Signal Theory Overview

Towards a Unified Theory of Consciousness & Psychedelic Action

Version 1.04 : 1.13.2006

By James Kent

 

Summary

Signal Theory presents a new model for objectively measuring expanded states of consciousness based on neural firing rate, synchrony of neural spiking, and intensity of signal feedback recursion occurring within the sensory processing circuits of neocortex. Signal Theory also proposes methodologies for mathematically modeling the action of psychedelic 5-HT2A receptor agonists in the production of diminished, amplified, and standing sensory feedback loops in simple neural circuits. Using the basic tenets of Signal Theory, we can define an empirical model of perceptual action in which both normal waking consciousness and expanded psychedelic consciousness can be defined. This document is intended to be a brief overview of the basics of Signal Theory, and is for general public review as well as scientific peer review in the hopes of moving towards a more refined model of consciousness and psychedelic action in the human brain.

Foundations of Consciousness

To understand how Signal Theory can predict specific perceptual alterations to normal waking consciousness, one must understand how the brain parses sensory data to create the moment-to-moment reality we experience as consciousness. There are many definitions of consciousness, but for the purposes of this discussion, consciousness is defined as the ongoing process of the self contextualizing external stimulus. Consciousness is not an end state of being, it is an ongoing feedback process between perception and contextualization; each experience is perceived, parsed into meaningful context, and integrated into memory to provide context for the next experience, ongoing, ad-infinitum, constantly updating through time to create the cohesive narrative identity that we refer to as the self. The three necessary components of waking consciousness are perception (external stimulus), contextualization (internal processing), and memory (data storage and recall). In the human brain, perception is handled by feedback interaction between the sensory organs, the thalamus, the amygdala, and the various advanced sensory processing areas of the neocortex. Contextualization of this sensory data is handled by feedback interaction between sensory processing cortices (external data resolution); the executive areas of the pre-frontal cortex (the PFC, or working memory); and the long-term-memory (LTM) functions of the rhinal- hippocampal complex in the medial temporal lobe (pattern-matching and associative memory). And though memory is stored non-locally (holistically) in associative synaptic connections all over the cortex, LTM compression and recall is handled by the rhinal-hippocampal complex in the medial temporal lobe.

 

The fourth component of human consciousness is, of course, emotional response, which is arguably not-essential to consciousness per se, but is nonetheless integral in all discussions of human consciousness. In a circuit model of the human brain, the amygdala works in tandem with the sensory processing cortices to provide instinctive, sub-rational emotional responses to sensory data, particularly data relating to fear, panic, survival, and mating. The amygdala also has direct feedback connections with the sensory cortices, the PFC, and the LTM structures in the temporal lobe, and is tightly wired into the intensity of both perception and memory storage and recall. These four components of the human brain – perception, contextualization, memory, and emotional response – are the foundations of the Signal Theory of Consciousness as detailed in this text.

Introducing Signal Theory

Signal Theory is the name I have coined for a new method of defining and measuring states of consciousness, particularly when explaining and predicting the mind-expanding powers of  psychedelic chemicals on the brain’s perceptual processing capacity. Signal Theory is derived from Cognitive Theory, a modern school of neuroscience that seeks to derive the functioning of the brain by identifying and following the active neural pathways where specific sensations, thoughts, and cognitive processes arise. Via modern scanning techniques, lesion studies, and pharmacological studies, cognitive scientists have been able to pinpoint areas of the brain responsible for specific functions of consciousness, and have successfully demonstrated how these specific areas of the brain all link up in networked circuits to process raw sensory data into what we perceive of as consciousness from moment to moment.

 

Signal Theory takes the cognitive model one step further, and seeks to define consciousness in terms of the flow of sensory signal through neural processing circuitry; flow that can be measured via neural firing rate, feedback parity within recursive circuitry, and synchrony of neural spike timing between parallel sensory processing circuits. In other words, consciousness is not the hardware itself (neural circuitry), it is the flow of electrical current which passes through that neural circuitry that gives us thought, mood, and sensation. This flow of consciousness alters subtly yet consistently from instant to instant, giving us a near-real-time picture of reality as it unfolds before us. By defining consciousness in terms of signal intensity and feedback recursion within parallel sensory-processing circuits, Signal Theory can empirically demonstrate how the normal functioning of the human mind can be turned up or down; filtered; distorted; gated; delayed; and looped to create an infinite array of classic psychedelic perceptual effects simply by introducing the proper chemical catalyst (such as a psychedelic partial 5-HT2A agonist) into the neural network.

The Role of Feedback Recursion in Analytical Neural Circuitry

Since consciousness is an ongoing process – literally a flow of electrochemical charge through the brain – it can be assumed that this process has a fundamental form and properties that can be accurately measured and logically predicted. We already know much about how signal passes through the brain, and the various neural pathways signal takes as it is processed into conscious thought. What is interesting about sensory signal is that it diverges on many different pathways through various different specialized circuits before finally emerging holistically and fully integrated into what we perceive as real-time consciousness. The processes of maintaining spatial and temporal awareness, identifying incoming data, and maintaining contextual identity all are ongoing and cyclical; which means that neural networks  rely heavily on real-time feedback loops between areas of the sensory cortices, areas of working memory, and areas of LTM. Real-time feedback occurs in the thalamus to screen out unimportant noise; in the sensory cortex to enhance data resolution; in the pre-frontal cortex to maintain holistic contextual awareness; and in the medial temporal lobe to ensure robust signal comprehension and recall. In other words, incoming sensory signal is routinely processed multiple times by various layers of the brain, and then is re-processed and double-checked by working memory to make sure it is accurate before being worked over (yet again) in your sleep before attaining full integration into LTM. The process of data perception, analysis, and memory integration relies heavily on signal recursion through the very same neural circuits over and over again to both ensure data fidelity as well as facilitate neural plasticity and the creation and reinforcement of new synaptic bonds.

 

Signal feedback occurs at many places and on many levels in the brain. Any particular piece of  data may be passed through working memory multiple times until it is fully analyzed and “released” in order to allow new stimulus to update the process of waking consciousness. During this “mulling time” the human subject often drifts into a kind of distracted reverie, much like daydreaming, where obsessive rumination or analysis of a specific data set takes over all active attention. This rumination may take a split second or many minutes depending on context, but if you have ever paused to make sure you understood a question correctly before answering, then you understand how a few moments of re-analysis and subsequent re-parsing of the same data set may yield new insights with each successive pass. Within the tenets of Signal Theory, this process of cyclical rumination is referred to as a recursive feedback circuit, a circuit which can be defined in terms of both structure (neural wiring) as well as recursion intensity, or the rate at which signal makes a complete pass through the entire analytical circuit before starting all over again.

Re-tuning the Cascade of Consciousness

Emerging from the bottom-up, sensation starts at the skin, passes up the brainstem, and reaches the cortex where it diverges into a parallel network of specialized analytic feedback filters. Much like a fountain, sensory signal gushes upward through fat neural pipes and then cascades into a shower of parallel logical circuits -- circuits literally meaning “circles” or “loops” of analytical processing. This is what I refer to as the cascade of consciousness, which is much like a standing wave formation that can analyze and retain any number of contextual cues almost indefinitely. This cascade represents the flow of consciousness through the brain, and can be measured in many ways using many different high-tech scanning devices.

 

In our normal waking state, we take this flow of consciousness for granted, and can assume that in most healthy individuals that the flow of signal through our neural processing circuitry is  tuned and running well enough to keep us upright and functional. However, what happens to this delicate cascade of consciousness when we tweak the flow, upset the filters, re-modulate the recursion, and let the feedback run wild? The fundamental assumption of Signal Theory is that an increase in recursion intensity within the analytical feedback circuits in the cortex would necessarily lead to classic psychedelic perceptual results, and thus classic psychedelic molecules, such as tryptamine 5-HT2A agonists, are prime suspects for facilitating the formation of standing neural feedback recursion in at least one or more layers of sensory processing circuitry.

Signal Theory Presumptions

 

1. Psychedelic 5-HT2A agonists act as neural feedback recursion promoters

The primary presumption of Signal Theory is that psychedelics, though targeted neural excitation, act as promoters for the intensity of feedback recursion occurring within various layers of neural circuitry. This feedback promotion may be stimulated via direct action at the post-synaptic receptor or via secondary action in the form of asynchronous signal leakage from the pre-synaptic axon terminal. The most likely target for this action is in the layer V pyramid cells of the sensory cortices, where 5-HT2A receptors have the greatest density.

 

2. Intensity of Feedback Recursion = Intensity of Psychedelic Experience

A secondary presumption of Signal Theory is that recursion intensity within the iterative analytical processes of the sensory cortices is the primary source of the distinct sensory amplification, perceptual distortions, standing hallucinations, and expanded states of consciousness perceived in the psychedelic state. Feedback recursion within states of consciousness can be measured in terms of zero-gain circuits (normal waking consciousness), amplified-gain circuits (expanded, psychotic, or psychedelic consciousness), or diminished-gain circuits (inhibited, dissociative, or sedated consciousness). Within the realm of psychedelic action, feedback recursion may be modulated upward or downward over the duration of any given psychedelic trip, based on dose taken, external sensory input, and direct user biofeedback.

 

3. There are Optimal Rates of Circuit Recursion and Neural Spike Synchrony

A third presumption of Signal Theory is that there are optimal rates of recursion and circuit synchrony where specific states of consciousness spontaneously manifest. Obviously, normal waking consciousness is a delicately tuned state, and any change, interruption, or perturbation of normal neural firing patterns would necessarily create a corollary change in the subjective state of consciousness. While it is well known that pharmaceuticals can be used to modulate neural firing patterns, there has been very little research into the various non-ordinary states of expanded consciousness generated by psychedelic drugs. Given that there are many specific psychedelic states that appear to be well beyond the normal range of human consciousness, one would expect there to be precise biophysical benchmarks where these states of consciousness emerge. While much attention has been paid to psychedelic effect on the action potentials of individual neurons, Signal Theory suggests that neural firing rate is only one of many factors in measuring psychedelic action. Instead, Signal Theory predicts that psychedelic action is caused by an overall gain in the intensity of signal feedback recursion, and subsequent synchrony of neural spiking occurring within cortical circuitry over the entire duration of pharmacological affect.

 

Much attention is also paid to the ‘peak’ of psychedelic experience, where neural processing appears to take a ‘quantum leap’ beyond normal cognitive functioning, perhaps suggesting a holographic comprehension of reality instead of the normal flat representation we expect. Signal Theory predicts that this ‘peak’ state of psychedelic action can be precisely measured and mathematically modeled against states of normal waking consciousness to empirically demonstrate how amplified feedback recursion and spike synchrony within neural circuits directly affects perception and analytical capacity. Conversely, one would assume that over the course of a psychedelic session that there might be transitional, re-modulatory states that lack synchrony, create confusion and dissonance, and severely distort or interrupt cohesive neural processing. As a practical example of using biofeedback create ‘optimal’ resonant firing patterns, one only need think of a shaman chanting, drumming, singing, and using other rituals that serve to modulate synchrony in neural firing patterns. Within the framework of Signal Theory, the shaman acts as a resonant biofeedback driver in the creation of optimized waves of recursion in the participant’s neural circuitry, thus allowing all participants within the circle to “tune in” to the same level of consciousness where the shamanic state spontaneously manifests.

 

If these three fundamental presumptions of Signal Theory prove to be true, then it will be possible to derive a unified model for describing the entire range of waking and expanded states of consciousness in a way that is empirically demonstrable.

 

A Schematic Description of Signal Theory

To illustrate the fundamentals of Signal Theory, it is helpful to view a basic schematic of sensory processing pathways within the brain. For simplicity, I have chosen to illustrate the audio pathway, though these schematics can be adapted to the visual and somatic pathways as well. The following illustrations represent two different representations of the same audio-processing pathways. These schematics are accurate, but over-simplified to demonstrate the levels of the signal-processing workflow where feedback loops are likely to occur.

 

Figure 1: Crude Cognitive Workflow of Auditory Signal Processing

 

 

Figure 2: Flat Schematic Workflow of Auditory Processing Pathway

 

 

 

In Figure 2, sensory signal flow originates from raw sense data hitting the ear (at left) and continues upward through the brain towards our waking image of consciousness, which emerges at the far right in working memory in the pre-frontal cortex (PFC) and rhinal-hippocampal long-term-memory (LTM) systems in the medial temporal lobe. Along this signal processing pathway there are many circuits which use feedback to control upstream signal flow, illustrated by the double-arrow connections. These feedback circuits allow both feed-forward and feed-back excitation and filtering of incoming signal processing. The cortical areas with the highest densities of 5-HT2A receptors (such as the layer V pyramid cells in the audio and pre-frontal cortices) are shown in red, and the feedback circuits in red are those most likely to be excited in the presence of a 5-HT2A agonist. These red feedback circuits have been numbered by type, with the description of each type of circuit detailed below:

 

  1. Thalamocortical feedback circuit: This circuit connects the sensory thalamus to the sensory cortex. An increase in recursion intensity in this circuit can lead to amplification of signal strength, signal distortion, and temporal delay of signal data.
  2. Intra-cortical feedback circuit: Processing in the sensory cortex is done in layers of interconnected neurons which are responsible for assembling fragmentary snippets of sensory data into holistic representations of reality. An increase in recursion intensity in these circuits can lead to  increased detail resolution; hyper-articulation of detail; signal noise and distortion; phantasmagoria; and hallucinations.
  3. Inter-cortical feedback circuit: Signal from divergent sensory processing pathways converges on the pre-frontal cortex to create the holistic, multi-modal sensory awareness we perceive as waking consciousness. Constant feedback between the PFC and the sensory cortices of the brain is essential to maintaining fidelity of signal and synchrony of multi-modal sensory convergence. An increase in recursion intensity in this circuit can lead to extreme temporal and perceptual distortions, including sensory flanging, phasing, and echoes; recursive thought loops and obsessive ideation; frame delay; moments replayed over and over in the head; disappearance of time; and loss of multi-modal sensory cohesion.
  4. Amygdalo-cortical feedback circuit: The amygdala performs signal processing in networked parallel to the sensory and pre-frontal cortices, monitoring sensory signal for potentially dangerous stimulus. The amygdala regulates the body’s instinctive fear and panic response, and is in constant feedback with all layers of sensory processing to ensure robust signal fidelity and rational override in the instance of false panic alarms. An increase in recursion intensity in this circuit can lead to anxiety, paranoia, and panic.
  5. Rhino-Cortical feedback circuit: The rhinal cortex in the medial temporal lobe is sometimes referred to as the transitional memory cortex, where information from various sensory processing areas of the brain converge for multi-modal memory compression in the hippocampus. These circuits are essential for accurate long-term memory storage and recall. An increase in recursion intensity in this circuit can lead to profound memory imprinting, spontaneous memory recall; memory distortion; false memories; temporary disruption of LTM storage (missing time, or blank spots) as well as. Spontaneous activity in the medial-temporal is also known to cause experiences which are mystical in nature, and patients with temporal-lobe epilepsy often hear voices and have messianic inclinations, indicating that recursion intensity in this circuit could potentially lead to a variety of mystical states. 

 

Although this is admittedly a crude model of sensory signal processing in the brain, it does demonstrate the basic tenets of Signal Theory and provides a working model in which accurate predictions of sensory processing and subjective experience can be made in terms of applied dosage and intensity of induced signal recursion.

Signal Theory and Cranial Blood Flow

While Signal Theory defines a method of action for amplifying and distorting the normal functioning of the human brain via the use of chemically mediated neural gates and feedback loops, it does not totally explain why the same psychedelic drug may target a different aspect of the mind or personality at different times in different trips (or sometimes within the same trip). In other words, since psychedelics are pharmacologically active in many parts of the brain, why would one LSD trip be more visual when another may be more emotional? In order to explain this targeted localization of psychedelic effect, I have come up with a model of cranial blood flow in which the drug follows the signal. In short, if a specific set of neural circuits becomes activated, then cranial blood must flow to that area to feed the active circuits oxygen and glucose. If the psychedelic molecule also moves through the bloodstream, the psychedelic molecule will follow in the blood to the most active brain areas, and become pharmacologically active wherever the brain is most active itself. Thus, psychedelic molecules can act as non-specific amplifiers of any aspect of the user’s psyche, an effect which is wholly targeted by the flow of signal, blood, and intent through the individual subject’s mind.

Signal Theory Predictions of Experiential Psychedelic Phenomena

The Signal Theory model of psychedelic action has been reverse-engineered from observing and cataloging the subjective results of psychedelic action, then attempting to find a theoretical cognitive model that could accurately predict the extreme range of experiential results. With that in mind, it would seem that I am simply re-stating what is already known about psychedelics when I say that Signal Theory makes “predictions” about the phenomenological results of psychedelics. Nevertheless, it is helpful to follow the theory back to action in order see if the predictions indicated by Signal Theory indeed make sense. 

 

Low Dose: Signal Theory predicts that at a low dose of psychedelics, moderate signal recursion in the layers of the sensory cortices would cause sensory signal to become sharper; the resolution of detail would begin to stand out; and thought in the rational forebrain would arise faster, sharper, and with heightened intensity.

 

Moderate Dose: Signal Theory would predict an overall increase in signal intensity in the thalamocortical, amygdalo-cortical, and inter-cortical feedback circuits, causing brief sensory echoes as well as hyper-articulation of all sensory detail. At this point perceptual distortions become more acute, thinking becomes more obsessive, anxiety and paranoia becomes more acute.

 

High Dose: Signal Theory would predict increased intensity in inter-cortical and rhino-cortical feedback circuits, creating sensory echoes which emerge and swirl inward on themselves with diminishing intensity. At this point fully articulated hallucinatory constructs begin to form with very limited temporal cohesion; thinking becomes erratic; there is an increase in spontaneous memory recall; there is an increase in temporal distortions; and multi-modal cohesion of external stimulus begins to unravel.

 

Extremely High Dose: Signal Theory would predict standing recursion waves throughout all sensory processing areas of the brain, allowing for the formation of fully articulated hallucinatory constructs with high temporal cohesion. At this point, the rational forebrain is overcome by information pouring in through self-sustaining sensory feedback loops, and thought emerges holographically into consciousness as fully-articulated multi-modal synesthesia.

Experiential Validation of Signal Theory

While the biomechanical fundamentals of Signal Theory are still being explored, the basic model itself can provide a great deal of insight into what the subjective consciousness may be capable of experiencing under the influence of psychedelics. Using the basic dynamics of Signal Theory, the subject can attempt to objectively parse what is happening inside the head during the psychedelic trip, and even attempt to apply the theory to targeted amplification or expansion of specific functions of the mind. If a basic understanding of Signal Theory helps the subject maintain a conscious level of control and mastery over the psychedelic state, then the model is experientially valid, and could have a broad application in both personal, professional, and therapeutic use of psychedelics. While experiential validation is a long way from empirical proof of the validity of Signal Theory, it does at least provide a workable model for thinking of the psychedelic state without having to invoke either psychosis or spirits to explain the unique experiential results.

Empirical Validation of Signal Theory

Signal Theory may be tested and validated in a number of objectively measurable ways. The first step would be to locate the primary areas of action where sensory signal feedback and iterative neural processes are most likely to occur. As the diagrammatic model of Signal Theory shows, the primary areas to watch for signal recursion and feedback excitation would be between the sensory thalamus, the layer V pyramid cells of the sensory processing cortices, and the projections which lead from the sensory cortices to the multi-modal convergence points in the pre-frontal lobe and transitional memory cortex (and back). In actuality, there may be multiple layers of signal recursion and feedback amplification occurring between many levels of the brain at all once, but charting the densities of 5HT2A receptors throughout the cortex and following their afferent feedback connections seems like the logical starting place to look for the kind of iterative feedback circuits Signal Theory predicts.

 

Secondly, once an area (or areas) of the brain are identified as potential pathways for sensory signal recursion, scanning and monitoring methods must be developed to measure neural activity and blood flow to these areas both in baseline settings and during targeted psychedelic states. Signal Theory predicts that specific areas of the brain will increase in neural firing rate to correspond to the intensity of specific psychedelic phenomena, and that the synchrony of neural firing will remain constant between all active brain areas where standing feedback recursion is occurring. Though the impact of signal recursion may theoretically be measured on one or more cranial scanning devices, getting precise rates of recursion from animal subjects would most likely require the implantation of electrodes into various areas of the brain to monitor inter-cortical signal recursion and intensity in a more precise manner.  

Mathematical Exploration of Signal Theory

Though I am presently not familiar with any mathematical models to describe the intensity of signal recursion within neural feedback circuitry, I must also confess that my overall knowledge of computational neurobiology is limited, and that I have no capacity to adequately test any mathematical models I may come up with or come across on my own. But moving towards a mathematical model of neural signal recursion, a few primary factors would need to be adequately articulated as a starting point. To simplify this process, I present a crude two-neuron schematic of an iterative feedback circuit for deconstruction below:

 

Figure 3. Simple Iterative Neural Feedback Circuit

 

In Figure 3., two neurons create a simple iterative feedback circuit. The cell-body on the left is neuron 1 (n1), the cell-body on the right is neuron 2 (n2). Feed-forward signal from (n1) passes to (n2); feedback signal passes from (n2) to (n1). The primary factors that can be measured or deduced from this model are as follows: 

 

Recursion Intensity Primary Factors

 

(f1) firing rate : feed-forward firing rate (frequency, in hz) of signaling neuron.

 

(t1) firing time: time of (n1) action potential (start at 0 for calculating circuit synchrony).

 

(f2) feedback rate: firing rate (frequency, in hz) of post-synaptic feedback neuron.

 

(t2) firing time: time of (n2) action potential (0+ milliseconds for calculating circuit synchrony).

 

(e1) and (e2) excitation: ratio which measures relative excitation of neural membrane at post-synaptic receptor site. 1e would be normal resting state (neutral), 1.2e would be a slightly excited state, .8e would be a slightly inhibited state. This factor expresses primary pharmacological interaction at the receptor.

 

(m1) and (m2) modifier: ratio which measures relative asynchronous transfer rate of signaling transmitters leaked from the pre-synaptic axon terminal. 1m would be normal 1:1 synaptic firing to signal transmission. 1.2m would indicate slight asynchronous leakage of transmitter at pre-synaptic terminal. This factor expresses secondary pharmacological interaction at pre-synaptic axon terminal.

 

(p) feedback parity: ratio of feed-forward output frequency to feed-back input frequency. Perfect parity = 1; slightly diminished parity = .8; slightly amplified parity = 1.2. Parity would be a good first indicator in calculating feedback recursion intensity and iterative signal decay.

 

(ips) iterations per second : the number of iterations signal makes through the neural circuit per second, expressed in hertz. The more hz, the greater the rate of recursion.

 

(rr) rate of recursion: time in milliseconds to complete a signal feedback circuit; another means of expressing iterations per second.

 

(s) synchrony : delay, in milliseconds, between firing of successive neurons in a circuit. Perfect synchrony would be 0, in which all neurons in the circuit were firing at precisely the same moment to create a standing wave of signal recursion.

 

(rs) resonance : a factor which modifies synchrony. Resonance can be calculated using (f) and (t) to mark sympathetic frequency amplifiers between neurons in synchronized circuits. 

 

(d) decay: time, in milliseconds, in which recursive feedback signal decays in a given iterative circuit. Can be observed and averaged over time, or may be generally predicted using feedback parity (p), excitation (e), synchrony (s), and asynchronous modifiers (m) as indicators of potential signal decay or amplification over successive iterations.

 

Signal Decay and Recursion Intensity

Normal sensory signal decay is fairly quick in terms of milliseconds; images fade from our mind very rapidly when we close our eyes. Signal Theory predicts that an increase in recursion intensity and circuit synchrony would also increase the length of time it takes that signal to decay over time (thus trails, feedback loops, after-images, etc). In other words, signal decay should increase in direct proportion to parity, synchrony and rate of recursion, and be modified incrementally by the pharmacological factors (m) and (e). And though it would be premature to try and model those variables without any experimental data to plug in and test, it seems clear that parity, circuit excitation, synchrony, and signal decay would be the major variables to look at when attempting to measure the relative recursion intensity of a particular neural circuit over time.

 

Notes on Calculating Synchrony, Resonance, and Standing Recursion

Synchrony and resonance may be the most difficult factors to calculate within complex neural circuits in living subjects. In a simple two-neuron circuit, s = (t2-t1), and if both neurons are firing rhythmically at the same moment, synchrony becomes a perfect 0. If synchrony becomes a perfect 0 and feedback parity reaches a perfect 1, then a perfect standing recursion wave has been achieved. It is unknown if human neural circuitry can actually achieve a perfect standing recursion wave, but we know it can certainly come very close. If synchrony is near 0 and parity is .8, you have what would be measurable feedback recursion with predictable signal decay. If synchrony is near 0 and parity is at 1.2, you have an amplified recursion wave that grows in intensity with each iteration. However, there are many ways that neurons in a complex circuit can fire in resonant synchrony, meaning a standing wave of recursive signal is generated not from perfect synchrony, but from neurons firing in sympathetic rhythmic patterns that actually drive  signal strength. Also, we are talking about modeling the action of thousands of neurons all firing within a hundredth-of-a-second of each other, so closing the gap on synchrony and resonance is literally a kind of temporal hairsplitting. In truth, identifying and modeling all the different rhythms, syncopations, and cohesive resonant and dissonant patterns that arise in complex standing neural iterative processes may become a scientific sub-field unto itself. The technology for scanning and measuring these distinct neural firing patterns is among us today, but the basic questions in this field still remain unanswered. Perhaps computer models can be generated to model the kind of output intensity Signal Theory predicts, but until a more accurate method for calculating synchrony in complex neural circuits is devised, we can only guess at how much of a factor synchrony and resonance are in generating standing signal recursion in neural structures.

 

Developing a Mathematical Model

What I offer here is only a brief mathematical discussion, which is the first steps towards exploring Signal Theory and developing a model that can be measured and predicted not only in experiential results, but in tests on electrical current running through slices of neural tissue in vitro. However, if one would like to examine examples of mathematical models for calculating synchrony and spike timing in complex neural networks, please follow the relevant references presented at the end of this overview.

Signal Theory Questions

 

The Role of Iterative Feedback Circuits in the Production of Consciousness

What are the functions of iterative neural feedback networks in normal sensory signal processing? Signal Theory would predict that they are instrumental in resolving fine detail from ambiguous data sets, and refining crude sensation into articulated thought. In other words, Signal Theory predicts that iterative neural analysis enhances signal resolution and articulation of detail in the same way that multiple optical scans of a photograph can produce far greater detail resolution than a single scan. In other words, the detail is in the recursion.

 

Dopamine as Promoter of Neural Firing Synchrony in Iterative Circuits

What is dopamine’s role in the formation standing recursion waves? Given dopamine’s role in ADD, psychosis, and mediating fine motor control, evidence suggests that dopamine may be essential to modulating neural firing synchrony in the formation of standing resonant patterns. Given this assumption, Signal Theory predicts that dopamine is also essential in modulating and fine-tuning resonant synchrony in standing waves of sensory feedback recursion in the psychedelic state. Too little dopamine would lead to the inability to focus the mind to form standing recursion waves; too much dopamine would lead to the spontaneous eruption of standing recursion waves with high temporal cohesion. Since fine motor control, concentration, and multi-modal sensory convergence all rely on recursive feedback circuits to fine-tune neural synchrony,    the basic tenets of the dopamine model of psychosis, Parkinson’s, and ADD dovetails precisely with the basic tenets of Signal Theory.

 

Optimal Levels of Recursion Intensity for Expanded Consciousness

Signal Theory predicts that there are optimized levels of recursion and circuit synchrony for both normal consciousness as well as various states of expanded psychedelic consciousness. Can these optimal recursion rates be objectively measured, mapped, and predicted? Can these states be spontaneously stimulated via focused biofeedback or application of targeted transcortical stimulation? This is the big question Signal Theory poses. Accurately identifying and predicting the optimal recursion factors that lead to specific expanded states of consciousness is the ultimate goal of Signal Theory, a goal that will not be achieved until further in-vitro an in-vivo experimental testing can be done.

 

Signal Theory, Neural Plasticity, and PHPD

Can recursive feedback networks activated over time form lasting recursion pathways which persist after the psychedelic molecule has been metabolized? Tenets of neural plasticity would indicate that the longer and more frequently recursion intensity is applied to any given circuit, the more likely it is that the circuit will fall into standing signal recursion under normal conditions. The role of iterative processing and circuit synchrony in neural plasticity and the formation and strengthening of long-term synaptic bonds should be explored in more detail to fully answer this question.

 

Consciousness as a Standing Field Formation

While Signal Theory focuses primarily on the movement of electrical current through the neocortex, it should also be noted that any electrical field passing through a channel also creates an electromagnetic (EM) field, and the same is true for the electrical pulses in the brain. If Signal Theory is extrapolated to its fullest potential, one would expect to see observable results in the EM field which represents the process of consciousness moving from one optimized state to another. If this is so, then distinct states of consciousness can also be modeled in terms of the field properties which accompany signal interaction through the neural circuitry.

 

Isomorphs of Iterative Feedback Processes and Psychedelic Action

  • Mediation and Biofeedback
  • Video Feedback
  • Audio Looping and Filtering Popularized in Acid House and Trance Music
  • Tides and Weather Systems
  • Fractals
  • Genetic Expression
  • Consciousness

The Future of Signal Theory

If the base principles of Signal Theory are true, then we will have a new model for describing targeted states of consciousness in terms empirical signal properties. Everything I am asserting here can be tested both experientially and objectively via cranial monitoring and scanning technology, and I would hope that psychedelic and consciousness research in the next few decades could either produce evidence to corroborate this theory or at least find one which more accurately demonstrates how simple psychedelic action in the cortex can generate the wide varieties of unique mind states and perceptual experiences associated with classic psychedelic phenomena.

 

 

 

The ramifications of Signal Theory are currently being explored in the text Psychedelic Information Theory, by James Kent (jamesk@tripzine.com). For more information, please visit http://tripzine.com/pit

 

  

 

References

·          Best, Ben; An Overview of Neural Networks; [link] Good overview of how to model complex neural networks in a computational environment.

 

·          BilZ0r. "Neuropharmacology of Hallucinogens : a brief introduction". Erowid.org, v1 Feb 2004. [link] Good overview of psychedelic 5-HT2A receptor interaction.

 

·          Hobson, J. Allan; The Dream Drugstore: Chemically Altered State of Consciousness.  MIT Press, Cambridge, MA. 2001. [Amazon.com Link]

 

·          J. Hunter, Milton, Thomas, Cowan; Resonance Effect for Neural Spike Time Reliability; The Journal of Neurophysiology Vol. 80 No. 3 September 1998, pp. 1427-1438. [abstract] University of Chicago paper detailing methods for measuring the effects of resonance on neural spike timing.

 

·          LeDoux, Joseph; Synaptic Self: How Our Brains Become Who We Are. Viking Press, New York, NY. 2002. [Amazon.com Link]

 

·          LeDoux, Joseph; The Emotional Brain: The Mysterious Underpinnings of Emotional Life. Touchstone Press, New York, NY. 1996. [Amzon.com Link]

 

·          Longtin , André; Autonomous stochastic resonance in bursting neurons; Phys. Rev. E 55, 868-876 (1997).  [abstract] This article is primarily about stochastic resonance, but contains formulas for measuring neural spike phase locking in response to resonant sub-threshold oscillations. (need to buy full article to read).

 

·          Lumer, Edelman, Tononi; Neural dynamics in a model of the thalamocortical system. II. The role of neural synchrony tested through perturbations of spike timing; Cerebral Cortex, Vol 7, 228-236, Copyright © 1997 by Oxford University Press. [pdf] Interesting article detailing methods for modeling synchronous oscillations and feedback loops within thalamocortical and corticocordical layers of sensory processing.

 

·          Mato, G.; Stochastic resonance using noise generated by a neural network; Physical Review E; Vol. 59 No. 3 March 1999; pp. 3339-3343. [pdf] Mato’s article describes a way to mathematically model synchrony and resonance in  complex neural networks.

 

 

Revision History

 

1.04 – 1/13/2006

·          Rewrote opening to remove entheogenic discourse altogether. Will move to a different essay.

·          Added section, “Foundations of Consciousness” to give crash overview of cognitive models

·          Added brain illustration to “Schematic Model” section.

·          Update References.

 

1.02 – 11/12/2005

·          Added References

 

1.01 - 11/10/2005

·          Per suggestion by Jon Hannah, rewrote opening and closing discourse to remove loaded terminology.

·          Added more detail to intro text to frame theory in both brain-based and spirit-based models.

·          Added Resonance (rs) as its own primary factor in calculating synchrony of firing patterns and recursion intensity within a given circuit.

·          Added dopamine hypothesis of neural synchrony to questions.

 

1.00 - 11/07/2005

·          Public release of Signal Theory Overview.