Topology and dynamics of the canonical circuit of cat V1

Binzegger, Tom; Douglas, Rodney J.; Martin, Kevan A

doi:10.1016/j.neunet.2009.07.011

Cited by 101 publications

(99 citation statements)

References 42 publications

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“…Specific activation of L4 and L2/3 gamma oscillations with high interlayer coherence during the processing of sensory stimuli is consistent with the known functional architecture of the cortex. Pyramidal cells in L2/3 have widespread horizontal connections that are known to be critical for cortico-cortical information processing (Barbas et al 2005;Binzegger et al 2009;Felleman and Van Essen 1991), and strong excitatory projections from thalamorecipient L4 neurons drive excitatory activity in L2/3 during the representation of sensory stimuli. Furthermore, the coherence at gamma frequency along the vertical axis of the cortex is highly increased during visual responses but mostly between L4 and L2/3.…”

Section: Discussionmentioning

confidence: 99%

“…Anatomical and electrophysiological studies of cortical circuits have found a strong efferent drive from L2/3 to L5/6 (Binzegger et al 2009;Thomson et al 2002). We hypothesized that the increase in gamma frequency activity in the LFP and single units of L2/3 during stimulus-associated gamma might be driving the increased rhythmicity seen in FS and RS cells in L5/6.…”

Section: Stimulus-driven But Not Spontaneous Gamma Frequency Activimentioning

confidence: 99%

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Sensory-driven and spontaneous gamma oscillations engage distinct cortical circuitry

Welle

Contreras

2016

Journal of Neurophysiology

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Section: Discussionmentioning

confidence: 99%

Section: Stimulus-driven But Not Spontaneous Gamma Frequency Activimentioning

confidence: 99%

Sensory-driven and spontaneous gamma oscillations engage distinct cortical circuitry

Welle

Contreras

2016

Journal of Neurophysiology

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“…However, in these other areas temporal integration is possible [e.g, when sensory stimuli are combined (51) and direct electrical stimuli are used (54)]. Across sensory cortical areas, there is strong divergence even in the first set of inputs, the thalamocortical connections, and most individual inputs are weak (1,39,55,56). These are the conditions-when each cell receives many inputs and no single input dominates-under which population linearity can hold.…”

Section: Discussion Linear Population Codes Can Arise From Weak Inputmentioning

confidence: 99%

Cortical neural populations can guide behavior by integrating inputs linearly, independent of synchrony

Histed

Maunsell

2013

Proc. Natl. Acad. Sci. U.S.A.

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Neurons are sensitive to the relative timing of inputs, both because several inputs must coincide to reach spike threshold and because active dendritic mechanisms can amplify synchronous inputs. To determine if input synchrony can influence behavior, we trained mice to report activation of excitatory neurons in visual cortex using channelrhodopsin-2. We used light pulses that varied in duration from a few milliseconds to 100 ms and measured neuronal responses and animals' detection ability. We found detection performance was well predicted by the total amount of light delivered. Short pulses provided no behavioral advantage, even when they concentrated evoked spikes into an interval a few milliseconds long. Arranging pulses into trains of varying frequency from beta to gamma also produced no behavioral advantage. Light intensities required to drive behavior were low (at low intensities, channelrhodopsin-2 conductance varies linearly with intensity), and the accompanying changes in firing rate were small (over 100 ms, average change: 1.1 spikes per s). Firing rate changes varied linearly with pulse intensity and duration, and behavior was predicted by total spike count independent of temporal arrangement. Thus, animals' detection performance reflected the linear integration of total input over 100 ms. This behavioral linearity despite neurons' nonlinearities can be explained by a population code using noisy neurons. Ongoing background activity creates probabilistic spiking, allowing weak inputs to change spike probability linearly, with little amplification of coincident input. Summing across a population then yields a total spike count that weights inputs equally, regardless of their arrival time.neuronal circuits | population coding | optogenetics | mouse M any neurons in the brain receive thousands of inputs spread over their dendritic trees, and several of those inputs need to be active simultaneously to generate a spike reliably (1, 2). In this way, coincident synaptic inputs can be amplified relative to asynchronous inputs. In addition to this nonlinearity caused by spike threshold, other active processes such as dendritic calcium spikes (3-5) can preferentially amplify synchronous inputs. A variety of ways that timing could impact network function have been explored, including oscillatory synchronization (6, 7), strong cascading effects of individual neurons or synapses (8-11), and information encoding via temporal patterns (12). In the songbird, for example, song neurons receive precisely timed coincident input that recruits active calcium conductances, generating strong, reliable spike bursts that control the song (13, 14). However, synchrony might not always be critical for neuronal processing. Several types of models show that neuronal networks can be insensitive to precise spike timing even though individual neurons are highly sensitive. Most of these models rely on strong (15) or numerous (16-18) inputs and amplification of small perturbations, leading to chaotic network dynamics and noisy single neu...

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“…In the present model, they also serve another function-that is, to convey excitation to the special group of inhibitory interneurons, r ij , which will be discussed in the next section. The proposed scheme respects the facts that there are more pyramidal cells in L2/3 than in L5 and that the density of excitatory connections between L2/3 and L5 is relatively low, presumably connecting only nodes in L2/3 with nodes in L5 within the same column (Binzegger et al, 2004(Binzegger et al, , 2009). …”

Section: Computation In a Cortical Microcircuitmentioning

confidence: 74%

A computational model of fMRI activity in the intraparietal sulcus that supports visual working memory

Domijan

2011

Cogn Affect Behav Neurosci

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A computational model was developed to explain a pattern of results of fMRI activation in the intraparietal sulcus (IPS) supporting visual working memory for multiobject scenes. The model is based on the hypothesis that dendrites of excitatory neurons are major computational elements in the cortical circuit. Dendrites enable formation of a competitive queue that exhibits a gradient of activity values for nodes encoding different objects, and this pattern is stored in working memory. In the model, brain imaging data are interpreted as a consequence of blood flow arising from dendritic processing. Computer simulations showed that the model successfully simulates data showing the involvement of inferior IPS in object individuation and spatial grouping through representation of objects' locations in space, along with the involvement of superior IPS in object identification through representation of a set of objects' features. The model exhibits a capacity limit due to the limited dynamic range for nodes and the operation of lateral inhibition among them. The capacity limit is fixed in the inferior IPS regardless of the objects' complexity, due to the normalization of lateral inhibition, and variable in the superior IPS, due to the different encoding demands for simple and complex shapes. Systematic variation in the strength of self-excitation enables an understanding of the individual differences in working memory capacity. The model offers several testable predictions regarding the neural basis of visual working memory.

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Topology and dynamics of the canonical circuit of cat V1

Cited by 101 publications

References 42 publications

Sensory-driven and spontaneous gamma oscillations engage distinct cortical circuitry

Sensory-driven and spontaneous gamma oscillations engage distinct cortical circuitry

Cortical neural populations can guide behavior by integrating inputs linearly, independent of synchrony

A computational model of fMRI activity in the intraparietal sulcus that supports visual working memory

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