epileptiform activity in a simpler context, I used the in vitro chronic-excitatory-block model of epilepsy of Furshpan and Potter and the microculture technique of Segal and Furshpan. 2. Intracellular recordings were made from 93 single-neuron microcultures. Forty of these solitary neurons were excitatory, their action potentials were replaced by PDS-like events or sustained depolarizations as kynurenate was removed from the perfusion solution. PDS-like events were similar to PDSs in intact cortex, mass cultures, and microcultures with more than one neuron. Small voltage fluctuations were also seen in solitary excitatory neurons in the absence of recorded action potentials. Sustained depolarizations developed in 5 of the 40 excitatory neurons. Forty-eight of the 93 solitary neurons were inhibitory, with bicuculline-sensitive hyperpolarizations after the action potential (ascribable to gamma-aminobutyric acid-A autapses). None of the solitary inhibitory neurons displayed sustained depolarizations. Five of the 93 neurons were insensitive to both kynurenate and bicuculline and were not placed in either the excitatory or the inhibitory category. 3. Both N-methyl-D-aspartate (NMDA) and non-NMDA glutamate receptors contributed to the PDS-like events and sustained depolarizations. Only a non-NMDA glutamate receptor component was evident for the small voltage fluctuations. 4. Intracellular recordings were also made from two-neuron microcultures, each containing one excitatory neuron and one inhibitory neuron. Sustained depolarizations developed in five microcultures, in each case only in the excitatory neuron.

Magnitude of Perceived Change in Natural Images May Be Linearly Proportional to Differences in Neuronal Firing Rates

2010 ◽  
Vol 23 (4) ◽  
pp. 349-372 ◽  
Author(s):  
Pei-Ying Chua ◽  
Tom Troscianko ◽  
P. George Lovell ◽  
David Tolhurst ◽  
Mazviita Chirimuuta ◽  
...  
Keyword(s):  
Neuronal Response ◽  
Neuronal Firing ◽  
Natural Scenes ◽  
Action Potential Firing ◽  
Firing Rates ◽  
V1 Neurons ◽  
Perceived Change ◽  
Potential Firing ◽  
Neuronal Firing Rate ◽  
Image Pairs

AbstractWe are studying how people perceive naturalistic suprathreshold changes in the colour, size, shape or location of items in images of natural scenes, using magnitude estimation ratings to characterise the sizes of the perceived changes in coloured photographs. We have implemented a computational model that tries to explain observers' ratings of these naturalistic differences between image pairs. We model the action-potential firing rates of millions of neurons, having linear and non-linear summation behaviour closely modelled on real V1 neurons. The numerical parameters of the model's sigmoidal transducer function are set by optimising the same model to experiments on contrast discrimination (contrast 'dippers') on monochrome photographs of natural scenes. The model, optimised on a stimulus-intensity domain in an experiment reminiscent of the Weber–Fechner relation, then produces tolerable predictions of the ratings for most kinds of naturalistic image change. Importantly, rating rises roughly linearly with the model's numerical output, which represents differences in neuronal firing rate in response to the two images under comparison; this implies that rating is proportional to the neuronal response.

scholarly journals Cortical circuits implement optimal context integration

2017 ◽  
Author(s):  
Ramakrishnan Iyer ◽  
Stefan Mihalas
Keyword(s):  
Receptive Field ◽  
Large Scale ◽  
Inhibitory Neuron ◽  
Visual Input ◽  
Inhibitory Neurons ◽  
Specific Cell ◽  
Natural Scene ◽  
Cortical Circuits ◽  
Classical Receptive Field ◽  
Excitatory Neurons

Neurons in the primary visual cortex (V1) predominantly respond to a patch of the visual input, their classical receptive field. These responses are modulated by the visual input in the surround [2]. This reflects the fact that features in natural scenes do not occur in isolation: lines, surfaces are generally continuous, and the surround provides context for the information in the classical receptive field. It is generally assumed that the information in the near surround is transmitted via lateral connections between neurons in the same area [2]. A series of large scale efforts have recently described the relation between lateral connectivity and visual evoked responses and found like-to-like connectivity between excitatory neurons [16, 18]. Additionally, specific cell type connectivity for inhibitory neuron types has been described [11, 31]. Current normative models of cortical function relying on sparsity [27], saliency [4] predict functional inhibition between similarly tuned neurons. What computations are consistent with the observed structure of the lateral connections between the excitatory and diverse types of inhibitory neurons?We combined natural scene statistics [24] and mouse V1 neuron responses [7] to compute the lateral connections and computations of individual neurons which optimally integrate information from the classical receptive field with that from the surround by directly implementing Bayes rule. This increases the accuracy of representation of a natural scene under noisy conditions. We show that this network has like-to-like connectivity between excitatory neurons, similar to the observed one [16, 18, 11], and has three types of inhibition: local normalization, surround inhibition and gating of inhibition from the surround - that can be attributed to three classes of inhibitory neurons. We hypothesize that this computation: optimal integration of contextual cues with a gate to ignore context when necessary is a general property of cortical circuits, and the rules constructed for mouse V1 generalize to other areas and species.

scholarly journals Stimulus selectivity and response latency in putative inhibitory and excitatory neurons of the primate inferior temporal cortex

2012 ◽  
Vol 108 (10) ◽  
pp. 2725-2736 ◽  
Author(s):  
Ryan E. B. Mruczek ◽  
David L. Sheinberg
Keyword(s):  
Functional Properties ◽  
Response Latency ◽  
Visual Processing ◽  
Temporal Cortex ◽  
Cell Types ◽  
Inferior Temporal Cortex ◽  
Inhibitory Neurons ◽  
Response Latencies ◽  
Firing Rates ◽  
Excitatory Neurons

The cerebral cortex is composed of many distinct classes of neurons. Numerous studies have demonstrated corresponding differences in neuronal properties across cell types, but these comparisons have largely been limited to conditions outside of awake, behaving animals. Thus the functional role of the various cell types is not well understood. Here, we investigate differences in the functional properties of two widespread and broad classes of cells in inferior temporal cortex of macaque monkeys: inhibitory interneurons and excitatory projection cells. Cells were classified as putative inhibitory or putative excitatory neurons on the basis of their extracellular waveform characteristics (e.g., spike duration). Consistent with previous intracellular recordings in cortical slices, putative inhibitory neurons had higher spontaneous firing rates and higher stimulus-evoked firing rates than putative excitatory neurons. Additionally, putative excitatory neurons were more susceptible to spike waveform adaptation following very short interspike intervals. Finally, we compared two functional properties of each neuron's stimulus-evoked response: stimulus selectivity and response latency. First, putative excitatory neurons showed stronger stimulus selectivity compared with putative inhibitory neurons. Second, putative inhibitory neurons had shorter response latencies compared with putative excitatory neurons. Selectivity differences were maintained and latency differences were enhanced during a visual search task emulating more natural viewing conditions. Our results suggest that short-latency inhibitory responses are likely to sculpt visual processing in excitatory neurons, yielding a sparser visual representation.

scholarly journals Role of Homeostasis in Learning Sparse Representations

2010 ◽  
Vol 22 (7) ◽  
pp. 1812-1836 ◽  
Author(s):  
Laurent U. Perrinet
Keyword(s):  
Sparse Coding ◽  
Hebbian Learning ◽  
Receptive Fields ◽  
Sparse Representations ◽  
Natural Images ◽  
Neural Representation ◽  
Natural Scenes ◽  
Efficient Coding ◽  
Sensory Data

Neurons in the input layer of primary visual cortex in primates develop edge-like receptive fields. One approach to understanding the emergence of this response is to state that neural activity has to efficiently represent sensory data with respect to the statistics of natural scenes. Furthermore, it is believed that such an efficient coding is achieved using a competition across neurons so as to generate a sparse representation, that is, where a relatively small number of neurons are simultaneously active. Indeed, different models of sparse coding, coupled with Hebbian learning and homeostasis, have been proposed that successfully match the observed emergent response. However, the specific role of homeostasis in learning such sparse representations is still largely unknown. By quantitatively assessing the efficiency of the neural representation during learning, we derive a cooperative homeostasis mechanism that optimally tunes the competition between neurons within the sparse coding algorithm. We apply this homeostasis while learning small patches taken from natural images and compare its efficiency with state-of-the-art algorithms. Results show that while different sparse coding algorithms give similar coding results, the homeostasis provides an optimal balance for the representation of natural images within the population of neurons. Competition in sparse coding is optimized when it is fair. By contributing to optimizing statistical competition across neurons, homeostasis is crucial in providing a more efficient solution to the emergence of independent components.

scholarly journals Cell-autonomous role of Presenilin in age-dependent survival of cortical interneurons

2020 ◽  
Vol 15 (1) ◽  
Author(s):  
Jongkyun Kang ◽  
Jie Shen
Keyword(s):  
Cerebral Cortex ◽  
Inhibitory Neuron ◽  
Gabaergic Neurons ◽  
Inhibitory Neurons ◽  
Double Knockout ◽  
Normal Number ◽  
Cortical Interneurons ◽  
Age Dependent ◽  
Excitatory Neurons

Abstract Background Mutations in the PSEN1 and PSEN2 genes are the major cause of familial Alzheimer’s disease. Previous studies demonstrated that Presenilin (PS), the catalytic subunit of γ-secretase, is required for survival of excitatory neurons in the cerebral cortex during aging. However, the role of PS in inhibitory interneurons had not been explored. Methods To determine PS function in GABAergic neurons, we generated inhibitory neuron-specific PS conditional double knockout (IN-PS cDKO) mice, in which PS is selectively inactivated by Cre recombinase expressed under the control of the endogenous GAD2 promoter. We then performed behavioral, biochemical, and histological analyses to evaluate the consequences of selective PS inactivation in inhibitory neurons. Results IN-PS cDKO mice exhibit earlier mortality and lower body weight despite normal food intake and basal activity. Western analysis of protein lysates from various brain sub-regions of IN-PS cDKO mice showed significant reduction of PS1 levels and dramatic accumulation of γ-secretase substrates. Interestingly, IN-PS cDKO mice develop age-dependent loss of GABAergic neurons, as shown by normal number of GAD67-immunoreactive interneurons in the cerebral cortex at 2–3 months of age but reduced number of cortical interneurons at 9 months. Moreover, age-dependent reduction of Parvalbumin- and Somatostatin-immunoreactive interneurons is more pronounced in the neocortex and hippocampus of IN-PS cDKO mice. Consistent with these findings, the number of apoptotic cells is elevated in the cerebral cortex of IN-PS cDKO mice, and the enhanced apoptosis is due to dramatic increases of apoptotic interneurons, whereas the number of apoptotic excitatory neurons is unaffected. Furthermore, progressive loss of interneurons in the cerebral cortex of IN-PS cDKO mice is accompanied with astrogliosis and microgliosis. Conclusion Our results together support a cell-autonomous role of PS in the survival of cortical interneurons during aging. Together with earlier studies, these findings demonstrate a universal, essential requirement of PS in the survival of both excitatory and inhibitory neurons during aging.

The Winner Takes It All

2020 ◽  
pp. 31-34
Author(s):  
Thomas Boraud
Keyword(s):  
Nervous System ◽  
Neural Plasticity ◽  
Hebbian Learning ◽  
Learning Rule ◽  
Neural System ◽  
Learning Processes ◽  
Inhibitory Neurons ◽  
Hebbian Learning Rule ◽  
Synaptic Noise ◽  
Two Populations

This chapter reviews the general principles that are necessary for a neural system to make decisions. A glance at the literature shows that the simplest system to obtain an imbalance between two populations of neurons subjected to the same activation consists of two interconnected populations of inhibitory neurons. These two populations exert lateral inhibition on each other. In order for a differential response to emerge, noise is necessary. Synaptic noise is considered the main source of noise in the nervous system. The chapter then goes on to look at positive feedback. It also studies the learning processes in the nervous system and explores neural plasticity rules, particularly the Hebbian learning rule.

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