Tag: Brain Science

Over the past thirty years a significant amount of research has accumulated showing the correlation of oscillatory activity with learning, memory, and perception. A new paper “Oscillatory Synchronization in Large-Scale Cortical Networks Predicts Perception” (published January 27, 2011 in Neuron) provides evidence that dynamic networks across cortical areas phase-lock and synchronize their oscillatory activity to support perception. The research team developed a new analysis method using electroencephalography (EEG) and magnetic resonance imaging (MRI) data that enabled an unbiased search for synchronized networks across the entire human brain.

Note: Supplemental information for this paper is available in a twenty-two page PDF file.

In the experiments described in this paper, human subjects reported the way they experienced an ambiguous audiovisual stimulus of two approaching bars that crossed over and then continued to move apart from each other. At the moment that the two bars crossed, a click sound was played. Perception of this stimulus spontaneously alternated between two bars bouncing off of each other and one bar passing the other. The addition of the click increased the relative frequency that subjects saw the bars bouncing off each other, which points to the integration of the visual and auditory stimuli.

The major finding emerged when the authors compared cortico-cortical coherence at the source level between stimulation and baseline periods. A highly structured cortical network showed enhanced beta frequency coherence (15–23 Hertz) during stimulation. This network included the extrastriate visual areas, the frontal eye fields, and posterior parietal and temporal cortices. Most striking, the authors found that beta synchrony was not only enhanced during stimulus processing, but also predicted the subject’s perception of the stimulus as two bars bouncing off of each other.

Other related blog posts:

Brain Modeling Using NEURON, Interneurons, and Resonant Circuits

A paper we examined last year showed that spikelet activity in CA1 hippocampal pyramidal cells displayed a preference for place (like action potentials in place cells; see blog post “Spikelets and Place Cells“). They also saw evidence that spikelets sometimes induced full action potentials as recorded in the soma of pyramidal cells.

A new paper “Slow integration leads to persistent action potential firing in distal axons of coupled interneurons” (published February 2011 in Nature Neuroscience) describes a previously unknown slow integration of action potentials that results in the abrupt appearance of action potentials in the distant segments of CA1 hippocampal interneuron axons that may persist in firing for tens of seconds to minutes. Perhaps most important, natural firing patterns of action potentials elicited persistent firing in the distal axons of these interneurons.

The authors showed that the persistent spiking activity in or near the distal axon could be shared by multiple neurons without a requirement for somatic or dendritic depolarization or synaptic interactions through excitatory or inhibitory receptors. In fact, they show that spikelets seem to be reflections of action potentials in distant axon branches that never interact with the interneuron’s cell body or dendrites.

Note: The authors present some preliminary evidence that the persistent firing of action potentials in distant axon segments may depend on gap junctions. This is particularly interesting given the recent report on the role played by gap junctions in learning and memory (see my blog post “Electrical Synapses are Important for Learning and Memory“).

Other related blog posts:

Spikelets and Place Cells

Electrical Synapses are Important for Learning and Memory

Typical approaches for studying neural coding focus on information transmission in neural circuits by quantifying how easily a stimulus can be recovered from evoked neural responses. These studies provide statistical descriptions of the mapping between stimuli and evoked responses.

An alternative approach assesses the “statistical optimality of an internal model for probabilistic inference.” As best I can tell this approach considers a sensory system to hold a statistically optimal internal model of the environment if the neuronal activity evoked by a stimulus closely matches spontaneous activity in the system. In a new paper that takes this approach (“Spontaneous Cortical Activity Reveals Hallmarks of an Optimal Internal Model of the Environment” published January 7, 2011 in Science) the authors argue that neural activity is the result of the interaction between an internal model of the environment, embedded in underlying neural circuits, and the sensory input.

Note: The paper under review has an associated 23 page PDF file containing supplementary material. Download the supplementary material from here.

In this paper, they describe experiments in the primary visual cortex that test if evoked neuronal responses to natural visual stimuli closely match spontaneous neuronal responses. The analysis is formally based on the concepts of the likelihood of features and the prior distribution of features. The likelihood of features describes the probability with which any given input image can be expected to arise from a particular combination of features. The prior distribution of features is the probability with which any particular combination of features can be expected to occur.

Spontaneous activity in the sensory cortex reflects the prior distribution of features. In contrast, stimulus evoked activity in the sensory cortex reflects the posterior distribution, which describes the probability that any given combination of features may have given rise to a particular input. The posterior distribution can be computed by Bayes’ rule from the likelihood of features and the prior distribution of features.

The authors carried out the tests to see if evoked neuronal responses to natural visual stimuli closely matched spontaneous neuronal responses in animals at four different ages:

• After the eyes first open.
• After the maturation of orientation tuning and long range horizontal connections in the primary visual cortex.
• In the young adult with a fully matured primary visual cortex.
• In the older adult with a fully matured primary visual cortex.

They found that the similarity between the spontaneous and evoked neuronal activities in the primary visual cortex increased with age and was specific to responses evoked by natural scenes. Their interpretation was that this showed a progressive “adaptation of internal models to the statistics of natural stimuli at the neural level.”