Brembs (2006) Brains as output/input devices

I just finished reading an excellent blog post (paper?) by Björn Brembs entitled Brains as output/input devices. I admit that I too have tended to think of brains as stimulus-response machines, paying little attention to spontaneous behaviour and operant learning. Indeed, the old iPlant programming section on my website used to begin "Like a digital computer, the brain generates output from input". On the contrary, Brembs argues, the brain generates input from output. That is, the main function of brains is to control the environment they're in - and thus the sensory input and rewards/punishments they receive - by figuring out the right motor output for a given situation.

"input/output transformations may only account for a small fraction of what brains are doing. Maybe a much more significant portion of the brain is occupied with the ongoing modelling of the world and how it might react to our actions?"

Furthermore, Brembs argues, the variability we observe in spontaneous behaviour is a feature of operant learning: it is a way for the brain to find and develop patterns of behaviour that give it optimal control over its environment. "Faced with novel situations, humans and most animals spontaneously increase their behavioural variability", presumably in order to figure out how this particular environment responds to behaviour. It's the environment that responds, not the animal. Perhaps even the subtle variability we see in the invertebrate feeding system is an expression of the molluscan brain trying to figure out the best way to eat this particular sea-weed. If so, such variability should be selectively enhanced by reward learning. Is it? Other questions:

• How is behavioural/neuronal variability generated in small and large brains?
• What % of behavioural/neuronal variability is really subject to learning/operant control in different networks?
• What features of neuronal activity are most likely to be subject to learning/operant control? In other words, where do we look? Spike rate of individual neurons? Network patterns? Duration of the different phases of motor programs?
• How is reward conditioning/operant control of spontaneous variability instantiated in small and large brains?
• How can we incorporate output-input functions in artificial neural networks and robotics? That is, what kind of tasks could such networks realistically perform today or in the future?
• What is the cultural effect (within in the neuroscience community and generally) of treating brains as output/input devices rather than input/output devices?

P.S. It was particularly stupid of me to emphasise the input-output side of things on the iPlant website, as the whole point of conditional rewarding brain stimulation is to modify output-input learning by rewarding beneficial but endogenously under-rewarded behavioural variations (rigorous exercise in morbidly obese patients etc.) with electrical pulses to the reward system.

Notes on electrical rhythms in the brain

I'm reading George Buszáki's Rhythms of the Brain at the moment. Here are some notes on what I've read so far. Please correct me if I've got something wrong.

Electrical fluctuations in the cortex are organized into rhytmic oscillations at different spatial and temporal scales. The resting cortex is characterized by oscillations primarily in the alpha band (8-12 Hz, the brain's 'default network'). The active (i.e. behaving, percieving) cortex is characterized by oscillations primarily in the gamma band (25-100 Hz). Buszáki and others argue that cortical neurons that synchronize their membrane oscillations in the gamma band 'bind' their respective functions (e.g. visual feature detection) together into cognitive processes (e.g. object perception). Such formations of neurons are called neuronal groups or assemblies. Particularly striking are neuronal groups in the gamma range emerging in the prefrontal cortex for the duration of time that human patients are asked to hold items in working memory.

(alpha)

(gamma)

Cortical neurons sponaneously synchronize their membrane oscillations in the gamma range and form transient neuronal groups even in the absence of stimuli. This is, at least in part, due to the time constants of GABA curents, synaptic delays and synaptic potentiation. Buszáki writes:

"If neurons are already engaged in internal synchronization, the external stimulus will compete with the central oscillator, and the coutcome depends on the relative timing and strenght of the external input and the propensity of the internal oscillator. The stimulus may be ignored, or it may enhance or quench the internal oscillation." p.255

In other words, the effect of a stimulus on cortical activity depends strongly on the prior state of the brain. This explains the significant variability in brain activity (e.g. on EEG/MEG/fMRI) seen within and between subjects in response to invariant stimuli. Buszáki laments the fact that this variability is usually averaged out and treated as 'noise'. Björn Brembs often makes a similar argument.

Stimuli interact with ongoing cortical activity in various ways. Whereas a weak stimulus may reset the phase of ongoing oscillations, a strong or salient stimulus may completely change the type and distribution of oscillations in the cortex. Several studies have found that strong ongoing oscillations in the gamma, theta or alpha ranges prior to stimulus presentation promote efficient memory encoding. A stimulus that resets a strong rhythm presumably has a larger impact on brain activity than one that resets a weak rhythm. The presence or absence of strong rhythms in the brain is directly related to states of attention and catecholamine concentrations.