Neural Coding Principles
Neural Coding Principles - Related Videos
09:40
Identification of Coding and Non-coding RNA Classes Expressed in Swine Whole Blood
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2018
Here, we present a protocol optimized for the processing of coding (mRNA) and non-coding (ncRNA) globin reduced RNA-seq libraries from a single whole blood sample.
08:37
Le Châtelier's Principle
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2023
Source: Laboratory of Dr. Lynne O'Connell — Boston College When the conditions of a system at equilibrium are altered, the system responds in such a way as to maintain the equilibrium. In 1888, Henri-Lewis Le Châtelier described this phenomenon in a principle that states, "When a change in temperature, pressure, or concentration disturbs a system in chemical equilibrium, the change will be counteracted by an alteration in the equilibrium composition." This experiment demonstrates Le Châtelier's...
01:49
Hardy-Weinberg Principle
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2026
Diploid organisms have two alleles of each gene, one from each parent, in their somatic cells. Therefore, each individual contributes two alleles to the gene pool of the population. The gene pool of a population is the sum of every allele of all genes within that population and has some degree of variation. Genetic variation is typically expressed as a relative frequency, which is the percentage of the total population that has a given allele, genotype or phenotype.In the early 20th century,...
02:39
lncRNA - Long Non-coding RNAs
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2020
In humans, more than 80% of the genome gets transcribed. However, only around 2% of the genome codes for proteins. The remaining part produces non-coding RNAs which includes ribosomal RNAs, transfer RNAs, telomerase RNAs, and regulatory RNAs, among other types. A large number of regulatory non-coding RNAs have been classified into two groups depending upon their length – small non-coding RNAs, such as microRNA, which are less than 200 nucleotides in length, and long non-coding RNA (lncRNA)...
04:08
The Uncertainty Principle
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2020
Werner Heisenberg considered the limits of how accurately one can measure properties of an electron or other microscopic particles. He determined that there is a fundamental limit to how accurately one can measure both a particle’s position and its momentum simultaneously. The more accurate the measurement of the momentum of a particle is known, the less accurate the position at that time is known and vice versa. This is what is now called the Heisenberg uncertainty principle. He mathematically...
FAQs
Firing rate and spike timing provide complementary descriptions of neural activity. The number of spikes over a period can carry information about stimulus intensity or identity, while the precise timing of spikes can represent temporal structure. Considering both measures helps distinguish signals that might appear similar when analyzed only by average activity.
Synapses and network circuits do more than pass information unchanged. They decode incoming activity, reshape its pattern, and determine how signals are integrated before reaching other neurons. This transformation allows the nervous system to modify representations as information moves through a circuit, supporting links between sensory signals, behavior, and decisions.
Coordinated activity across populations can represent information that is not fully captured by any one neuron. Different neurons may contribute signals about stimulus features such as location, timing, or identity, and their combined activity can provide a richer representation. Studying populations therefore helps explain how neural systems support perception and motor control.
Researchers examine patterns of electrical activity in relation to the stimuli, behaviors, or decisions under study. They may compare firing rates, spike timing, and coordinated activity across neurons to determine which features are represented. These analyses connect recorded brain signals with computational models and help clarify how information is transformed within neural circuits.
Brain-computer interfaces rely on relationships between recorded neural activity and intended actions or other relevant information. Coding principles guide the interpretation of firing rates, spike timing, or population activity so that signals can be linked to behavior. This framework supports systems designed to translate brain activity into useful control information.
The same coding framework can be used to study how sensory features are represented, how motor commands are organized, and how activity changes during learning or decisions. Comparing neural signals across these functions helps researchers trace information from stimulus processing to behavior, while also providing context for disorders involving disrupted neural communication.