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78-578: The Princeton Neuroscience Institute was created in 2004 under the leadership of psychology professor Jonathan D. Cohen and molecular biology professor David Tank , who served as Co-Directors of the PNI until 2023. Cohen joined Princeton in 1998 and specializes in cognitive neuroscience. He is the scientific director of the Scully Center for the Neuroscience of Mind and Behavior within the PNI. He has also directed

156-683: A certain odor, but is not completely necessary, as average spike count over the course of the animal's sniffing was also a good identifier. Along the same lines, experiments done with the olfactory system of rabbits showed distinct patterns which correlated with different subsets of odorants, and a similar result was obtained in experiments with the locust olfactory system. The specificity of temporal coding requires highly refined technology to measure informative, reliable, experimental data. Advances made in optogenetics allow neurologists to control spikes in individual neurons, offering electrical and spatial single-cell resolution. For example, blue light causes

234-403: A cycle of gamma oscillation, each neuron has its own preferred relative firing time. As a result, an entire population of neurons generates a firing sequence that has a duration of up to about 15 ms. Population coding is a method to represent stimuli by using the joint activities of a number of neurons. In population coding, each neuron has a distribution of responses over some set of inputs, and

312-427: A firing rate and a preferred direction), the sum points in the direction of motion. In this manner, the population of neurons codes the signal for the motion. This particular population code is referred to as population vector coding. Place-time population codes, termed the averaged-localized-synchronized-response (ALSR) code, have been derived for neural representation of auditory acoustic stimuli. This exploits both

390-686: A former University trustee, established the program in 2007. Founded in 2007, the Regina and John Scully '66 Center for the Neuroscience of Mind and Behavior analyzes how physical mechanisms of the brain give rise to the functions of the mind. Jonathan D. Cohen Jonathan David Cohen (born October 5, 1955) is an American psychologist and cognitive neuroscientist . He is the Robert Bendheim and Lynn Bendheim Thoman Professor in Neuroscience and Professor of Psychology at Princeton University , where he

468-407: A multivariate distribution of the neuronal responses. These models can assume independence, second order correlations, or even more detailed dependencies such as higher order maximum entropy models , or copulas . The correlation coding model of neuronal firing claims that correlations between action potentials , or "spikes", within a spike train may carry additional information above and beyond

546-677: A number of research awards, including the James M. Shapiro ’80 Fund for Undergraduate Research in Neuroscience, the Nancy J. Newman, MD ’78 & Valerie Biousse, MD Senior Thesis Research Fund for Neuroscience, and the Sanda & Jeremiah Lambert ’55 Fund for Undergraduate Neuroscience, in Honor of Clare Lambert ’08 and Hilary Lambert ‘10. The graduate program in Neuroscience is designed to prepare students for careers as in academia or in industry. Students may select one of

624-401: A population of unimodal tuning curves, i.e. with a single peak, the precision typically scales linearly with the number of neurons. Hence, for half the precision, half as many neurons are required. In contrast, when the tuning curves have multiple peaks, as in grid cells that represent space, the precision of the population can scale exponentially with the number of neurons. This greatly reduces

702-547: A rate code. Temporal codes (also called spike codes ), employ those features of the spiking activity that cannot be described by the firing rate. For example, time-to-first-spike after the stimulus onset, phase-of-firing with respect to background oscillations, characteristics based on the second and higher statistical moments of the ISI probability distribution , spike randomness, or precisely timed groups of spikes ( temporal patterns ) are candidates for temporal codes. As there

780-437: A rate coding assumption, any information possibly encoded in the temporal structure of the spike train is ignored. Consequently, rate coding is inefficient but highly robust with respect to the ISI ' noise '. During rate coding, precisely calculating firing rate is very important. In fact, the term "firing rate" has a few different definitions, which refer to different averaging procedures, such as an average over time (rate as

858-551: A second or more to accumulate enough information. This is not consistent with numerous organisms which are able to discriminate between stimuli in the time frame of milliseconds, suggesting that a rate code is not the only model at work. To account for the fast encoding of visual stimuli, it has been suggested that neurons of the retina encode visual information in the latency time between stimulus onset and first action potential, also called latency to first spike or time-to-first-spike. This type of temporal coding has been shown also in

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936-587: A series of all-or-none point events in time. The lengths of interspike intervals ( ISIs ) between two successive spikes in a spike train often vary, apparently randomly. The study of neural coding involves measuring and characterizing how stimulus attributes, such as light or sound intensity, or motor actions, such as the direction of an arm movement, are represented by neuron action potentials or spikes. In order to describe and analyze neuronal firing, statistical methods and methods of probability theory and stochastic point processes have been widely applied. With

1014-419: A single neuron's signal. When monkeys are trained to move a joystick towards a lit target, a single neuron will fire for multiple target directions. However it fires the fastest for one direction and more slowly depending on how close the target was to the neuron's "preferred" direction. If each neuron represents movement in its preferred direction, and the vector sum of all neurons is calculated (each neuron has

1092-447: A single-neuron spike count) or an average over several repetitions (rate of PSTH) of experiment. In rate coding, learning is based on activity-dependent synaptic weight modifications. Rate coding was originally shown by Edgar Adrian and Yngve Zotterman in 1926. In this simple experiment different weights were hung from a muscle . As the weight of the stimulus increased, the number of spikes recorded from sensory nerves innervating

1170-434: A special case of spike-timing-dependent plasticity . The issue of temporal coding is distinct and independent from the issue of independent-spike coding. If each spike is independent of all the other spikes in the train, the temporal character of the neural code is determined by the behavior of time-dependent firing rate r(t). If r(t) varies slowly with time, the code is typically called a rate code, and if it varies rapidly,

1248-462: Is a neuroscience field concerned with characterising the hypothetical relationship between the stimulus and the neuronal responses, and the relationship among the electrical activities of the neurons in the ensemble . Based on the theory that sensory and other information is represented in the brain by networks of neurons , it is believed that neurons can encode both digital and analog information. Neurons have an ability uncommon among

1326-418: Is a neural coding scheme that combines the spike count code with a time reference based on oscillations . This type of code takes into account a time label for each spike according to a time reference based on phase of local ongoing oscillations at low or high frequencies. It has been shown that neurons in some cortical sensory areas encode rich naturalistic stimuli in terms of their spike times relative to

1404-469: Is a topic of intense debate within the neuroscience community, even though there is no clear definition of what these terms mean. The rate coding model of neuronal firing communication states that as the intensity of a stimulus increases, the frequency or rate of action potentials , or "spike firing", increases. Rate coding is sometimes called frequency coding. Rate coding is a traditional coding scheme, assuming that most, if not all, information about

1482-403: Is also much faster than rate coding and can reflect changes in the stimulus conditions nearly instantaneously. Individual neurons in such a population typically have different but overlapping selectivities, so that many neurons, but not necessarily all, respond to a given stimulus. Typically an encoding function has a peak value such that activity of the neuron is greatest if the perceptual value

1560-600: Is also one of the few universities in the country to offer a graduate and postdoctoral program in Quantitative and Computational Neuroscientists. Research at the PNI spans the disciplines of molecular , cellular , systems , and cognitive neuroscience . The PNI is also especially dedicated to computational research. The PNI directs a number of programs and projects, including the Intel Labs and PNI Project, Rutgers-Princeton Center for Computational Cognitive Neuropsychiatry, and

1638-403: Is also the fraction of trials on which a spike occurred between those times. Equivalently, r(t)Δt is the probability that a spike occurs during this time interval. As an experimental procedure, the time-dependent firing rate measure is a useful method to evaluate neuronal activity, in particular in the case of time-dependent stimuli. The obvious problem with this approach is that it can not be

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1716-664: Is also the founding co-director of the Princeton Neuroscience Institute . He originally joined the faculty of Princeton in 1998, and became the founding director of the Center for the Study of Brain, Mind, and Behavior in 2000. A noted expert on neuroimaging , he played a major role in increasing the use of fMRI scanners in scientific research. He has been a fellow of the Association for Psychological Science since 2007 and of

1794-554: Is an abundance of information present in temporal patterns across populations of neurons, and this information is different from that which is determined by rate coding schemes. Groups of neurons may synchronize in response to a stimulus. In studies dealing with the front cortical portion of the brain in primates, precise patterns with short time scales only a few milliseconds in length were found across small populations of neurons which correlated with certain information processing behaviors. However, little information could be determined from

1872-403: Is close to the peak value, and becomes reduced accordingly for values less close to the peak value. It follows that the actual perceived value can be reconstructed from the overall pattern of activity in the set of neurons. Vector coding is an example of simple averaging. A more sophisticated mathematical technique for performing such a reconstruction is the method of maximum likelihood based on

1950-412: Is encoded not only in the firing rate but also in spike timing. More generally, whenever a rapid response of an organism is required a firing rate defined as a spike-count over a few hundred milliseconds is simply too slow. The time-dependent firing rate is defined as the average number of spikes (averaged over trials) appearing during a short interval between times t and t+Δt, divided by the duration of

2028-436: Is especially important for sound localization , which occurs within the brain on the order of milliseconds. The brain must obtain a large quantity of information based on a relatively short neural response. Additionally, if low firing rates on the order of ten spikes per second must be distinguished from arbitrarily close rate coding for different stimuli, then a neuron trying to discriminate these two stimuli may need to wait for

2106-464: Is especially important when the spike rate reaches its limit, as in high-contrast situations. For this reason, temporal coding may play a part in coding defined edges rather than gradual transitions. The mammalian gustatory system is useful for studying temporal coding because of its fairly distinct stimuli and the easily discernible responses of the organism. Temporally encoded information may help an organism discriminate between different tastants of

2184-412: Is experimentally easier to record from a single neuron and average over N repeated runs. Thus, the time-dependent firing rate coding relies on the implicit assumption that there are always populations of neurons. When precise spike timing or high-frequency firing-rate fluctuations are found to carry information, the neural code is often identified as a temporal code. A number of studies have found that

2262-462: Is hardly stationary, but often changing on a fast time scale. For example, even when viewing a static image, humans perform saccades , rapid changes of the direction of gaze. The image projected onto the retinal photoreceptors changes therefore every few hundred milliseconds ( Chapter 1.5 in ) Despite its shortcomings, the concept of a spike-count rate code is widely used not only in experiments, but also in models of neural networks . It has led to

2340-422: Is higher information rates capable of encoding more states (i.e. higher fidelity) than spiking neurons. Although action potentials can vary somewhat in duration, amplitude and shape, they are typically treated as identical stereotyped events in neural coding studies. If the brief duration of an action potential (about 1 ms) is ignored, an action potential sequence, or spike train, can be characterized simply by

2418-407: Is independent of each other spike within the spike train . A typical population code involves neurons with a Gaussian tuning curve whose means vary linearly with the stimulus intensity, meaning that the neuron responds most strongly (in terms of spikes per second) to a stimulus near the mean. The actual intensity could be recovered as the stimulus level corresponding to the mean of the neuron with

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2496-432: Is no absolute time reference in the nervous system, the information is carried either in terms of the relative timing of spikes in a population of neurons (temporal patterns) or with respect to an ongoing brain oscillation (phase of firing). One way in which temporal codes are decoded, in presence of neural oscillations , is that spikes occurring at specific phases of an oscillatory cycle are more effective in depolarizing

2574-440: Is obtained by counting the number of spikes that appear during a trial and dividing by the duration of trial. The length T of the time window is set by the experimenter and depends on the type of neuron recorded from and to the stimulus. In practice, to get sensible averages, several spikes should occur within the time window. Typical values are T = 100 ms or T = 500 ms, but the duration may also be longer or shorter ( Chapter 1.5 in

2652-409: Is that global features such as pitch or formant transition profiles can be represented as global features across the entire nerve simultaneously via both rate and place coding. Population coding has a number of other advantages as well, including reduction of uncertainty due to neuronal variability and the ability to represent a number of different stimulus attributes simultaneously. Population coding

2730-749: The American Association for the Advancement of Science since 2012. He is a recipient of the Joseph Zubin Memorial Fund Award , the APA Award for Distinguished Scientific Contributions to Psychology , and the Association for Psychological Science's William James Fellow Award . This article about a neuroscientist is a stub . You can help Misplaced Pages by expanding it . Neural coding Neural coding (or neural representation )

2808-522: The nervous system . Prior to Princeton, he was a researcher at Bell Laboratories from 1983 to 2001. He earned his Ph.D. in Physics from Cornell University is currently a member of the National Academy of Sciences . Moneo Arquitecto , an international architecture and design firm , was commissioned to design the new neuroscience and psychology buildings in 2006. The building is 248,00 square feet, and

2886-490: The post-synaptic neuron . The temporal structure of a spike train or firing rate evoked by a stimulus is determined both by the dynamics of the stimulus and by the nature of the neural encoding process. Stimuli that change rapidly tend to generate precisely timed spikes (and rapidly changing firing rates in PSTHs) no matter what neural coding strategy is being used. Temporal coding in the narrow sense refers to temporal precision in

2964-422: The receptive fields of simple cells in the visual cortex. The capacity of sparse codes may be increased by simultaneous use of temporal coding, as found in the locust olfactory system. Given a potentially large set of input patterns, sparse coding algorithms (e.g. sparse autoencoder ) attempt to automatically find a small number of representative patterns which, when combined in the right proportions, reproduce

3042-535: The undergraduate certificate program in neuroscience since 2001. Cohen earned his M.D. from the University of Pennsylvania and a Ph.D. in Cognitive Psychology from Carnegie Mellon University . He did an internship and his residency in psychiatry at Stanford University School of Medicine . Tank joined Princeton's faculty in 2001, where he specializes in physics-based measurement techniques to study

3120-574: The International Brain Lab (IBL). The Bezos Center for Neural Circuit Dynamics was founded by Jeff and MacKenzie Bezos to focus on the development of microscopy imaging techniques for measuring neural circuit dynamics in the functioning brain. The Center hosts a number of custom-built optical instrumentation for large-scale monitoring and optogenetic perturbation of neural activity. The McDonnell Center for Systems Neuroscience specializes on neural coding and dynamics. James McDonnell III,

3198-408: The auditory and somato-sensory system. The main drawback of such a coding scheme is its sensitivity to intrinsic neuronal fluctuations. In the primary visual cortex of macaques, the timing of the first spike relative to the start of the stimulus was found to provide more information than the interval between spikes. However, the interspike interval could be used to encode additional information, which

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3276-399: The average firing rate of two pairs of neurons. A good example of this exists in the pentobarbital-anesthetized marmoset auditory cortex, in which a pure tone causes an increase in the number of correlated spikes, but not an increase in the mean firing rate, of pairs of neurons. The independent-spike coding model of neuronal firing claims that each individual action potential , or "spike",

3354-455: The brain. For example, in the visual area medial temporal (MT), neurons are tuned to the direction of object motion. In response to an object moving in a particular direction, many neurons in MT fire with a noise-corrupted and bell-shaped activity pattern across the population. The moving direction of the object is retrieved from the population activity, to be immune from the fluctuation existing in

3432-421: The cells of the body to propagate signals rapidly over large distances by generating characteristic electrical pulses called action potentials : voltage spikes that can travel down axons. Sensory neurons change their activities by firing sequences of action potentials in various temporal patterns, with the presence of external sensory stimuli, such as light , sound , taste , smell and touch . Information about

3510-425: The code is called temporal. For very brief stimuli, a neuron's maximum firing rate may not be fast enough to produce more than a single spike. Due to the density of information about the abbreviated stimulus contained in this single spike, it would seem that the timing of the spike itself would have to convey more information than simply the average frequency of action potentials over a given period of time. This model

3588-464: The code while looking only at mean firing rates. Understanding any temporally encoded aspects of the neural code and replicating these sequences in neurons could allow for greater control and treatment of neurological disorders such as depression , schizophrenia , and Parkinson's disease . Regulation of spike intervals in single cells more precisely controls brain activity than the addition of pharmacological agents intravenously. Phase-of-firing code

3666-405: The coding scheme used by neurons in the brain. Neurons can not wait for the stimuli to repeatedly present in an exactly same manner before generating a response. Nevertheless, the experimental time-dependent firing rate measure can make sense, if there are large populations of independent neurons that receive the same stimulus. Instead of recording from a population of N neurons in a single run, it

3744-504: The development of large-scale neural recording and decoding technologies, researchers have begun to crack the neural code and have already provided the first glimpse into the real-time neural code as memory is formed and recalled in the hippocampus, a brain region known to be central for memory formation. Neuroscientists have initiated several large-scale brain decoding projects. The link between stimulus and response can be studied from two opposite points of view. Neural encoding refers to

3822-416: The following areas of research: systems and circuits, human neuroscience, or theory and computation. The PNI also offers a joint graduate degree program in neuroscience, which is designed for students who are interested in an interdisciplinary approach to neuroscience. Prospective applicants may work in a number of other related departments, including Psychology , Molecular Biology , or Philosophy . Princeton

3900-422: The greatest response. However, the noise inherent in neural responses means that a maximum likelihood estimation function is more accurate. This type of code is used to encode continuous variables such as joint position, eye position, color, or sound frequency. Any individual neuron is too noisy to faithfully encode the variable using rate coding, but an entire population ensures greater fidelity and precision. For

3978-461: The idea that a neuron transforms information about a single input variable (the stimulus strength) into a single continuous output variable (the firing rate). There is a growing body of evidence that in Purkinje neurons , at least, information is not simply encoded in firing but also in the timing and duration of non-firing, quiescent periods. There is also evidence from retinal cells, that information

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4056-410: The interval length Δt yields time-dependent firing rate r(t) of the neuron, which is equivalent to the spike density of PSTH ( Chapter 1.5 in ). For sufficiently small Δt, r(t)Δt is the average number of spikes occurring between times t and t+Δt over multiple trials. If Δt is small, there will never be more than one spike within the interval between t and t+Δt on any given trial. This means that r(t)Δt

4134-408: The interval. It works for stationary as well as for time-dependent stimuli. To experimentally measure the time-dependent firing rate, the experimenter records from a neuron while stimulating with some input sequence. The same stimulation sequence is repeated several times and the neuronal response is reported in a Peri-Stimulus-Time Histogram (PSTH). The time t is measured with respect to the start of

4212-419: The light-gated ion channel channelrhodopsin to open, depolarizing the cell and producing a spike. When blue light is not sensed by the cell, the channel closes, and the neuron ceases to spike. The pattern of the spikes matches the pattern of the blue light stimuli. By inserting channelrhodopsin gene sequences into mouse DNA, researchers can control spikes and therefore certain behaviors of the mouse (e.g., making

4290-496: The map from stimulus to response. The main focus is to understand how neurons respond to a wide variety of stimuli, and to construct models that attempt to predict responses to other stimuli. Neural decoding refers to the reverse map, from response to stimulus, and the challenge is to reconstruct a stimulus, or certain aspects of that stimulus, from the spike sequences it evokes. A sequence, or 'train', of spikes may contain information based on different coding schemes. In some neurons

4368-536: The mouse turn left). Researchers, through optogenetics, have the tools to effect different temporal codes in a neuron while maintaining the same mean firing rate, and thereby can test whether or not temporal coding occurs in specific neural circuits. Optogenetic technology also has the potential to enable the correction of spike abnormalities at the root of several neurological and psychological disorders. If neurons do encode information in individual spike timing patterns, key signals could be missed by attempting to crack

4446-427: The muscle also increased. From these original experiments, Adrian and Zotterman concluded that action potentials were unitary events, and that the frequency of events, and not individual event magnitude, was the basis for most inter-neuronal communication. In the following decades, measurement of firing rates became a standard tool for describing the properties of all types of sensory or cortical neurons, partly due to

4524-422: The nervous system only used rate codes to convey information, a more consistent, regular firing rate would have been evolutionarily advantageous, and neurons would have utilized this code over other less robust options. Temporal coding supplies an alternate explanation for the “noise," suggesting that it actually encodes information and affects neural processing. To model this idea, binary symbols can be used to mark

4602-506: The number of neurons required for the same precision. The sparse code is when each item is encoded by the strong activation of a relatively small set of neurons. For each item to be encoded, this is a different subset of all available neurons. In contrast to sensor-sparse coding, sensor-dense coding implies that all information from possible sensor locations is known. As a consequence, sparseness may be focused on temporal sparseness ("a relatively small number of time periods are active") or on

4680-560: The patterns; one possible theory is they represented the higher-order processing taking place in the brain. As with the visual system, in mitral/tufted cells in the olfactory bulb of mice, first-spike latency relative to the start of a sniffing action seemed to encode much of the information about an odor. This strategy of using spike latency allows for rapid identification of and reaction to an odorant. In addition, some mitral/tufted cells have specific firing patterns for given odorants. This type of extra information could help in recognizing

4758-515: The phase are enough to represent all the information content in this kind of code with respect to the phase of oscillations in low frequencies. Phase-of-firing code is loosely based on the phase precession phenomena observed in place cells of the hippocampus . Another feature of this code is that neurons adhere to a preferred order of spiking between a group of sensory neurons, resulting in firing sequence. Phase code has been shown in visual cortex to involve also high-frequency oscillations . Within

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4836-432: The phase of ongoing network oscillatory fluctuations, rather than only in terms of their spike count. The local field potential signals reflect population (network) oscillations. The phase-of-firing code is often categorized as a temporal code although the time label used for spikes (i.e. the network oscillation phase) is a low-resolution (coarse-grained) reference for time. As a result, often only four discrete values for

4914-406: The place or tuning within the auditory nerve, as well as the phase-locking within each nerve fiber auditory nerve. The first ALSR representation was for steady-state vowels; ALSR representations of pitch and formant frequencies in complex, non-steady state stimuli were later demonstrated for voiced-pitch, and formant representations in consonant-vowel syllables. The advantage of such representations

4992-413: The relative ease of measuring rates experimentally. However, this approach neglects all the information possibly contained in the exact timing of the spikes. During recent years, more and more experimental evidence has suggested that a straightforward firing rate concept based on temporal averaging may be too simplistic to describe brain activity. The spike-count rate, also referred to as temporal average,

5070-488: The response that does not arise solely from the dynamics of the stimulus, but that nevertheless relates to properties of the stimulus. The interplay between stimulus and encoding dynamics makes the identification of a temporal code difficult. In temporal coding, learning can be explained by activity-dependent synaptic delay modifications. The modifications can themselves depend not only on spike rates (rate coding) but also on spike timing patterns (temporal coding), i.e., can be

5148-424: The responses of many neurons may be combined to determine some value about the inputs. From the theoretical point of view, population coding is one of a few mathematically well-formulated problems in neuroscience. It grasps the essential features of neural coding and yet is simple enough for theoretic analysis. Experimental studies have revealed that this coding paradigm is widely used in the sensory and motor areas of

5226-522: The same category (sweet, bitter, sour, salty, umami) that elicit very similar responses in terms of spike count. The temporal component of the pattern elicited by each tastant may be used to determine its identity (e.g., the difference between two bitter tastants, such as quinine and denatonium). In this way, both rate coding and temporal coding may be used in the gustatory system – rate for basic tastant type, temporal for more specific differentiation. Research on mammalian gustatory system has shown that there

5304-438: The simple timing of the spikes. Early work suggested that correlation between spike trains can only reduce, and never increase, the total mutual information present in the two spike trains about a stimulus feature. However, this was later demonstrated to be incorrect. Correlation structure can increase information content if noise and signal correlations are of opposite sign. Correlations can also carry information not present in

5382-439: The sparseness in an activated population of neurons. In this latter case, this may be defined in one time period as the number of activated neurons relative to the total number of neurons in the population. This seems to be a hallmark of neural computations since compared to traditional computers, information is massively distributed across neurons. Sparse coding of natural images produces wavelet -like oriented filters that resemble

5460-423: The spikes: 1 for a spike, 0 for no spike. Temporal coding allows the sequence 000111000111 to mean something different from 001100110011, even though the mean firing rate is the same for both sequences, at 6 spikes/10 ms. Until recently, scientists had put the most emphasis on rate encoding as an explanation for post-synaptic potential patterns. However, functions of the brain are more temporally precise than

5538-454: The stimulation sequence. The Δt must be large enough (typically in the range of one or a few milliseconds) so that there is a sufficient number of spikes within the interval to obtain a reliable estimate of the average. The number of occurrences of spikes n K (t;t+Δt) summed over all repetitions of the experiment divided by the number K of repetitions is a measure of the typical activity of the neuron between time t and t+Δt. A further division by

5616-439: The stimulus is contained in the firing rate of the neuron. Because the sequence of action potentials generated by a given stimulus varies from trial to trial, neuronal responses are typically treated statistically or probabilistically. They may be characterized by firing rates, rather than as specific spike sequences. In most sensory systems, the firing rate increases, generally non-linearly, with increasing stimulus intensity. Under

5694-537: The stimulus is encoded in this pattern of action potentials and transmitted into and around the brain. Beyond this, specialized neurons, such as those of the retina, can communicate more information through graded potentials . These differ from action potentials because information about the strength of a stimulus directly correlates with the strength of the neurons' output. The signal decays much faster for graded potentials, necessitating short inter-neuron distances and high neuronal density. The advantage of graded potentials

5772-448: The strength with which a postsynaptic partner responds may depend solely on the 'firing rate', the average number of spikes per unit time (a 'rate code'). At the other end, a complex ' temporal code ' is based on the precise timing of single spikes. They may be locked to an external stimulus such as in the visual and auditory system or be generated intrinsically by the neural circuitry. Whether neurons use rate coding or temporal coding

5850-554: The temporal resolution of the neural code is on a millisecond time scale, indicating that precise spike timing is a significant element in neural coding. Such codes, that communicate via the time between spikes are also referred to as interpulse interval codes, and have been supported by recent studies. Neurons exhibit high-frequency fluctuations of firing-rates which could be noise or could carry information. Rate coding models suggest that these irregularities are noise, while temporal coding models suggest that they encode information. If

5928-471: The textbook 'Spiking Neuron Models' ). The spike-count rate can be determined from a single trial, but at the expense of losing all temporal resolution about variations in neural response during the course of the trial. Temporal averaging can work well in cases where the stimulus is constant or slowly varying and does not require a fast reaction of the organism — and this is the situation usually encountered in experimental protocols. Real-world input, however,

6006-403: The use of only rate encoding seems to allow. In other words, essential information could be lost due to the inability of the rate code to capture all the available information of the spike train. In addition, responses are different enough between similar (but not identical) stimuli to suggest that the distinct patterns of spikes contain a higher volume of information than is possible to include in

6084-594: Was designed by Rafael Moneo to meet LEED Silver certification. At the undergraduate level, the PNI directs both the Undergraduate Concentration in Neuroscience ( major ) and the Undergraduate Certificate in Neuroscience ( minor ). Both are designed for undergraduate students interested in a wide variety of fields, such as molecular biology , psychology , chemistry , and applied mathematics . Exceptional undergraduate students may qualify for

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