How To Record a Million Synaptic Weights in a Hippocampal Slice

A key step toward understanding the function of a brain circuit is to find its wiring diagram. New methods for optical stimulation and optical recording of neurons make it possible to map circuit connectivity on a very large scale. However, single synapses produce small responses that are difficult to measure on a large scale. Here I analyze how single synaptic responses may be detectable using relatively coarse readouts such as optical recording of somatic calcium. I model a network consisting of 10,000 input axons and 100 CA1 pyramidal neurons, each represented using 19 compartments with voltage-gated channels and calcium dynamics. As single synaptic inputs cannot produce a measurable somatic calcium response, I stimulate many inputs as a baseline to elicit somatic action potentials leading to a strong calcium signal. I compare statistics of responses with or without a single axonal input riding on this baseline. Through simulations I show that a single additional input shifts the distribution of the number of output action potentials. Stochastic resonance due to probabilistic synaptic release makes this shift easier to detect. With ∼80 stimulus repetitions this approach can resolve up to 35% of individual activated synapses even in the presence of 20% recording noise. While the technique is applicable using conventional electrical stimulation and extracellular recording, optical methods promise much greater scaling, since the number of synapses scales as the product of the number of inputs and outputs. I extrapolate from current high-speed optical stimulation and recording methods, and show that this approach may scale up to the order of a million synapses in a single two-hour slice-recording experiment

What Is Stochastic Resonance? Definitions, Misconceptions, Debates, and Its Relevance to Biology

Stochastic resonance is said to be observed when increases in levels of unpredictable fluctuations—e.g., random noise—cause an increase in a metric of the quality of signal transmission or detection performance, rather than a decrease. This counterintuitive effect relies on system nonlinearities and on some parameter ranges being “suboptimal”. Stochastic resonance has been observed, quantified, and described in a plethora of physical and biological systems, including neurons. Being a topic of widespread multidisciplinary interest, the definition of stochastic resonance has evolved significantly over the last decade or so, leading to a number of debates, misunderstandings, and controversies. Perhaps the most important debate is whether the brain has evolved to utilize random noise in vivo, as part of the “neural code”. Surprisingly, this debate has been for the most part ignored by neuroscientists, despite much indirect evidence of a positive role for noise in the brain. We explore some of the reasons for this and argue why it would be more surprising if the brain did not exploit randomness provided by noise—via stochastic resonance or otherwise—than if it did. We also challenge neuroscientists and biologists, both computational and experimental, to embrace a very broad definition of stochastic resonance in terms of signal-processing “noise benefits”, and to devise experiments aimed at verifying that random variability can play a functional role in the brain, nervous system, or other areas of biology

Tiedonkäsittelyn tehokkuus aivoissa

Brains are capable of processing information with remarkable efficiency under constraints set by the limited supply of physical resources such as the amount of space and the availability of metabolic energy. Natural selection has optimised the structure and function of brain networks using simple design rules similar to those found in man-made electronic and information systems. This study presents findings concerning a number of general principles of brain design governing the evolution and organisation of neural information processing. The rule of minimising wiring in neuronal networks is one such principle operating on multiple levels of brain organisation. Both individual components and larger brain architectural units are seen to feature characteristics of near-optimal wiring. Miniaturisation of neuronal components conserves space but raises problems about noise in signalling. Small-world organisation of anatomical and functional networks is widely employed in the brain, contributing to high global efficiency at low cost. Metabolic costs severely constrain signal traffic in the human brain, necessitating the use of energy-efficient sparse neural representations. Extensive evidence is presented of anatomical and physiological optimisations facilitating efficient information processing in brain networks. Limitations of current experimental techniques are discussed, with a view on possible future avenues of research.Aivojen tiedonkäsittely on huomattavan tehokasta vallitsevien fysikaalisten rajoitteiden puitteissa, jotka liittyvät muun muassa tilan ja metabolisen energian käyttöön keskushermostossa. Luonnonvalinta pyrkii optimoimaan aivoissa toimivien rakenteellisten ja toiminnallisten verkostojen toiminnan yksinkertaisten sääntöjen pohjalta. Nämä säännöt ovat monesti huomattavan samankaltaisia kuin ihmisen suunnittelemissa elektronisissa laitteissa ja tietoverkoissa. Tämä tutkielma esittelee joukon aivojen tiedonkäsittelyn evolutiivista historiaa, kehitystä ja toimintaa ohjaavia yleisiä suunnittelun periaatteita sekä niihin liittyviä tutkimustuloksia. Keskushermoston rakenteeseen laajalti vaikuttava kaapeloinnin minimoinnin periaate on eräs tällainen sääntö, joka vaikuttaa sekä yksittäisten hermosolujen että kokonaisten aivoalueiden rakenteeseen ja sijoitteluun. Yksittäisten hermosolujen pienentäminen säästää tilaa, mutta vaikeuttaa viestintää kasvattamalla satunnaisen aktiviteetin eli kohinan määrää hermosoluissa. Sekä aivojen rakenteellisissa että toiminnallisissa verkostoissa havaitaan monin niin kutsuttu pieni maailma tyyppinen rakenne, joka tuottaa tehokkaan verkostorakenteen verrattain pienillä biologisilla kustannuksilla. Hermoviestinnän metaboliset kustannukset puolestaan rajoittavat hermoimpulssien määrää ja luovat evolutiivisen paineen muodostaa tehokkaita neuraalisia representaatioita. Esitelty kirjallisuus tarjoaa runsaasti todisteita aivojen tiedonkäsittelyn rakenteellisesta ja toiminnallisesta tehokkuudesta. Lopuksi käsitellään nykyisten tutkimusmetodien rajoituksia ja avoimeksi jääviä kysymyksiä