Neuronal Circuits of the Neocortex

Neuroscience and Neuroeconomics, 2016

Neurophysiological and neuroanatomical studies have found that the same basic structural and functional organization of neuronal circuits exists throughout the cortex. This kind of cortical organization, termed canonical circuit, has been functionally demonstrated primarily by studies involving visual striate cortex, and then, the concept has been extended to different cortical areas. In brief, the canonical circuit is composed of superficial pyramidal neurons of layers II/III receiving different inputs and deep pyramidal neurons of layer V that are responsible for cortex output. Superficial and deep pyramidal neurons are reciprocally connected, and inhibitory interneurons participate in modulating the activity of the circuit. The main intuition of this model is that the entire cortical network could be modeled as the repetition of relatively simple modules composed of relatively few types of excitatory and inhibitory, highly interconnected neurons. We will review the origin and the application of the canonical cortical circuit model in the six sections of this paper. The first section (The origins of the concept of canonical circuit: the cat visual cortex) reviews the experiments performed in the cat visual cortex, from the origin of the concept of canonical circuit to the most recent developments in the modelization of cortex. The second (The canonical circuit in neocortex) and third (Toward a canonical circuit in agranular cortex) sections try to extend the concept of canonical circuit to other cortical areas, providing some significant examples of circuit functioning in different cytoarchitectonic contexts. The fourth section (Extending the concept of canonical circuit to economic decisions circuits) reviews the experiments conducted in humans by using transcranial magnetic stimulation to demonstrate the validity of the canonical cortical circuit model. The fifth section (Extending the concept of canonical circuit to economic decisions circuits) explores the hypothesis that also complex human behaviors such as economic decision-making could also be explained in terms of canonical cortical circuit. The final section (Conclusion) provides a critical point of view, evidencing the limits of the available data and tracking directions for future research.

Science Advances, 2022

The traditional view of neural computation in the cerebral cortex holds that sensory neurons are specialized, i.e., selective for certain dimensions of sensory stimuli. This view was challenged by evidence of contextual interactions between stimulus dimensions in which a neuron's response to one dimension strongly depends on other dimensions. Here we use methods of mathematical modeling, psychophysics, and electrophysiology to address shortcomings of the traditional view. Using a model of a generic cortical circuit, we begin with the simple demonstration that cortical responses are always distributed among neurons, forming characteristic waveforms, which we call neural waves. When stimulated by patterned stimuli, circuit responses arise by interference of neural waves. Resulting patterns of interference depend on interaction between stimulus dimensions. Comparison of these modeled responses with responses of biological vision makes it clear that the framework of neural wave interference provides a useful alternative to the standard concept of neural computation. Teaser Investigating interference of neural waves helps to overcome limitations of the traditional view of cortical computation.

MIT Press eBooks, 2014

Nature neuroscience, 2005

Can neuronal morphology predict functional synaptic circuits? In the rat barrel cortex, 'barrels' and 'septa' delineate an orderly matrix of cortical columns. Using quantitative laser scanning photostimulation we measured the strength of excitatory projections from layer 4 (L4) and L5A to L2/3 pyramidal cells in barrel- and septum-related columns. From morphological reconstructions of excitatory neurons we computed the geometric circuit predicted by axodendritic overlap. Within most individual projections, functional inputs were predicted by geometry and a single scale factor, the synaptic strength per potential synapse. This factor, however, varied between projections and, in one case, even within a projection, up to 20-fold. Relationships between geometric overlap and synaptic strength thus depend on the laminar and columnar locations of both the pre- and postsynaptic neurons, even for neurons of the same type. A large plasticity potential appears to be incorporate...

2005

Current Biology, 2007

Current opinion in neurobiology, 2015

Despite considerable effort over a century and the benefit of remarkable technical advances in the past few decades, we are still far from understanding mammalian cerebral neocortex. With its six layers, modular architecture, canonical circuits, innumerable cell types, and computational complexity, isocortex remains a challenging mystery. In this review, we argue that identifying the structural and functional similarities between mammalian piriform cortex and reptilian dorsal cortex could help reveal common organizational and computational principles and by extension, some of the most primordial computations carried out in cortical networks.

Preprints, 2023

The basis for computation in the brain is the quantum threshold of the ‘soliton’ accompanying the ion changes of the action potential and the refractory membrane at convergences. We provide a logical explanation from the action potential to a neuronal model of the coding and computation of the retina and have explained how the visual cortex operates by quantum phase processing. In the small world network parallel frequencies collide into definable patterns of distinct objects. Elsewhere we have shown how many sensory cells are mean sampled to a single neuron and that convergences of neurons are common. We have also demonstrated, using the threshold and refractory period of a quantum phase pulse, that action potentials diffract across a neural network due to the annulment of parallel collisions in phase ternary computation (PTC). Thus, PTC applied to neuron convergences results in collective mean sampled frequency and is the only mathematical solution within the constraints of brain neural networks (BNN). In the retina and other sensory areas, we discuss how this information is coded and then understood in terms of network abstracts within the lateral geniculate nucleus (LGN) and visual cortex. First by defined neural patterning within a neural network, and then in terms of contextual networks, we demonstrate that the output of frequencies from the visual cortex contain information amounting to abstract representations of objects in increasing detail. We show that nerve tracts from the LGN provide time synchronisation to the neocortex (defined as the location of the combination of connections of the visual cortex, motor cortex, auditory cortex, etc). The full image is therefore combined in the neocortex with other sensory modalities so that it receives information about the object from the eye, and all abstracts that make up the object. Spatial patterns in the visual cortex are formed from individual patterns illuminating the retina and memory is encoded by reverberatory loops of computational actions potentials (CAPs). We demonstrate that a similar process of PTC may take place in the cochlea and associated ganglia, as well as ascending information from the spinal cord, and that this function should be considered universal where convergences of neurons occur.

Nature Neuroscience, 2011

The cytoarchitectonic similarities of different neocortical regions have given rise to the idea of "canonical" connectivity between excitatory neurons of different layers within a column. It is unclear whether similarly general organizational principles also exist for inhibitory neocortical circuits. Here, we delineate and compare local inhibitory-to-excitatory wiring patterns in all principal layers of primary motor (M1), somatosensory (S1), and visual cortex (V1), using genetically targeted photostimulation in a mouse knock-in line that conditionally expresses channelrhodopsin-2 in GABAergic neurons. Inhibitory inputs to excitatory neurons derive largely from the same cortical layer within a three-column diameter. However, subsets of pyramidal cells in layers 2/3 and 5B receive extensive translaminar inhibition. These neurons are prominent in V1, where they might correspond to complex cells, less numerous in barrel cortex, and absent in M1. Although inhibitory connection patterns are stereotypical, the abundance of individual motifs varies between regions and cells, potentially reflecting functional specializations. The anatomical fine structure of the neocortex is remarkably uniform, suggesting extensive replication of a limited number of circuit motifs 1. In support of this view, the excitatory connections of different neocortical areas in different species appear to conform, with minor variations 2-5 , to the "canonical" laminar organization first described in cat visual cortex 6-9 : Thalamic afferents arrive in layer 4 (L4), whose neurons project to L2 and L3. Axonal projections of pyramidal cells in these layers terminate in L5 and some of those from L5 in L6. It has been difficult to determine whether similarly general principles also hold for the organization of inhibitory neocortical circuits 10 , 11. Systematic studies of inhibitory connectivity have been hampered by the relative sparseness of inhibitory neurons and a bewildering diversity of cell types 10-14. While the rules governing the interneuron typespecific positioning of inhibitory terminals on post-synaptic target cells are increasingly well Correspondence should be addressed to G. M.

Neural Computation, 1989

We have used microanatomy derived from single neurons, and in vivo intracellular recordings to develop a simplified circuit of the visual cortex. The circuit explains the intracellular responses to pulse stimulation in terms of the interactions between three basic populations of neurons, and reveals the following features of cortical processing that are important to computational theories of neocortex. First, inhibition and excitation are not separable events. Activation of the cortex inevitably sets in motion a sequence of excitation and inhibition in every neuron. Second, the thalamic input does not provide the major excitation arriving at any neuron. Instead the intracortical excitatory connections provide most of the excitation. Third, the time evolution of excitation and inhibition is far longer than the synaptic delays of the circuits involved. This means that cortical processing cannot rely on precise timing between individual synaptic inputs.

Trends in Neurosciences, 1992

Science degree in Cognitive Science. He came to the University of Rochester in the Fall of 2004 and began graduate studies in Neuroscience. In the Fall of 2005, he pursued a master of Science degree in Biomedical Engineering concurrent with his studies in Neuroscience, completing the degree in 2006. He pursued his research in neocortical circuit processing of temporal information under the direction of Professor David J Pinto, and received the Master of Science degree in Neurobiology from the University of Rochester in 2009. Publications Pesavento, M. J., & Pinto, D. J. (2010a). Contribution of network connections to response processing of simulated thalamocortical input in vitro. Neuron (in preparation).

Annals of the New York Academy of Sciences, 1997

What would a satisfactory theory of higher brain function look like? At one level of detail, one would want to know how properties of neurons are determined by their connections and ion channels, and how the properties of these ion channels are in turn determined by their molecular structure. However, although an understanding at this level of detail is certainly required, it is far from sufficient. To account for higher brain function, one needs a way to link function on a cellular and molecular level to perception, behavior, and consciousness.

Cell, 2014

Numerous studies have examined the neuronal inputs and outputs of many areas within the mammalian cerebral cortex, but how these areas are organized into neural networks that communicate across the entire cortex is unclear. Over 600 labeled neuronal pathways acquired from tracer injections placed across the entire mouse neocortex enabled us to generate a cortical connectivity atlas. A total of 240 intracortical connections were manually reconstructed within a common neuroanatomic framework, forming a cortico-cortical connectivity map that facilitates comparison of connections from different cortical targets. Connectivity matrices were generated to provide an overview of all intracortical connections and subnetwork clusterings. The connectivity matrices and cortical map revealed that the entire cortex is organized into four somatic sensorimotor, two medial, and two lateral subnetworks that display unique topologies and can interact through select cortical areas. Together, these data provide a resource that can be used to further investigate cortical networks and their corresponding functions.

2022

In the cortex, the interplay between excitation and inhibition determines the fidelity of neuronal representations. However, while the receptive fields of excitatory neurons are often fine-tuned to the encoded features, the principles governing the tuning of inhibitory neurons are still elusive. We addressed this problem by recording populations of neurons in the postsubiculum (PoSub), a cortical area where the receptive fields of most excitatory neurons correspond to a specific headdirection (HD). In contrast to PoSub-HD cells, the tuning of fast-spiking (FS) cells, the largest class of cortical inhibitory neurons, was broad and heterogeneous. However, we found that PoSub-FS cell tuning curves were fine-tuned in the spatial frequency domain, resulting in various radial symmetries in their HD tuning. In addition, the average frequency spectrum of PoSub-FS cell populations was virtually indistinguishable from that of PoSub-HD cells but different from that of the upstream thalamic HD cells, suggesting that this co-tuning in the frequency domain has a local origin. Two observations corroborated this hypothesis. First, PoSub-FS cell tuning was independent of upstream thalamic inputs. Second, PoSub-FS cell tuning was tightly coupled to PoSub-HD cell activity even during sleep. Together, these findings provide evidence that the resolution of neuronal tuning is an intrinsic property of local cortical networks, shared by both excitatory and inhibitory cell populations. We hypothesize that this reciprocal encoding supports two parallel streams of information processing in thalamocortical networks. .