behaviorism
Major conscious and unconcoscious processes in the brain
May 22nd
Today I plan to touch upon the topic of consciousness (from which many bloggers shy) and more broadly try to delineate what I believe are the important different conscious and unconscious processes in the brain. I will be heavily using my evolutionary stages model for this.
To clarify myself at the very start , I do not believe in a purely reactive nature of organisms; I believe that apart from reacting to stimuli/world; they also act , on their own, and are thus agents. To elaborate, I believe that neuronal groups and circuits may fire on their own and thus lead to behavior/ action. I do not claim that this firing is under voluntary/ volitional control- it may be random- the important point to note is that there is spontaneous motion.
- Sensory system: So to start with I propose that the first function/process the brain needs to develop is to sense its surroundings. This is to avoid predators/ harm in general. this sensory function of brain/sense organs may be unconscious and need not become conscious- as long as an animal can sense danger, even though it may not be aware of the danger, it can take appropriate action – a simple ‘action’ being changing its color to merge with background.
- Motor system:The second function/ process that the brain needs to develop is to have a system that enables motion/movement. This is primarily to explore its environment for food /nutrients. Preys are not going to walk in to your mouth; you have to move around and locate them. Again , this movement need not be volitional/conscious – as long as the animal moves randomly and sporadically to explore new environments, it can ‘see’ new things and eat a few. Again this ‘seeing’ may be as simple as sensing the chemical gradient in a new environmental.
- Learning system: The third function/process that the brain needs to develop is to have a system that enables learning. It is not enough to sense the environmental here-and-now. One needs to learn the contingencies in the world and remember that both in space and time. I am inclined to believe that this is primarily pavlovaion conditioning and associative learning, though I don’t rule out operant learning. Again this learning need not be conscious- one need not explicitly refer to a memory to utilize it- unconscious learning and memory of events can suffice and can drive interactions. I also believe that need for this function is primarily driven by the fact that one interacts with similar environments/con specifics/ predators/ preys and it helps to remember which environmental conditions/operant actions lead to what outcomes. This learning could be as simple as stimuli A predict stimuli B and/or that action C predicts reward D .
- Affective/ Action tendencies system .The fourth function I propose that the brain needs to develop is a system to control its motor system/ behavior by making it more in sync with its internal state. This I propose is done by a group of neurons monitoring the activity of other neurons/visceral organs and thus becoming aware (in a non-conscious sense)of the global state of the organism and of the probability that a particular neuronal group will fire in future and by thus becoming aware of the global state of the organism , by their outputs they may be able to enable one group to fire while inhibiting other groups from firing. To clarify by way of example, some neuronal groups may be responsible for movement. Another neuronal group may be receiving inputs from these as well as say input from gut that says that no movement has happened for a time and that the organism has also not eaten for a time and thus is in a ‘hungry’ state. This may prompt these neurons to fire in such a way that they send excitatory outputs to the movement related neurons and thus biasing them towards firing and thus increasing the probability that a motion will take place and perhaps the organism by indulging in exploratory behavior may be able to satisfy hunger. Of course they will inhibit other neuronal groups from firing and will themselves stop firing when appropriate motion takes place/ a prey is eaten. Again nothing of this has to be conscious- the state of the organism (like hunger) can be discerned unconsciously and the action-tendencies biasing foraging behavior also activated unconsciously- as long as the organism prefers certain behaviors over others depending on its internal state , everything works perfectly. I propose that (unconscious) affective (emotional) state and systems have emerged to fulfill exactly this need of being able to differentially activate different action-tendencies suited to the needs of the organism. I also stick my neck out and claim that the activation of a particular emotion/affective system biases our sensing also. If the organism is hungry, the food tastes (is unconsciously more vivid) better and vice versa. thus affects not only are action-tendencies , but are also, to an extent, sensing-tendencies.
- Decisional/evaluative system: the last function (for now- remember I adhere to eight stage theories- and we have just seen five brain processes in increasing hierarchy) that the brain needs to have is a system to decide / evaluate. Learning lets us predict our world as well as the consequences of our actions. Affective systems provide us some control over our behavior and over our environment- but are automatically activated by the state we are in. Something needs to make these come together such that the competition between actions triggered due to the state we are in (affective action-tendencies) and the actions that may be beneficial given the learning associated with the current stimuli/ state of the world are resolved satisfactorily. One has to balance the action and reaction ratio and the subjective versus objective interpretation/ sensation of environment. The decisional/evaluative system , I propose, does this by associating values with different external event outcomes and different internal state outcomes and by resolving the trade off between the two. This again need not be conscious- given a stimuli predicting a predator in vicinity, and the internal state of the organism as hungry, the organism may have attached more value to ‘avoid being eaten’ than to ‘finding prey’ and thus may not move, but camouflage. On the other hand , if the organisms value system is such that it prefers a hero’s death on battlefield , rather than starvation, it may move (in search of food) – again this could exist in the simplest of unicellular organisms.
Of course all of these brain processes could (and in humans indeed do) have their conscious counterparts like Perception, Volition,episodic Memory, Feelings and Deliberation/thought. That is a different story for a new blog post!
And of course one can also conceive the above in pure reductionist form as a chain below:
sense–>recognize & learn–>evaluate options and decide–>emote and activate action tendencies->execute and move.
and then one can also say that movement leads to new sensation and the above is not a chain , but a part of cycle; all that is valid, but I would sincerely request my readers to consider the possibility of spontaneous and self-driven behavior as separate from reactive motor behavior.
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A gene implicated in operant learning finally discovered
Oct 16th
Till now, most of the research on learning at the molecular level or LTP/TLD has focused on classical conditioning paradigms. To my knowledge for the first time someone has started looking at whether , on the molecular level, classical conditioning , which works by associations between external stimuli, is differently encoded and implemented from operant learning , which depends on learning the reward contingencies of one’s spontaneously generated behavior.
Bjorn Brembs and colleagues have shown that the normal learning pathway implicated in classical conditioning, which involves Rugbata gene in fruit fly and works on adenylyl cyclase (AC) , is not involved in pure operant learning; rather pure operant learning is mediated by Protein Kinase C (PKC) pathways. This is not only a path breaking discovery , as it cleary shows the double dissociation showing genetically mutant flies, it is also a marvelous example fo how a beautiful experimental setup was convened to separate and remove the classical conditioning effects from normal operant learning and generate a pure operant learning procedure. You can read more about the procedure on Bjorn Brembs site and he also maintains a very good blog, so check that out too.
Here is the abstract of the article and the full article is available at the Bjorn Brembs site.
Learning about relationships between stimuli (i.e., classical conditioning ) and learning about consequences of one’s own behavior (i.e., operant conditioning ) constitute the major part of our predictive understanding of the world. Since these forms of learning were recognized as two separate types 80 years ago , a recurrent concern has been the
issue of whether one biological process can account for both of them . Today, we know the anatomical structures required for successful learning in several different paradigms, e.g., operant and classical processes can be localized to different brain regions in rodents [9] and an identified neuron in Aplysia shows opposite biophysical changes after operant and classical training, respectively. We also know to some detail the molecular mechanisms underlying some forms of learning and memory consolidation. However, it is not known whether operant and classical learning can be distinguished at the molecular level. Therefore, we investigated whether genetic manipulations could differentiate between operant and classical learning in dorsophila. We found a double dissociation of protein kinase C and adenylyl cyclase on operant and classical learning. Moreover, the two learning systems interacted hierarchically such that classical predictors were learned preferentially over operant predictors.
Do take a look at the paper and the experimental setup and lets hope that more focus on operant learning would be the focus from now on and would lead to a paradigmatic shift in molecular neuroscience with operant conditioning results more applicable to humans than classical conditioning results, in my opinion.
B BREMBS, W PLENDL (2008). Double Dissociation of PKC and AC Manipulations on Operant and Classical Learning in Drosophila Current Biology, 18 (15), 1168-1171 DOI: 10.1016/j.cub.2008.07.041
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Cloninger’s Temaparements and character traits: room for a behaviorist view?
Sep 19th
Today I wish to discuss C. Robert Cloninger’s theory of temperaments and character traits. It is a psycho biological theory based on genetic and neural substrates and mechanisms and in it he proposes for the existence of four temperament traits and three character traits; thus talking about seven personality traits. First the abstract to give you some idea:
In this study, we describe a psychobiological model of the structure and development of personality that accounts for dimensions of both temperament and character. Previous research has confirmed four dimensions of temperament: novelty seeking, harm avoidance, reward dependence, and persistence, which are independently heritable, manifest early in life, and involve preconceptual biases in perceptual memory and habit formation. For the first time, we describe three dimensions of character that mature in adulthood and influence personal and social effectiveness by insight learning about self-concepts. Self-concepts vary according to the extent to which a person identifies the self as (1) an autonomous individual, (2) an integral part of humanity, and (3) an integral part of the universe as a whole. Each aspect of self-concept corresponds to one of three character dimensions called self-directedness, cooperativeness, and self-transcendence, respectively. We also describe the conceptual background and development of a self-report measure of these dimensions, the Temperament and Character Inventory. Data on 300 individuals from the general population support the reliability and structure of these seven personality dimensions. We discuss the implications for studies of information processing, inheritance, development, diagnosis, and treatment
This article provides an excellent in-depth look at the Temperament and character Inventory (TCI) developed by Cloninger and it gives detailed description of all the traits and their sub-scales or facets.
I’ll list them briefly below (in order )(along with their sub scales/ facets)
I) Novelty seeking (NS)
- Exploratory excitability (NS1)
- Impulsiveness (NS2)
- Extravagance (NS3)
- Disorderliness (NS4)
II) Harm avoidance (HA)
- Anticipatory worry (HA1)
- Fear of uncertainty (HA2)
- Shyness (HA3)
- Fatigability (HA4)
III) Reward dependence (RD)
- Sentimentality (RD1)
- Openness to warm communication (RD2)
- Attachment (RD3
- Dependence (RD4)
IV) Persistence (PS)
- Eagerness of effort (PS1)
- Work hardened (PS2)
- Ambitious (PS3)
- Perfectionist (PS4)
V) Self-directedness (SD)
- Responsibility (SD1)
- Purposeful (SD2)
- Resourcefulness (SD3)
- Self-acceptance (SD4)
- Enlightened second nature (SD5)
VI) Cooperativeness (C)
- Social acceptance (C1)
- Empathy (C2)
- Helpfulness (C3)
- Compassion (C4)
- Pure-hearted conscience (C5)
VII) Self-transcendence (ST)
- Self-forgetful (ST1)
- Transpersonal identification (ST2)
- Spiritual acceptance (ST3)
To me this lacks one more trait and I’m sure Cloninger will identify and add one more in the future (he added the three character traits relatively late).
Now for the meat of the post. My thesis is that these are similar to the Big Eight temperaments that I have discussed in my earlier post and follow the same eight fold developmental/evolutionary pattern. Further , I would claim that each facet of a trait follows the same structure. Most traits have 4 or 5 facets and these are typically related to 5 major ways of reacting/ relating to world around us. It is also my thesis that juts as cloninger had tied the initial three traits to behavioral inhibition, behavioral approach and behavioral maintenance and to the three neurotransmitter systems of serotonin, dopamine and norepinephrine respectively; the same line of argument can be extended to other facets and new biogenic amine CNS neurotransmitters pathways correlated with each trait.
Harm Avoidance:
Individuals high in HA tend to be cautious, careful,fearful, tense, apprehensive, nervous, timid, doubtful,discouraged, insecure, passive, negativistic, or pessimistic even in situations that do not normally worry other people. These individuals tend to be inhibited and shy in most social situations. Their energy level tends to be low and they feel chronically tired or easily fatigued. As a consequence they need more reassurance and encouragement than most people and are usually sensitive to criticism and punishment. The advantages of of high Harm Avoidance are the greater care and caution in anticipating possible danger, which leads to careful planning when danger is possible. The disadvantages occur when danger is unlikely but still anticipated, such pessimism or inhibition leads to unnecessary worry.
In contrast, individuals with low scores on this temperament dimension tend to be carefree, relaxed, daring, courageous, composed, and optimistic even in situations that worry most people. These individuals are described as outgoing, bold, and confident in most social situations. Their energy level tends to be high, and they impress others as dynamic, lively, and vigorous persons. The advantages of low Harm Avoidance are confidence in the face of danger and uncertainty,leading to optimistic and energetic efforts with little or no distress. The disadvantages are related to unresponsiveness to danger, which can lead to reckless optimism.
Form the above it is clear that this is related to Neurotcisim and. This would also be related to anxiety witnessed in clinical situations and requiring treatment. It is instructive to note that Cloninger proposes Serotonin CNS system as a substrate for this trait and that many anti-anxiety drugs actually target serotonin receptors (SSRIS are the best anti anxiety drugs).also as per the model this is involved in behavior inhibition. Let me elaborate that and propose that what is meant by behavior inhibition is learning to avoid the predator. In operant conditioning paradigms this would be learning due to Positive punishment. Learning to inhibit a pre-potent behavior because of punishments.
Novelty Seeking:
Individuals high in Novelty Seeking tend to be quick-tempered, excitable, exploratory, curious, enthusiastic, ardent, easily bored, impulsive, and disorderly The advantages of high Novelty Seeking are enthusiastic and quick engagement with whatever is new and unfamiliar, which leads to exploration of potential rewards. The disadvantages are related to excessive anger and quick disengagement whenever their wishes are frustrated, which leads to inconsistencies in relationships and instability in efforts.
In contrast, individuals low in Novelty Seeking are described as slow tempered, indifferent, uninquisitive, unenthusiastic, umemotional, reflective, thrifty, reserved, tolerant of monotony, systematic, and orderly.
These are classical Impulsiveness related symptoms and can be safely associated with the dopamine system. this trait then is related to conscientiousness and is driven by rewards and reward-related behavior learning. Excess is this trait may result in psychosis and many anti-psychotic drugs act on this dopamine system. This is the traditional behavioral activation system. In operant conditioning terms we can call this learning under positive reinforcement. New behaviors are learned or strength of old behaviors is modified (increased) in the presence of primary reinforces like food, sex,(even money) etc).
Reward dependence:
Individuals who score high in Reward Dependence tend to be tender-hearted, loving and warm, sensitive, dedicated, dependent, and sociable. They seek social contact and are open to communication with other people. Typically, they find people they like everywhere they go. A major advantage of high Reward Dependence is the sensitivity to social cues, which facilitates warm social relations and understanding of others’ feelings. A major disadvantage of high Reward Dependence involves the ease with which other people can influence the dependent person’s views and feelings, possibly leading to loss of objectivity.
Individuals low on the Reward Dependence are often described as practical, tough minded, cold, and socially insensitive. They are content to be alone and rarely initiate open communication with others. They prefer to keep their distance and typically have difficulties in finding something in common with other people. An advantage of low Reward Dependence is that independence from sentimental considerations.
From the above it is clear that this is related to trait Extraversion or sociability and influences how adept, and prone, one is at forming alliances and friends. This has been hypothesized to be related to norepinephrine system and related to behavioral maintenance. In operant conditioning terms , I interpret it as maintaining a behavior despite no real (primary) reinforcement, but just because of secondary reinforcement (social approval, praise, status etc). This is not necessary maladaptive and secondary reinforcement are necessary; but too much dependence on that may lead to depression. Initial anti-depressants all worked on the norepinepherine system and the monoamine theory of depression is still around. I believe that depression is multi-factorial, but the social striving/approval/negotiation is a prime facet underlying the illness.
Persistence:
Individuals high in Persistence tend to be industrious, hard-working, persistent, and stable despite frustration and fatigue. They typically intensify their effort in response to anticipated reward. They are ready to volunteer when there is something to be done, and are eager to start work on any assigned duty. Persistent persons tend to perceive frustration and fatigue as a personal challenge. They do not give up easily and, in fact, tend to work extra hard when criticized or confronted with mistakes in their work. Highly persistent persons tend to be ambitious overachievers
who are willing to make major sacrifices to be a success. A highly persistent individual may tend to be a perfectionist and a workaholic who pushes him/herself far beyond what is necessary to get by.High Persistence is an adaptive behavioral strategy when rewards are intermittent but the contingencies remain stable. However, when the contingencies change rapidly, perseveration becomes maladaptive.
When reward contingencies are stable, individuals low in Persistence are viewed as indolent, inactive, unreliable, unstable and erratic on the basis of both self-reports and interviewer ratings. They rarely intensify their effort even in response to anticipated reward. These persons rarely volunteer for anything they do not have to do, and typically go slow in starting work, even if it is easy to do. They tend to give up easily when faced with frustration, criticism, obstacles, and fatigue. These persons are usually satisfied with their current accomplishments, rarely strive for bigger and better things, and are frequently described as underachievers who could probably accomplish for than they actually do, but do not push themselves harder than it is necessary to get by. Low scorers manifest a low level of perseverance and repetitive behaviors even in response to intermittent reward. Low Persistence is an adaptive strategy when reward contingencies change rapidly and may be maladaptive when rewards are infrequent but occur in the long run.
By some stretch of imagination one can relate this to being empathetic or agreeable. (volunteering etc) and thus to agreeableness. One way this could be related to parental investment is that those who do not care for their kids have children that give up easily and are frustrated easily; thus the same mechanism may lie both parental care behavior and persistent behavior in the kid. This behavior/trait I propose may be related to epinepherine CNS system. This is related to behavior persistence; in opernat conditioning terms this is behavior persistence despite no primary or even secondary reinforcement. Of course extinction will eventually happen in absence of reward, but factors like time/ no. of trials taken to archive extinction may be a factor here. Although, behavior is not reinforced at all still it is persisted with and maybe even different related variations tried to get the desired reward. Stimulants as a class of drug may be acting on this pathway, stimulating individuals to engage in behavior despite no reinforcement.
Self-directedness:
Highly self-directed persons are described as mature, strong, self-sufficient, responsible, reliable, goaloriented, constructive, and well-integrated individuals when they have the opportunity for personal leadership. They have good self-esteem and self-reliance. The most distinctive characteristics of self-directed individuals is that they are effective, able to adapt their behavior in accord with individualy chosen, voluntary goals. When a self-directed individual is required to follow the orders of others in authority, they may be viewed as rebellious trouble maker because they challenge the goals and values of those in authority.
In contrast, individuals who are low in Self-Directedness are described as immature, weak, fragile, blaming, destructive, ineffective, irresponsible, unreliable, and poorly integrated when they are not conforming to the direction of a mature leader. They are frequently described by clinicians as immature or having a personality disorder. They seem to be lacking an internal organizational principle, which renders them unable to define, set, and pursue meaningful goals. Instead, they experience numerous minor, short term, frequently mutually exclusive motives, none of which can develop to the point of long lasting personal significance and realization.
To me the above looks very much like the Rebelliousness/ conformity facet of Openness or intellect. The core idea being whether one has archived ego-integrity and good habits. I propose that histamine or melatonin may be the mono amine CNS system involved here, though phenuylethylamine(PEA) also seems a good target, so do tyrosine and other trace amines. Whatever be the neurotransmitter system involved, the operant conditioning phenomenon would be learning to engage in behavior despite +ve punishment. thus, the ability to go against the grain, convention, or social expectations and be true to oneself. This behavior can be called learning under -ve reinforcement i.e engaging in a behavior despite there being troubling things around, in the hope that they would be taken away on successful new behavior. I would also relate this to behavioral reportaire of the individual. People high on this trait would show greater behavioral variability during extinction trials and come up with novel and insightful problem solving behaviors.
That is it for now; I hope to back up these claims, and extend this to the rest of the 3 traits too in the near future. Some things I am toying with is either classical conditioning and avoidance learning on these higher levels; or behavior remembering (as opposed to learning) at these higher levels. Also other neurotransmitter systems like gluatamete, glycine, GABA and aspartate may be active at the higher levels. Also neuro peptides too are broadly classified in five groups so they too may have some role here. Keep guessing and do contribute to the theory if you can!!
The situational factors: compliance, personality and charachter
Mar 12th
I’ve recently come across a new blog the Situationist and have just read a three part article by the famed Phillip Zimbardo (who has conducted the Stanford prison experiments) titled Situational sources of evil.
In the part I, he discusses Stanley Milgram’s compliance experiments wherein under the authority of a professor, subjects were forced to apply outrageous electric shocks to the confederates. This experiment was a classical one in social psychology and showed how under the situations of authority, normal individuals can be made to do evil deeds in the laboratory. Milgram also did a number of variations of this experiment to find out what factors facilitated compliance and which factors enabled resistance to authority.
In Part II, Zimbardo discusses how these laboratory results can be extended to the real world phenomenons like the holocaust/ palestentain suicide bombers/ suicide cults and how most of the perpetrators are very common people (banality of evil).
In part III, Zimbardo outlines 10 learnings from Milgram’s experiments and I find then worth summarizing here -
Compliance can be increased by :
- A pseudo-legal contract that binds one to the act (which may not be construed as evil, a priori, but becomes evil while actual execution). also the public declarations of commitment force cognitive dissonance and make people stick to their ‘contracts’.
- Meaningful social roles like ‘teacher’ etc given to the perpetrators. They may find solace under the fact that their social role demands the unavoidable evil.
- Adherence to and sanctity of rules that were initially agreed upon. The rules may be subtly changed, but an emphasis on rule-based behavior would guarantee better compliance.
- Right framing of the issues concerned. Insteada of ‘hurting the participant’ framing it as ‘improving the learners learning ability’. Regular readers will note how committed I personally am to the framing effects.
- Diffusion/ abdication of responsibility: Either enabling the responsibility for the evil act to be taken upon by a senior authority; or by having many non-rebelling peers diffuse responsibility similar to the bystander effect.
- Small evil acts initially to reduce the resistance to recruitment. Once into the fold, one may increase the atrocities demanded from the perpetrator.
- Gradual increase in the degree of the evil act. Sudden and large jumps in evilness of the acts are bound to be resisted more.
- Morphing the Authority from just and reasonable initially to unjust and unreasonable in the later parts.
- High exit costs. You cannot beat the system, so better join it! The system can beat you up, so better remain in it!! also, allow dissent or freedom of voice, but suppress freedom of action!!!
- An overarching lie or framework or ‘cover story’ that gives a positive spin to the evil acts (in good terms)..’this experiment would help humanity’ , ‘Jews are bad/inferior and need to be eliminated’ etc.
Zimbardo is hopeful that by recognizing these factors that normally help in compliance to unjust and irrational authority, one can have the courage and acumen to resist such authority. The two traits he picks up are taking responsibility for one’s own acts and asserting one’s own authority.
The word character is normally frowned upon, and rarely used, in psychological discourses nowadays, but like Zimbardo I would like to highlight Eric Fromm’s works like Escape from Freedom in this regard, which posit that one can overcome the natural tendency to escape from one’s freedom and sense of responsibility and make a positive character or habitual behavioral tendencies that takes full responsibility for the self.
There is another related debate to which I would like to draw attention. Normally it is posited that we are composed of temperaments or personality traits ( the most famous being the Big Five or OCEAN traits) and much of our behavior is a result of our inherent tendencies.
A dissenting voice is of Walter Mischel , who claims that the concept of personality is vague and much of behavior is due to situational factors. I’m sure the truth is more towards a middle ground and like genes and environment, both personality and situations affect a behavioral outcome. Not stopping here I also see a role here for acquired propensities or habits or character that can overcome both the underlying propensities and the situational factors. Even after taking character into account our acts may not be totally non-deterministic or free or non-predictable, but could be free in a limited sense that we, ourselves, incorporated those habits/ character traits. We may still behave predictably, but that would not be due to our conditionings or situational factors; but because of an acquired character.
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Artificial Neural Networks: temporal summation, embedded ‘clocks’ and operant learning
Nov 17th
Artificial Neural Networks have historically focussed on modeling the brain as a collection of interconnected neurons. The individual neurons aggregate inputs and either produce an on/off output based on threshold values or produce a more complex output as a linear or sigmoid function of their inputs. The output of one neuron may go to several other neurons.
Not all inputs are equivalent and the inputs to the neuron are weighed according to a weight assigned to that input connection. This mimics the concept of synaptic strength. The weights can be positive (signifying an Excitatory Post-Synaptic Potential ) or negative (signifying an Inhibitory Post-Synaptic Potential).
Learning consists of the determination of correct weights that need to be assigned to solve the problem; i.e. to produce a desired output, given a particular input. This weight adjustment mimics the increase or decrease of synaptic strengths due to learning. Learning may also be established by manipulating the threshold required by the neuron for firing. This mimics the concept of long term potentiation (LTP).
The model generally consists of an input layer (mimicking sensory inputs to the neurons) , a hidden layer (mimicking the association functions of the neurons in the larger part of the brain) and an output layer ( mimicking the motor outputs for the neurons).
This model is a very nice replication of the actual neurons and neuronal computation, but it ignores some of the other relevant features of actual neurons:
1. Neuronal inputs are added together through the processes of both spatial and temporal summation. Spatial summation occurs when several weak signals are converted into a single large one, while temporal summation converts a rapid series of weak pulses from one source into one large signal. The concept of temporal summation is generally ignored. The summation consists exclusively of summation of signals from other neurons at the same time and does not normally include the concept of summation across a time interval.2. Not all neuronal activity is due to external ‘inputs’. Many brain regions show spontaneous activity, in the absence of any external stimulus. This is not generally factored in. We need a model of brain that takes into account the spontaneous ‘noise’ that is present in the brain, and how an external ‘signal’ is perceived in this ‘noise’. Moreover, we need a model for what purpose does this ‘noise’ serve?
3. This model mimics the classical conditioning paradigm, whereby learning is conceptualized in terms of input-output relationships or stimulus-response associations. It fails to throw any light on many operant phenomenon and activity, where behavior or response is spontaneously generated and learning consist in the increase\decrease \ extinction of the timing and frequency of that behavior as a result of a history of reinforcement. This type of learning accounts for the majority of behavior in which we are most interested- the behavior that is goal directed and the behavior that is time and context and state-dependent. The fact that a food stimulus, will not always result in a response ‘eat’, but is mediated by factors like the state (hunger) of the organism, time-of-day etc. is not explainable by the current models.
4. The concept of time, durations and how to tune the motor output as per strict timing requirements has largely been an unexplored area. While episodic learning and memory may be relatively easier to model in the existing ANNs, its my hunch that endowing them with a procedural memory would be well nigh impossible using existing models.
Over a series of posts, I would try to tackle these problems by enhancing the existing neural networks by incorporating some new features into it, that are consistent with our existing knowledge about actual neurons.
First, I propose to have a time-threshold in each neural unit. This time-threshold signifies the duration in which temporal summation is applicable and takes place. All inputs signals, that are received within this time duration, either from repeated firing of the same input neuron or from time-displaced firings of different input neurons, are added together as per the normal input weights and if at any time this reaches above the normal threshold-for-firing, then the neuron fires. This has combined both temporal and spatial summation concepts. With temporal summation, we have an extra parameter- the time duration for which the history of inputs needs to be taken into account.
All neurons will also have a very short-term memory, in the sense that they would be able to remember the strengths of the inputs signals that they have received in the near past , that is in the range of the typical time-thresholds that are set for them. This time-threshold can typically be in milliseconds.
Each time a neuron receives an input, it starts a timer. This timer would run for a very small duration encoded as the time-threshold for that neuron. Till the time this timer is running and has not expired, the input signal is available to the neuron for calculation of total input strength and for deciding whether to fire or not. As soon as the timer expires, the memory of the associated input is erased from the neurons memory and that particular input would no longer be able to affect any future firing of the neuron.
All timers as well as the memory of associated input signals are erased after each successful neural firing (every time the neuron generates an action potential). After each firing, the neuron starts from afresh and starts accumulating and aggregating the inputs it receives thereafter in the time-threshold window that is associated with it.
Of course there could be variations to this. Just like spatial aggregation/firing need not be an either/or decision based on a threshold; the temporal aggregation/ firing need not be an either-or decision: one could have liner or sigmoid functions of time that modulate the input signal strength based on the time that has elapsed. One particular candidate mechanism could be a radioactive decay function, that decreases the input signal strength by half after each half-life. Here, the half-life is equivalent to the concept of a time-threshold. While in the case of time-threshold, after a signal arrives, and once the time-threshold has elapsed, then the input signal is not available to the neuron at all, and while the time-threshold had not elapsed the signal was available in its entirety; in the case of radioactive deacy the inpiut signal is available till infinity in theory; but the strength of the signal would get diminisehd by half after each half-life period; thus making the effects of the input signal negligible after a few half-lives. Of course in the radioactive case too, once the neuron has fired, all memory of that input would be erased and any half-life decay computations stopped.
These are not very far-fetched speculations and modeling the neural networks this way can lead to many interesting results.
Second, I propose to have some ‘clocks’ or ‘periodic oscillators’ in the network, that would be generating spontaneous outputs after a pre-determined time and irrespective of any inputs. Even one such clock is sufficient for our discussions. Such a clock or oscillator system is not difficulty to envisage or conceive. We just need a non-random, deterministic delay in the transmission of signals from one neuron to the other. There do exist systems in the brain that delay the signals, but leaving aside such specialized systems, even a normal synaptic transmission along an axon between two neurons, would suffer from some deterministic delay based on the time it takes the signal to travel down the axon length and assuming that no changes in myelination takes place over time, so that the speed of transmission is constant.
In such a scenario, the time it takes for a signal to reach the other neuron, is held constant over time. (Note that this time may be different for different neuron pairs based on both the axon lengths involved and the associated myelination, but would be same for the same neuron pair over time). Suppose that both the neurons have very long, unmyelinated axons and that these axons are equal in length and provide inputs to each other. Further suppose that both the neurons do not have any other inputs , though each may send its output to many other neurons.
Thus, the sole input of the first neuron is the output of the second neuron and vice versa. Suppose that the thresholds of the two neurons are such that each would trigger, if it received a single input signal (from the peer neuron). As there would be a time lag between the firing of neuron one, and its reaching the second neuron, the second neuron would fire only after, say 5 milliseconds, the time it takes for signal to travel, after the first neuron has fired. The first neuron meanwhile will respond to the AP generated by the second neuron -which would reach it after (5+5= 10 ms) the round trip delay- and generate an AP after 10 ms from its initial firing.
We of course have to assume that somehow, the system was first put in motion: someone caused the first neuron to fire initially (this could not be other neurons, as we have assumed that this oscillator pair has no external input signals) and after that it is a self-sustaining clock with neuron 1 and neuron 2 both firing regularly at 10 ms intervals but in opposite phases. We just need GOD to initally fire the first neuron (the park of life) and thereafter we do have a periodic spontaneous activity in the system.
Thirdly, I propose that this ‘clock’, along with the concept of temporal summations, is able to calculate and code any arbitrary time duration and any arbitrary time dependent behavior, but in particular any periodic or sate/ goal based behavior. I’ve already discussed some of this in my previous posts and elaborate more in subsequent posts.
For now, some elementary tantalizing facts.
1. Given a 10 ms clock and a neuron capable of temporal summation over 50 ms duration, we can have a 50 ms clock: The neuron has the sole input as the output of the 10ms clock. After every 50 ms, it would have accumulated 5 signals in its memory. If the threshold-for-firing of the neuron is set such that it only fires if it has received five time the signal strength that is outputted by the 10 ms clock , then this neuron will fire after very 50 ms. This neuron would generate a periodic output after every 50 ms and implements a 50 ms clock.2. Given a 10 ms clock and a neuron capable of temporal summation over 40 ms, (or lets have the original 50 ms time-threshold neuron, but set its threshold-for-firing to 4 times the output strength of the 10 ms clock neuron) , using the same mechanism as defined above, we can have a 40 ms clock.
3. Given a 40 ms clock, a 50 ms clock and a neuron that does not do temporal summation, we can have a 2000 ms clock. The sole inputs to the neuron implementing the 2000 ms clock are the outputs of the 50 ms and the 40 ms clock. This neuron does not do temporal summation. Its threshold for firing is purely spatial and it fires only if it simultaneously receives a signal strength that is equal to or greater than the combined output signal strength of 50ms and 40 ms neuron. It is easy to see, that if we assume that the 50 ms and 40 ms neurons are firing in phase, then only after every 2000 ms would the signals from the two neurons arrive at the same time for this 2000ms clock. Viola, we have 2000 ms clock. After this, I assume, its clear that the sky is the limit as to the arbitrariness of the duration that we can code for.
Lastly, learning consists of changing the temporal thresholds associated with a neuron, so that any arbitrary schedule can be associated with a behavior, based on the history of reinforcement. After the training phase, the organism would exhibit spontaneous behavior that follows a schedule and could learn novel schedules for novel behaviors (transfer of learning).
To me all this seems very groundbreaking theorizing and I am not aware of how and whether these suggestions/ concepts have been incorporated in existing Neural Networks. Some temporal discussions I could find here. If anyone is aware of such research , do let me know via comments or by dropping a mail. I would be very grateful. I am especially intrigued by this paper (I have access to abstract only) and the application of temporal summation concepts to hypothalamic reward functions.
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